Proceedings Track Presentations
As of May 1, 2012 (schedule subject to change)
All Highlights and Proceedings Track presentations are presented by scientific area part of the combined Paper Presentation schedule.
A full schedule of Paper Presentations can be found here.
AA01 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA02 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA03 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA04 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA05 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA06 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA07 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA08 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA09 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA10 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA11 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA12 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA13 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA14 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA15 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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AA16 -
Room: Exhibit Hall BDate: Tuesday, July 17, 12:40 p.m. - 2:30 p.m.Author(s):
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AA17 -
Room: Exhibit Hall BDate: Monday, July 16, 12:40 p.m. - 2:30 p.m.Author(s):
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KN1 - Seeing forward by looking back
Room: BallroomDate: Sunday, July 15Author(s):
Richard H. Lathrop, Lawrence Hunter,
TOP
KN2 - Data integration for understanding dynamic biological systems
Room: BallroomDate: Sunday, July 15Author(s):
Ziv Bar-Joseph, Carnegie Mellon University, United States
TOP
KN3 - Analysis of transcriptome structure and chromatin landscapes
Room: BallroomDate: Monday, July 16Author(s):
Barbara Wold, California Institute of Technology, Pasadena, United States
TOP
KN4 - Progress, challenges and opportunities in population genome sequencing
Room: BallroomDate: Monday, July 16Author(s):
Richard Durbin, Wellcome Trust Sanger Institute, United Kingdom
TOP
KN5 - Integrative Structural Biology
Room: BallroomDate: Tuesday, July 17Author(s):
Andrej Sali, University of California, San Francisco, United States
TOP
KN6 - The other Third: Coming to grips with membrane proteins
Room: BallroomDate: Tuesday, July 17Author(s):
Gunnar von Heijne, Stockholm University, Sweden
TOP
LBR01 - Identifying tissue specificity of protein complexes based on a global map of human expression data
Room: 202B/CDate: Sunday, July 15Author(s):
Daniela Börnigen, Harvard University, Harvard School of Public Health, us
Daniela Börnigen, Harvard School of Public Health, Harvard University, United States
Tune Pers, Technical University of Denmark, Denmark
Lieven Thorrez, KU Leuven, Belgium
Curtis Huttenhower, Harvard School of Public Health, Harvard University, United States
Yves Moreau, KU Leuven, Belgium
Søren Brunak, Technical University of Denmark, Denmark
Session Chair: Carl Kingsford
Abstract Show
Disease-causing human genetic variants are often highly tissue specific, but for most disease genes the primarily affected tissue is unknown. We hypothesized that the degree of coordinated expression between genes coding for distinct protein complex subunits might pinpoint the tissues in which linked diseases are manifested.
We thus developed a method to predict the tissue involvement of disease-linked protein complexes. For each susceptibility gene, we ranked tissues according the gene’s concordant expression with its protein interaction partners under normal conditions. The analysis thus combined a high-quality human interactome, its constituent set of protein complexes, a global map of human gene expression data in healthy tissues, and a predefined set of disease-linked genes.
We validated our hypothesis using this method by comparing our predictive tissue ranking with a literature-based gold standard ranking of 260 unique protein disease associations across 35 tissues. Our predictions achieved an average AUC of 0.78 over all tissues, with some (such as adipose or placental) tissues obtaining AUCs over 0.9. These were due to less heterogeneous cell types within the tissues, in contrast to tissues such as the blood or lymphatic system in which tissue specific disease involvement proved more difficult to predict. Our overall accuracy, however, suggests that the degree of coordinated expression of a disease gene and its protein interaction partners indeed provides insight into as to which tissue is most likely to be affected or causal in human disease.
TOPWe thus developed a method to predict the tissue involvement of disease-linked protein complexes. For each susceptibility gene, we ranked tissues according the gene’s concordant expression with its protein interaction partners under normal conditions. The analysis thus combined a high-quality human interactome, its constituent set of protein complexes, a global map of human gene expression data in healthy tissues, and a predefined set of disease-linked genes.
We validated our hypothesis using this method by comparing our predictive tissue ranking with a literature-based gold standard ranking of 260 unique protein disease associations across 35 tissues. Our predictions achieved an average AUC of 0.78 over all tissues, with some (such as adipose or placental) tissues obtaining AUCs over 0.9. These were due to less heterogeneous cell types within the tissues, in contrast to tissues such as the blood or lymphatic system in which tissue specific disease involvement proved more difficult to predict. Our overall accuracy, however, suggests that the degree of coordinated expression of a disease gene and its protein interaction partners indeed provides insight into as to which tissue is most likely to be affected or causal in human disease.
LBR02 - Quantifying the Systemic Consequences of Point Mutations in Proteins through Pathway Dynamics and Protein Structures
Room: 202B/CDate: Sunday, July 15Author(s):
Tammy Cheng, Cancer Research UK London Research Institute, uk
Tammy Cheng, Cancer Research UK London Research Institute, United Kingdom
Lucas Goehring, Max Planck Institute, Germany
Linda Jeffery, Cancer Research UK London Research Institute, United Kingdom
Yu-En Lu, University of Cambridge, United Kingdom
Jacqueline Hayles, Cancer Research UK London Research Institute, United Kingdom
Béla Novák , University of Oxford, United Kingdom
Paul Bates, Cancer Research UK London Research Institute, United Kingdom
Session Chair: Carl Kingsford
Abstract Show
Gauging the systemic effect of point mutations in proteins is an important topic in the current post GWAS era. However, it is not a trivial task to understand how a change at the protein structure level eventually affects a cell's phenotypic outcome. This is because complex, multi-scale information, ranging from proteins to pathways, is usually required for obtaining analytical results with physiological meaning. With respect to the fact that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of point mutations in proteins remain predominantly unexplored, we investigate the practicality of this approach by formulating mathematical models to study point mutations that involve the cell cycle control mechanism (G2 to Mitosis transition) in yeast and the neuro-cardio-facial-cutaneous syndrome associated with the human MAPK signalling pathway.
TOPLBR03 - Regulatory Network Structure as the Dominant Determinant of Transcription Factor Evolutionary Rate in Yeast
Room: 202B/CDate: Sunday, July 15
Author(s):
Jasmin Coulombe-Huntington, Boston University, us
Session Chair: Carl Kingsford
Abstract Show
The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. However, since trans-level variation is known to account for much of the gene expression variation between strains, studying the evolution of trans-factors is crucial to understanding regulatory network evolution. Here, we systematically asses the different genomic and network-level determinants of transcription factor (TF) evolutionary rate in yeast and how they compare to those of generic proteins. We develop a novel method to demonstrate that transcription factors possess significantly distinct trends relating evolutionary rate to various genomic features, such as mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We then go on to show that the strongest predictor of transcription factor evolutionary rate is the median evolutionary rate of its target genes, followed by the fraction of target genes which are species-specific. After decomposing the regulatory network into positive and negative edges, we found that this effect is limited to activating regulatory relationships. This work is the first to establish the modularity of TF-target protein evolution and highlights key evolutionary differences between positive and negative regulation systems. We have also demonstrated that systems-level properties can leave evolutionary traces of comparable effect size to physical features such as interaction degree and expression level and that TF evolution in particular is best understood through a regulatory network-level perspective.
TOPLBR04 - Global and specific Regulation of mRNA Decay analyzed by Dynamic Transcriptome Analysis
Room: 202B/CDate: Sunday, July 15Author(s):
Achim Tresch, Ludwig-Maximilians-University Munich, de
Session Chair: Carl Kingsford
Abstract Show
To measure eukaryotic mRNA turnover, we developed comparative Dynamic Transcriptome Analysis (cDTA). cDTA provides absolute rates of mRNA synthesis and decay in Saccharomyces cerevisiae (Sc) cells with the use of Schizosaccharomyces pombe (Sp) as internal standard. We apply cDTA to Sc mutants of its transcription- and degradation machinery. We find that mutants with a decreased degradation show also a decreased transcriptional activity. Surprisingly, this negative feedback is mutual, i.e., mutants that are globally impaired in their RNA synthesis have a globally decreased decay. Extended kinetic modeling reveals that this mutual feedback is achieved by a factor that inhibits synthesis and a factor that enhances degradation.
TOPLBR05
- Fractionation, rearrangement and subgenome dominance
Room: 202ADate: Sunday, July 15Author(s):
David Sankoff, University of Ottawa, ca
Session Chair: Olga Vitek
Abstract Show
Fractionation, the loss of duplicate genes after whole genome duplication (WGD), causes more gene order disruption than classical chromosomal rearrangements such as inversion or reciprocal translocation. WGD and fractionation are particularly prevalent in flowering plants. Gene order disruption follows from the partly random choice of which of the two copies is deleted, This artificially inflates the inferred amount of chromosomal rearrangement observed between the WGD descendant and an unduplicated sister genome. Our work is designed to computationally detect, characterize and correct for this impediment to the study of evolution.
We developed the "consolidation algorithm" to assess and correct for the gross errors in rearrangement inference caused by fractionation. In simulations our procedure almost completely wipes out this distortion.
In applying our method to the poplar genome, an ancient tetraploid, compared to a diploid sister genome, grapevine, we discovered that the majority of the apparent rearrangement is actually attributable to fractionation. Examining the consolidated regions detected by our algorithm, there are a number of regions much longer than those in the simulations, suggesting a non-independence of deletion events affecting neighboring genes, and clear tendency for genes to be deleted in one of the two homeologs, as would be predicted by the recent theory of subgenome dominance
TOPWe developed the "consolidation algorithm" to assess and correct for the gross errors in rearrangement inference caused by fractionation. In simulations our procedure almost completely wipes out this distortion.
In applying our method to the poplar genome, an ancient tetraploid, compared to a diploid sister genome, grapevine, we discovered that the majority of the apparent rearrangement is actually attributable to fractionation. Examining the consolidated regions detected by our algorithm, there are a number of regions much longer than those in the simulations, suggesting a non-independence of deletion events affecting neighboring genes, and clear tendency for genes to be deleted in one of the two homeologs, as would be predicted by the recent theory of subgenome dominance
LBR06 - Internal pseudo-symmetry in proteins
Room: 202ADate: Sunday, July 15Author(s):
Andreas Prlic, University of California San Diego, us
Andreas Prlic, UCSD, United States
Spencer Bliven, UCSD, United States
Philippe Youkharibache, InPharmatics Corporation, United States
Peter Rose, UCSD, United States
Phil Bourne, UCSD, United States
Session Chair: Olga Vitek
Abstract Show
Symmetry in the quaternary structure of proteins is frequently associated with function. For example, symmetry plays a prominent role in models of enzyme activity. While the observation of symmetry in quaternary structure goes back to the very first protein structures, more and more cases of pseudo-symmetry within protein domains have been described. It is hypothesized that such symmetries can be linked to function and folding of proteins. Here, we attempt to verify this hypothesis by both systematically detecting pseudo-symmetry via a new algorithm and by manually investigating crafted alignments of symmetric proteins. The new algorithm detects internal pseudo-symmetry and repeats in protein chains and is available in the software CE-Symm. By applying it systematically we can detect such structural features in many examples that have previously not been described. We investigate the hypothesis that symmetry is related to function by manually analyzing many of the detected cases. Our results show that symmetry plays an important functional role not only in quaternary structure, but also within protein chains. We can identify local alignments between distant folds, in which symmetric subunits, here called “protodomains” are conserved. This allows us to gain novel insights into distant evolutionary relationships. Knowledge of internal symmetry is important for a better understanding of evolution, function and folding and newly resolved protein structures should be investigated for hidden internal pseudo-symmetries.
TOPLBR07 - Technology to identify global dynamics of protein interaction networks
Room: 202ADate: Sunday, July 15Author(s):
Nozomu Yachie, University of Toronto, ca
Nozomu Yachie, University of Toronto, Canada
Sedide Ozturk, University of Toronto, Canada
Joseph Mellor, University of Toronto, Canada
Atina Cote, University of Toronto, Canada
Anna Karkhanina, University of Toronto, Canada
Haiyuan Yu, Cornell University, United States
Pascal Braun, Dana Farber Cancer Institute, United States
David Hill, Dana Farber Cancer Institute, United States
Marc Vidal, Dana Farber Cancer Institute, United States
Frederick Roth, University of Toronto, Canada
Session Chair: Olga Vitek
Abstract Show
Cancer and other genetic diseases are mediated by a web of macromolecular interactions that are regulated dynamically (for example, through post-transcriptional modification). Thus, a technology that captures the regulated dynamics of a global-scale protein interaction network would be important to accelerate our understanding of complex diseases. In vivo assays such as affinity purification followed by mass spectrometry (AP-MS) capture interactions under one condition, while in vitro assays such as Y2H capture interactions that could occur under different conditions, so long as these interactions do not require a third co-factor or post-translational modifier. No current method has the ability to economically produce many “conditional interactome” maps, each in the presence of different co-factors or modifiers. Here we describe a new technology BFG-Y2H (Barcode Fusion Genetics-Y2H) which exploits the efficiencies of deep short-read sequencing and offers the potential to map dozens of genome-scale conditional interactomes for a given species by one researcher within one year with the cost of less than $1,000 per interactome.
TOPLBR08 - Assembling Acute Myeloid Leukemia RNA-seq Data to Infer Alternative Polyadenylation Site Usage
Room: 202ADate: Sunday, July 15Author(s):
Inanc Birol, Genome Sciences Centre BC Cancer Agency, ca
Inanc Birol, BC Cancer Agency, Canada
Session Chair: Olga Vitek
Abstract Show
Alternative polyadenylation in 3’ UTRs is known to affect post-transcriptional gene regulation, and can be dysregulated in tumour cells. Thus identification of alternative polyadenylation site usage and measurement of expression levels of the resulting 3’ UTRs will be valuable for understanding tumor biology. In this study, we use RNA-seq data from the Illumina HiSeq 2000 platform to characterize the transcriptome repertoires of several Acute Myeloid Leukemia (AML) samples with and without NPM1 insertions, and investigate the association of this biomarker with 3’ UTR usage and expression.
To interrogate RNA-seq data for unbiased 3’ UTR reconstruction, we expanded the functionality of Trans-ABySS, our de novo transcriptome assembly tool. Trans-ABySS assembles RNA-seq data using a range of read-to-read overlap stringency levels to account for the sensitivity-specificity balance while reconstructing transcripts with a range of expression levels. Our preliminary analysis of AML transcriptomes indicate that our approach can assemble one or more 3’ UTRs for about 80% of genes that are expressed at 10-fold or more coverage, and offer a number of novel 3’ UTR predictions, which we will study further to assess their relationships to disease biology.
TOPTo interrogate RNA-seq data for unbiased 3’ UTR reconstruction, we expanded the functionality of Trans-ABySS, our de novo transcriptome assembly tool. Trans-ABySS assembles RNA-seq data using a range of read-to-read overlap stringency levels to account for the sensitivity-specificity balance while reconstructing transcripts with a range of expression levels. Our preliminary analysis of AML transcriptomes indicate that our approach can assemble one or more 3’ UTRs for about 80% of genes that are expressed at 10-fold or more coverage, and offer a number of novel 3’ UTR predictions, which we will study further to assess their relationships to disease biology.
LBR09 - CAGI: The Critical Assessment of Genome Interpretation, a community experiment to evaluate phenotype prediction
Room: 202ADate: Monday, July 16
Author(s):
Steven Brenner, University of California, Berkeley, us
Steven Brenner, University of California, Berkeley, United States
Session Chair: Chad Myers
Abstract Show
The Critical Assessment of Genome Interpretation (CAGI, 'kā-jē) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. In this assessment, participants are provided genetic variants and make predictions of resulting phenotype. These predictions are evaluated against experimental characterizations by independent assessors. The CAGI experiment culminates with a community workshop and publications to disseminate results, assess our collective ability to make accurate and meaningful phenotypic predictions, and better understand progress in the field. A long-term goal for CAGI is to improve the accuracy of phenotype and disease predictions in clinical settings.
This presentation will focus on the practical implications of CAGI 2011 results on a diversity of challenges. The presentation will summarize the state-of-the-art in identifying the impact of variants in a metabolic enzyme and in an oncogene, and thus the appropriate use of such methods in basic and clinical research. CAGI has revealed the relative strengths of different prediction approaches, and the best will be described.
CAGI also explored genome-scale data, showing unexpected successes in predicting Crohn’s disease from exomes, as well as disappointing failures in using genome and transcriptome data to distinguish discordant monozygotic twins with asthma. Predictors had promising complementary approaches in predicting distinct response of breast cancer cell lines to a panel of drugs. Predictors also made measurable progress in predicting a diversity of phenotypes present in the personal genome project participants.
Current information including additional challenges is available at the CAGI website at http://genomeinterpretation.org.
TOPThis presentation will focus on the practical implications of CAGI 2011 results on a diversity of challenges. The presentation will summarize the state-of-the-art in identifying the impact of variants in a metabolic enzyme and in an oncogene, and thus the appropriate use of such methods in basic and clinical research. CAGI has revealed the relative strengths of different prediction approaches, and the best will be described.
CAGI also explored genome-scale data, showing unexpected successes in predicting Crohn’s disease from exomes, as well as disappointing failures in using genome and transcriptome data to distinguish discordant monozygotic twins with asthma. Predictors had promising complementary approaches in predicting distinct response of breast cancer cell lines to a panel of drugs. Predictors also made measurable progress in predicting a diversity of phenotypes present in the personal genome project participants.
Current information including additional challenges is available at the CAGI website at http://genomeinterpretation.org.
LBR10 - Chromatin Structure and Genomic Context Influence Mitochondrial DNA Insertion in Mammalian Nuclear Genomes
Room: 202ADate: Monday, July 16Author(s):
Junko Tsuji, University of Tokyo, jp
Martin Frith, National Institute of Advanced Industrial Science and Technology, Japan
Kentaro Tomii, National Institute of Advanced Industrial Science and Technology, Japan
Paul Horton, National Institute of Advanced Industrial Science and Technology, Japan
Session Chair: Chad Myers
Abstract Show
It is known that remnants of partial or whole copies of mitochondrial DNAs are found in nuclear genomes. Such mtDNA-like sequences are called‚ NUMTs (Nuclear MiTochondrial sequences), and are integrated in the double-strand break sites of the nuclear genomes via non-homologous end joining repair. Several computational studies have investigated NUMTs, however those studies have not used appropriate methodology for sensitive detection of NUMTs and precise delineation of their boundaries. We developed a carefully considered protocol to redefine NUMT datasets of four mammalian species (human, rhesus, mouse, and rat). The issues we considered include appropriate alignment parameters, correct handling of circular mtDNA, masking of low complexity sequences, post-insertion duplication of NUMTs, long indels and validation of E-value thresholds. By analyzing the redefined datasets, we found new characteristics of NUMT integration sites. Most of the inferred insertion points of NUMTs in all organisms tested occur in the vicinity of retrotransposons (82.9-90.4%), and the insertion sites show the significant level of over-representation of A+T oligomers (p<0.0001). As well as such genomic contexts, chromatin structures also influenced the NUMT insertion. We found that NUMT insertion sites show a strong tendency to have high predicted DNA curvature, and often occur in experimentally defined nucleosome depleted regions. In light of the above results, the mtDNA insertion events are surely influenced by observed specific chromatin structures and genomic contexts.
TOPLBR11 - Computing with Chromatin Modification
Room: 202ADate: Monday, July 16Author(s):
Barbara Bryant, Constellation Pharmaceuticals, us
Barbara Bryant, Constellation Pharmaceuticals, United States
Greg Tucker-Tellogg, National University of Singapore, Singapore
Session Chair: Chad Myers
Abstract Show
In living cells, DNA is wrapped around histone octamers to make the nucleosomes that comprise chromatin. The histones and DNA can be modified with chemical groups that are added, removed and recognized by multi-functional molecular complexes. Here we present a computational model, in which chromatin modifications are information units that can be written onto a one-dimensional chromatin memory. Chromatin-modifying complexes are modeled as read-write rules that operate on several adjacent nucleosomes. We illustrate the use of this “chromatin computer” by writing programs to solve problems that cannot be solved with finite state automata or logic circuits. We show the execution of these programs on a chromatin computer simulator, and provide animated snapshots of the intermediate states of the nucleosome memory. We model additional features of biological chromatin, resulting in more efficient computation. This formalism is useful both analytically, to model chromatin biology, and theoretically, as a programming paradigm.
TOPLBR12 - Transcription factor target gene identification based on ChIP-seq data
Room: 202ADate: Monday, July 16Author(s):
Andreas Beyer, TU Dresden, de
Andreas Beyer, TU Dresden, Germany
Weronika Sikora-Wohlfeld, TU Dresden, Germany
Marit Ackermann, TU Dresden, Germany
Eleni Christodoulou, TU Dresden, Germany
Session Chair: Chad Myers
Abstract Show
Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has been instrumental for elucidating transcriptional networks by measuring the genome-wide binding of proteins at high resolution. Despite the precision of these experiments it is not trivial to identify the genes that are regulated through the observed bindings. A lot of recent research has been devoted to the correct identification of binding sites, but very little to predicting target genes. Here we present a comprehensive evaluation of computational methods used to define target genes of transcription factors (TFs) based on ChIP-seq data. In order to systematically analyze target gene prediction we structured the process into three steps and we evaluated alternatives for each of these steps. Using 66 ChIP-seq and 23 expression datasets we could show that parameter-free methods (not requiring any tunable parameters) better adapt to the specificities of a particular ChIP-seq dataset. Our analysis revealed a potential bias when comparing ChIP-seq and perturbation expression data sets due to unregulated genes. We show that target genes with the highest TF association scores tend to respond later than medium scoring targets, which partly explains the poor overlap typically observed between ChIP-seq and expression data. Finally, we investigated the clustering of TF target genes in the genome, revealing 95 regions with highly significant enrichment of targets of 42 different factors.
TOPLBR13 - Fast and accurate metagenomic profiling of microbial community composition using unique clade-specific marker genes
Room: 202ADate: Monday, July 16Author(s):
Nicola Segata, Harvard School of Public Health, us
Nicola Segata, Harvard School of Public Health, United States
Levi Waldron, Harvard School of Public Health, United States
Annalisa Ballarini, University of Trento, It
Vagheesh Narasimhan, Harvard School of Public Health, United States
Olivier Jousson, University of Trento, It
Curtis Huttenhower, Harvard School of Public Health, United States
Session Chair: Predrag Radivojac
Abstract Show
Identifying which organisms populate a microbial community and in what proportions is crucial for characterizing human-associated microbiomes. Shotgun sequencing allows biological function and phylogenetic composition to be assayed simultaneously, but existing taxonomic profiling methods are impractical for the scope of current datasets. We propose MetaPhlAn, a novel approach incorporating clade-specific marker genes identified computationally using 2,887 reference genomes. The resulting catalog of 400,000 genes permits unambiguous taxonomic assignments from metagenomic data more accurately and >50 times faster than current approaches. The method was evaluated on terabases of short reads in addition to ten synthetic metagenomes, achieving correlations with true organismal relative abundances over 0.99 for high-complexity and log-normally distributed communities. Applied to the 691 metagenomes of the Human Microbiome Project, MetaPhlAn profiled the microbial species populating all 15 assayed body sites together with their abundance pattern signatures. Specifically, on 51 vaginal microbiomes, MetaPhlAn agreed closely with 16S-based results and further identified the Lactobacillus species forming five distinct microbiome types. An analysis of marine ecosystems confirmed detection of archaeal organisms and MetaPhlAn's applicability and accuracy even in communities with limited numbers of sequenced reference genomes. Finally, MetaPhlAn allowed us to perform a meta-analysis integrating 263 samples from the HMP and MetaHIT projects, providing the largest metagenomic community profiling to date of the human gut microbiota. This dataset highlights a range of dominant Bacteroides species among these American and European cohorts, and it suggests complexity at the species level beyond that captured by the recently proposed gut enterotypes.
MetaPhlAn is available at http://huttenhower.sph.harvard.edu/metaphlan.
TOPMetaPhlAn is available at http://huttenhower.sph.harvard.edu/metaphlan.
LBR14 - Optimizing functional genomics screening strategies for drug target prediction
Room: 202ADate: Monday, July 16
Author(s):
Raamesh Deshpande, University of Minnesota, us
Session Chair: Predrag Radivojac
Abstract Show
Developing new drugs is a lengthy and expensive process. In comparison, many
compounds have been identified from natural sources but their activity on living cells has not been haracterized. Recent studies have proven the utility of chemical genomics based on yeast functional genomics tools for the discovery of compounds’ modes of action. Specifically, the chemical genetic interactions of a particular compound across a large nonessential deletion strain collection should mimic the genetic interactions of the corresponding target. One limitation of this approach, however, is that it requires a relatively high volume of compound given the size of the deletion collection to be queried. As a solution to this problem, we propose a method to identify a small subset of the deletion collection that is the most informative in discovering compounds’ modes of action. We have applied this method in the context of yeast and identified a diagnostic strain set comprising around 5% of the non-essential deletion mutant collection. We show that even with a small fraction of the genome, this diagnostic set performs comparably to complete chemicalgenetic profiles. We also demonstrate that our method provides substantial improvement over baseline strategies based on selection of either random genes or hubs. Large-scale chemical genomic screens of natural compound libraries based on this diagnostic set of genes are currently in progress.
TOPcompounds have been identified from natural sources but their activity on living cells has not been haracterized. Recent studies have proven the utility of chemical genomics based on yeast functional genomics tools for the discovery of compounds’ modes of action. Specifically, the chemical genetic interactions of a particular compound across a large nonessential deletion strain collection should mimic the genetic interactions of the corresponding target. One limitation of this approach, however, is that it requires a relatively high volume of compound given the size of the deletion collection to be queried. As a solution to this problem, we propose a method to identify a small subset of the deletion collection that is the most informative in discovering compounds’ modes of action. We have applied this method in the context of yeast and identified a diagnostic strain set comprising around 5% of the non-essential deletion mutant collection. We show that even with a small fraction of the genome, this diagnostic set performs comparably to complete chemicalgenetic profiles. We also demonstrate that our method provides substantial improvement over baseline strategies based on selection of either random genes or hubs. Large-scale chemical genomic screens of natural compound libraries based on this diagnostic set of genes are currently in progress.
LBR15 - Structure-Based Ligand Discovery for Solute Carrier Transporters
Room: 202ADate: Monday, July 16Author(s):
Avner Schlessinger, University of California, San Francisco, us
Session Chair: Predrag Radivojac
Abstract Show
Polypharmacology is a phenomenon in which a drug binds multiple rather than a single target with significant affinity. The effect of polypharmacology on therapy can be positive (effective therapy) and/or negative (side effects). Solute Carrier (SLC) Transporters are membrane proteins that control the uptake and efflux of various solutes such as amino acids, sugars, and drugs. SLCs can be drug targets themselves or be responsible for absorption, targeting, and disposition of drugs. We describe an integrated structure-based approach for identifying protein-small molecule interactions. Particularly, we use comparative modeling, virtual screening, and experimental validation (with kinetic measurements of uptake), to identify interactions between SLC transporters and small molecules ligands, including prescription drugs and metabolites. For example, we discovered that several existing prescription drugs interact with the norepinephrine transporter, NET, which may explain some of the pharmacological effects (i.e., efficacy and/or side effects) of these drugs. We also apply our approach to related transporters, to identify rules for substrate specificity in a key membrane transporter family of the nervous system. Our systems pharmacology approach is generally applicable to structural characterization of protein families other than SLCs, including receptors, ion-channels, and enzymes, as well as their interactions with small molecule ligands.
TOPLBR16 - Data-driven Prediction of Drug Effects and Interactions
Room: 202ADate: Monday, July 16Author(s):
Nicholas Tatonetti, Stanford University, us
Session Chair: Predrag Radivojac
Abstract Show
Adverse drug events remain a leading cause of morbidity and mortality around the world and many are not detected during clinical trials. Fortunately, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems (for example, patient medical histories), and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (OFFSIDES) and a database of drug-drug interaction side effects (TWOSIDES). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Patients taking combined treatment of selective serotonin reuptake inhibitors and thiazides had a significantly increased incidence of prolonged QT. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.
TOPLBR17 - MalaCards – the integrated Human Malady Compendium
Room: 202B/CDate: Tuesday, July 17Author(s):
Marilyn Safran, Weizmann Institute of Science, il
Marilyn Safran, Weizmann Institute of Science , Israel
Session Chair: Yinyin Yuan
Abstract Show
We introduce MalaCards, an integrated database of human maladies and their annotations (malacards.weizmann.ac.il), modeled on the architecture and richness of the popular GeneCards human genes database, (www.genecards.org). MalaCards mines varied sources to generate a ‘card’ for each disease via: 1. Identifying sources of nomenclature/annotation, targets for disease data mining; 2. Developing algorithms for merging heterogeneous disease names, and defining unique identifiers. For example, alzheimer’s disease, ad, dementia alzheimer’s type, are merged under Alzheimer Disease, acronym AD, ID=ALZ001, with others listed as aliases (see malacards.weizmann.ac.il/card/index/ALZ001); 3. Engineering scripts to mine annotations; 4. Building MalaCards V1.01(alpha), with thousands of user-friendly ‘cards’ for all incorporated maladies, containing a variety of sections; 5. Implementing a strategy whereby detailed gene-disease relationships within GeneCards are used to create disease-specific content, leveraging the GeneCards relational database and search engine; 6. Constructing a second-tier annotator, based on GeneDecks Set Distiller, a GeneCards suite member. For example, diseases related to the key disease are computed to be those maximally associated with the set of found genes. Similarly, we obtain drugs/compounds, publications and mouse phenotypes contextually related to the disease; 7. Formulating scores for prioritizing derived annotations; 8. Initiating QA based on extensive knowledge within the Crown Human Genome Center. As our R&D continues, we plan to expand the list of annotation sources and sections, and include genetic variation details. This will be enhanced by collaborations with researchers outside of our group, and expanded by the initiation of systems biology tools, towards the goal of enabling novel biomedical discoveries.
TOPLBR18 - Simultaneous host-parasite transcriptomes provide insight into malarial host-parasite interactomes
Room: 202B/CDate: Tuesday, July 17Author(s):
Adam Reid, Wellcome Trust Sanger Institute, uk
Adam Reid, Wellcome Trust Sanger Institute, United Kingdom
Matthew Berriman, Wellcome Trust Sanger Institute, United Kingdom
Session Chair: Yinyin Yuan
Abstract Show
Molecular interactions are key to the ability of a parasite to enter and persist in its host. However our understanding of the genes and proteins involved in these interactions is no more than partial in even the most well understood systems. We have applied the popular concept of using correlated gene expression profiles to identify molecular interactions in one species to the interspecific (host-parasite) case. We show for the first time that genes in different species with correlated expression are more likely to encode proteins which interact or are otherwise involved in host-parasite interaction. We go on to examine predicted host-parasite interactions between the malaria parasite and both its mammalian host and insect vector.
TOPLBR19 - A Predictive Gene Expression Model for quantifying Plasmodium falciparum red blood cell stages
Room: 202B/CDate: Tuesday, July 17Author(s):
Vagheesh Narasimhan, Harvard University, us
Vagheesh Narasimhan, Harvard University, United States
Regina Joice, Harvard University, United States
Curtis Huttenhower, Harvard University, United States
Matthias Marti, Harvard University, United States
Jacqui Montgomery, Malawi-Liverpool-Wellcome Trust, United Kingdom
Karl Seydel, Michigan State University, United States
Daouda Ndiaye, University of Cheikh Anta Diop, Sn
Johanna Daily, Albert Einstein College of Medicine, United States
Kim Williamson, Loyola University, Chicago, United States
Terrie Taylor, Michigan State University, United States
Danny Milner, Harvard University, United States
Session Chair: Yinyin Yuan
Abstract Show
P. falciparum, the parasitic causative agent of malaria, undergoes a complex staged life cycle during its infection of human hosts. The transcriptional expression program of this cycle has been well-modeled, but not that of the small minority of these stages that are transmissible among hosts and thus offer a potential target for preventative interventions. We have thus developed a quantitative model for determining the proportions of transmissible morphological stages of P. falciparum in a mixed population based on transcript levels. Our model consists of a constrained linear regression, in which each transcript's total measured expression level is the sum of parasites' contributions from each life cycle stage. The model was trained and initially cross-validated using a set of five published in vitro microarray time courses in which stage distributions were determined by light and fluorescence microscopy. To apply this method in vivo, we selected the minimum number of markers needed to quantify the stage distribution using a combination of model selection, stage-specificity, and qRT-PCR primer design. We then assessed the model on microarray and qRT-PCR expression measurements from blood samples of 40 malaria patients from Valingara, Senegal, revealing that only a small subset of patients carry transmissible parasite stages. In addition to the model's ability to capture enriched biomolecular processes within transmissible malaria stages, we believe the field-applicable qRT-PCR assay may be a useful tool for future control of malaria transmission through stage-specific targeted interventions.
TOPLBR20 - Achieving better agreement among microarray disease studies through automatic correction for latent variables
Room: 202B/CDate: Tuesday, July 17Author(s):
Maria Chikina, Mount Sinai Medical School, us
Stuart Sealfon, Mount Sinai School of Medicine, United States
Session Chair: Yinyin Yuan
Abstract Show
Microarray studies with human subjects often have limited sample sizes, hampering the ability of differential expression analysis to make trustworthy predictions of biomarkers associated with disease. Existing techniques for meta-analysis address this problem by aggregating the results of multiple datasets to gain statistical power, but the performance of this kind approach is limited by the fact that human gene expression is influenced by many non-random factors such as genetics, sample preparations, tissue heterogeneity, etc. that may contribute to the lack of inter-study agreement.
We show that it is in fact possible to carry out an automatic correction of individual datasets to reduce the effect of such `latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition will show better agreement once each is corrected, and allowing for more trustworthy aggregated predictions. We demonstrate our approach, which involves a crucial modification of the method of "surrogate variable analysis", on studies of multiple sclerosis. We find improved agreement across varying study designs, platforms, and tissues, and are able to make a number of novel predictions. Our analysis implicates several metabolic pathways contributing to the emerging understanding of metabolic involvement in MS pathology.
TOPWe show that it is in fact possible to carry out an automatic correction of individual datasets to reduce the effect of such `latent variables' (without prior knowledge of the variables) in such a way that datasets addressing the same condition will show better agreement once each is corrected, and allowing for more trustworthy aggregated predictions. We demonstrate our approach, which involves a crucial modification of the method of "surrogate variable analysis", on studies of multiple sclerosis. We find improved agreement across varying study designs, platforms, and tissues, and are able to make a number of novel predictions. Our analysis implicates several metabolic pathways contributing to the emerging understanding of metabolic involvement in MS pathology.
LBR21 - Matrix geometry determines optimal cancer migration strategy and modulates response to therapeutic agents
Room: 202B/CDate: Tuesday, July 17
Author(s):
Melda Tozluoglu, Cancer Research UK, uk
Session Chair: Christina Curtis
Abstract Show
Cell motility is required for many biological processes, including cancer metastasis. The molecular requirements for migration and morphology of migrating cells can vary considerably depending on matrix geometry; therefore, predicting the optimal migration strategy or the effect of experimental perturbation is difficult. Here, we present a model of single cell motility that encompasses actin polymerisation based protrusions, cell cortex asymmetry, membrane blebbing, local heterogeneity, cell-extracellular matrix adhesion, and varying extracellular matrix geometries. This is used to explore the theoretical requirements for rapid migration in different matrix geometries. Confined matrix geometries cause profound changes in the relationship of adhesion and contractility to cell velocity; indeed cell-matrix adhesion is dispensable for migration in discontinuous confined environments. The utility of the model is shown by predicting the effect of different drugs and integrin depletion in vivo based only on simple in vitro measurements. Multiphoton intravital imaging of melanoma is used to verify bleb-driven migration of both melanoma and endothelial cells at tumour margins, and the predicted response to drugs.
TOPLBR22 - Single Sample Expression-Anchored Mechanisms Predict Survival in Head and Neck Cancer
Room: 202B/CDate: Tuesday, July 17Author(s):
Yves Lussier, University of Illinois at Chicago, us
Yves Lussier, University of Illinois at Chicago, United States
Session Chair: Christina Curtis
Abstract Show
Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic interpretation of expression arrays remains challenging. Additionally, there is surprisingly little gene overlap among distinct clinically validated signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. Comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor controls, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” among three HNSCC datasets is more significant than the gene overlap. These Oncogenic FAIME Features of HNSCC accurately discriminated tumors from control tissues and stratify recurrence-free survival in patients from two independent studies. Previous approaches depending on group assignment of individual samples before learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies.
TOPLBR23 - Exploring the subclonal architecture of breast cancer
Room: 202B/CDate: Tuesday, July 17Author(s):
David Wedge, Wellcome Trust Sanger Institute, uk
Session Chair: Christina Curtis
Abstract Show
Although the existence of substantial genetic heterogeneity within a tumour is now widely accepted, fundamental questions remain about the dynamics of Darwinian evolution in cancer. Our work aims to answer some of these questions using a variety of bioinformatic algorithms to characterise the subclonal architecture of 21 breast cancers from their whole-genome sequences.
We gain substantial statistical power to discriminate copy number aberrations (CNAs) present in a small fraction of tumor cells through the application of haplotype phasing. Further, by combining novel segmentation algorithms, including a Hierarchical Dirichlet Process - Hidden Markov Model, with constraints that reflect the known structure of the sequence data, we are able to detect CNAs present in less than 5% of the sampled cells.
We model the patterns of clonal and subclonal single nucleotide mutations using a Bayesian Dirichlet process, which simultaneously identifies the number of subclones, the fraction of tumour cells within each subclone and the mutation burden within each subclone. Using novel methods to phase mutations relative to each other and to heterozygous SNP loci, this information is used to discern the phylogenetic relationships between the subclones.
Applying our methods to 20 breast cancers reveals a complex subclonal landscape, reflecting the variety of previous genomic aberrations and clonal expansions that have shaped the tumours. In particular, they show that every tumour harbours a dominant subclone, whose expansion may represent the final rate-limiting step in carcinogenesis.
TOPWe gain substantial statistical power to discriminate copy number aberrations (CNAs) present in a small fraction of tumor cells through the application of haplotype phasing. Further, by combining novel segmentation algorithms, including a Hierarchical Dirichlet Process - Hidden Markov Model, with constraints that reflect the known structure of the sequence data, we are able to detect CNAs present in less than 5% of the sampled cells.
We model the patterns of clonal and subclonal single nucleotide mutations using a Bayesian Dirichlet process, which simultaneously identifies the number of subclones, the fraction of tumour cells within each subclone and the mutation burden within each subclone. Using novel methods to phase mutations relative to each other and to heterozygous SNP loci, this information is used to discern the phylogenetic relationships between the subclones.
Applying our methods to 20 breast cancers reveals a complex subclonal landscape, reflecting the variety of previous genomic aberrations and clonal expansions that have shaped the tumours. In particular, they show that every tumour harbours a dominant subclone, whose expansion may represent the final rate-limiting step in carcinogenesis.
LBR24 - The Landscape of Somatic Structural Variations in Human Cancer Genomes
Room: 202B/CDate: Tuesday, July 17Author(s):
Lixing Yang, Harvard University, us
Lixing Yang, Harvard, United States
Peter Park, Harvard, United States
Session Chair: Christina Curtis
Abstract Show
The cancer genome is known to harbor various somatic rearrangements. However, the full spectrum of these alterations and their underlying mechanisms remain poorly understood. Here, we performed a comprehensive identification of somatic Structural Variations (SVs) and the mechanisms generating them, using high-coverage whole-genome sequencing data of tumor and matched normal samples from 48 individuals across five tumor types (glioblastoma, ovarian, colon, prostate and multiple myeloma). By analyzing a total of 160 billion Illumina short reads, 4555 somatic SVs have been identified with true positive rate of 91%. The patterns of rearrangements are highly variable across tumor types and among individuals, with translocations (46%) being the most abundant, followed by deletions (36%) and tandem duplications (18%). Our detailed reconstruction of the events responsible for CDKN2 loss, EGFR and CDK4 gain in glioblastoma revealed much more complex sets of events than previously assumed, sometimes involving dozens of fragments. Our analysis of the breakpoints at base pair resolution shows that focal CDKN2 loss is often generated by non-homologous end joining but could also be generated by microhomology-mediated end joining or template switching mechanisms. Focal amplifications are sometimes generated by complex tandem duplications via template switching mechanism. This study provides new insights on cancer genome rearrangements and their contribution to cancer progression.
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PP01 (PT) - GenomeRing: alignment visualization based on SuperGenome coordinates
Room: Grand BallroomDate: Sunday, July 15Author(s):
Alexander Herbig, University of Tübingen, Germany
Günter Jäger, University of Tübingen
Florian Battke, University of Tübingen
Kay Nieselt, University of Tübingen
Session Chair: Robert Murphy
Abstract Show
Motivation:
The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools which can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge.
Results:
Here we present two complementary approaches to tackle this problem. Firstly, we propose the SuperGenome concept for the computation of a common coordinate system for all genomes in a multiple alignment. This coordinate system allows for the consistent placement of genome annotations in the presence of insertions, deletions, and rearrangements. Secondly, we present the GenomeRing visualization which, based on the SuperGenome, creates an interactive visualization of the multiple genome alignment in a circular layout. We demonstrate our methods by applying them to an alignment of Campylobacter jejuni strains for the discovery of genomic islands as well as to an alignment of Helicobacter pylori, which we visualize in combination with gene expression data.
TOPPP02 (HT) - Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes
Room: 104ADate: Sunday, July 15
Author(s):
Saliha Ece Acuner Ozbabacan, Koc University, tr
Ozlem Keskin, Koc University, Turkey
Ruth Nussinov, NCI-Frederick, United States
Attila Gursoy, Koc University, Turkey
Session Chair: Yanay Ofran
TOP
PP03 (HT) - Prediction by promoter logic in bacterial quorum sensing
Room: 104BDate: Sunday, July 15Author(s):
Mukund Thattai, National Centre for Biological Sciences, in
Navneet Rai, National Centre for Biological Sciences, India
Rajat Anand, National Centre for Biological Sciences, India
Krishna Ramkumar, Indian Institute of Technology Bombay, India
Varun Sreenivasan, St. Xavier’s College, India
Sugat Dabholkar, National Centre for Biological Sciences, India
Kareenhalli Venkatesh, Indian Institute of Technology Bombay, India
Session Chair: Paul Horton
TOP
PP04 (PT) - Joint Stage Recognition and Anatomical Annotation of Drosophila Gene Expression Patterns
Room: Grand BallroomDate: Sunday, July 15
Author(s):
Xiao Cai, University of Texas at Arlington, United States
Hua Wang, University of Texas at Arlington
Heng Huang, University of Texas at Arlington
Chris Ding, University of Texas at Arlington
Session Chair: Robert Murphy
Abstract Show
Motivation: Staining the mRNA of a gene via in situ hybridization (ISH) during the development of a Drosophila melanogaster embryo delivers the detailed spatio-temporal patterns of the gene expression. Many related biological problems such as the detection of co-expressed genes, co-regulated genes, and transcription factor binding motifs rely heavily on the analysis of these image patterns. To provide the text-based pattern searching for facilitating related biological studies, the images in the Berkeley Drosophila Genome Project (BDGP) study are annotated with developmental stage term and anatomical ontology terms manually by domain experts. Due to the rapid increasing number of such images and the inevitable bias annotations by human curators, it is necessary to develop an automatic method to recognize the developmental stage and annotate anatomical terms.
Results: In this paper, we propose a novel computational model for joint stage classification and anatomical terms annotation of Drosophila gene expression patterns. We introduce a new Tri-Relational Graph (TG) model that comprises the data graph, anatomical terms graph, developmental stage term graph, and connects them by three additional graphs induced from stage or annotation label assignments. Upon the TG model, we present a Preferential Random Walk (PRW) method to jointly recognize developmental stage and annotate anatomical terms by utilizing the interrelations between two tasks. The experimental results on two refined BDGP data sets demonstrate our joint learning method can achieve superior prediction results on both tasks than the state-of-the-art methods.
TOPPP05 (HT) - Understanding human disease through 3D protein interactome network
Room: 104ADate: Sunday, July 15Author(s):
Haiyuan Yu, Cornell University, us
Xiujuan Wang, Cornell University, United States
Xiaomu Wei, Weill Cornell Medical College, United States
Bram Thijssen, Maastricht University, Netherlands
Jishnu Das, Cornell University, United States
Steven Lipkin, Weill Cornell Medical College, United States
Session Chair: Yanay Ofran
TOP
PP06 (HT) - An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways
Room: 104BDate: Sunday, July 15Author(s):
Pavel Sumazin, Columbia, us
Andrea Califano, Columbia, United States
Session Chair: Paul Horton
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PP07 (PT) - Improved synapse detection for mGRASP-asssisted brain connectivity mapping
Room: Grand BallroomDate: Sunday, July 15
Author(s):
Linqing Feng, Korea Institute of Science and Technology, Republic of Korea
Ting Zhao, Zhejiang University
Jinhyun Kim, Korea Institute of Science and Technology
Session Chair: Robert Murphy
Abstract Show
Motivation: A new technique, mammalian GFP reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To! characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy.
Results: We developed a fully-automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a Variational Bayesian Gaussian Mixture Model. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors.
Availability: The software is available on http! ://jinny.kist.re.kr
PP08 (HT) - Guilt by association is the exception rather than the rule in gene networks
Room: 104ADate: Sunday, July 15
Author(s):
Jesse Gillis, University of British Columbia, ca
Session Chair: Yanay Ofran
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PP09 (PT) - Nonparametric Bayesian Inference for Perturbed and Orthologous Gene Regulatory Networks
Room: 104BDate: Sunday, July 15Author(s):
Christopher A. Penfold, University of Warwick, United Kingdom
Vicky Buchanan-Wollaston, University of Warwick
Katherine J. Denby, University of Warwick
David L. Wild, University of Warwick
Session Chair: Paul Horton
Abstract Show
The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. Here we outline a hierarchical, nonparametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions i.e., where switching events could potentially arise under the different treatments; and (ii) for inference in evolutionary related species in which orthologous GRNs exist.
The hierarchical inference outperforms related (but non- hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one-hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to abiotic stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses.
TOPPP10 (PT) - Protein Subcellular Location Pattern Classification in Cellular Images Using Latent Discriminative Models
Room: Grand BallroomDate: Sunday, July 15
Author(s):
Jieyue Li, Carnegie Mellon University, United States
Liang Xiong, Carnegie Mellon University
Robert Murphy, Carnegie Mellon University
Jeff Schneider, Carnegie Mellon University
Session Chair: Robert Murphy
Abstract Show
In human proteome, the subcellular location pattern is crucial for understanding the functions of a protein. This pattern is essentially characterized by the co-localization of the protein and the components in the cell. In this paper, we address the protein pattern classification problem based on the confocal immune-fluorescence cellular images from the Human Protein Atlas (HPA) project. In our HPA data set, each cell has the staining images of one protein and three reference components, and in the meanwhile there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. Compared to traditional cell based methods, this region based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient based methods to maximize the data likelihood.
In the experiments, we show that the proposed models can both help improve the classification accuracies on synthetic data and real cellular images. The best overall accuracy we report in this paper for classifying $942$ proteins into $13$ classes of patterns is about $84.6\%$, which to our knowledge is the best so far. In addition, we can give these results biological interpretations.
TOPPP11 (HT) - Putting genetic interactions in context through a global modular decomposition
Room: 104ADate: Sunday, July 15Author(s):
Chad Myers, University of Minnesota, us
Jeremy Bellay, University of Minnesota, United States
Gowtham Atluri, University of Minnesota, United States
Tina Sing, University of Toronto, Canada
Kiana Toufighi, Centre for Genomic Regulation, Spain
Michael Costanzo, University of Toronto, Canada
Philippe Souza Moraes Ribeiro, University of Minnesota, United States
Gaurav Pandey, Mount Sinai School of Medicine, United States
Joshua Baller, University of Minnesota, United States
Benjaim VanderSluis, University of Minnesota, United States
Magali Michaut, University of Toronto, Canada
Sangjo Han, University of Toronto, Canada
Philip Kim, University of Toronto, Canada
Grant Brown, University of Toronto, Canada
Brenda Andrews, University of Toronto, Canada
Charles Boone, University of Toronto, Canada
Vipin Kumar, University of Minnesota, United States
Session Chair: Yanay Ofran
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PP12 (PT) - NOrMAL: Accurate Nucleosome Positioning using a Modified Gaussian Mixture Model
Room: 104BDate: Sunday, July 15Author(s):
Anton Polishko, UC Riverside, United States
Nadia Ponts, UC Riverside
Karine Le Roch, UC Riverside
Stefano Lonardi, UC Riverside
Session Chair: Paul Horton
Abstract Show
Motivation: Nucleosomes are the basic elements of DNA chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome position for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle "overlapping" nucleosomes. Also, they can only provide the positions of nucleosomes and their occupancy level, while it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes, and/or whether nucleosome are well-positioned or "fuzzy" in the sequenced cell sample.
Results: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization (EM) algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. Experimental results show that our method detects a significantly higher number of nucleosomes than Template Filtering.
TOPPP13 (HT) - Disrupting human pathways by minimal miRNA sets
Room: Grand BallroomDate: Sunday, July 15Author(s):
Ohad Balaga, The Hebrew University of Jerusalem, il
Guy Naamati, The Hebrew University of Jerusalem, Israel
Yitzhak Friedman, The Hebrew University of Jerusalem, Israel
Michal Linial, The Hebrew University of Jerusalem, Israel
Session Chair: Lenore Cowen
TOP
PP14 (HT) - cn.MOPS: mixture of Poissons for discovering copy number variations in next generation sequencing data with a low false discovery rate
Room: 104ADate: Sunday, July 15Author(s):
Guenter Klambauer, Johannes Kepler University of Linz, at
Karin Schwarzbauer, Johannes Kepler University of Linz, Austria
Andreas Mayr, Johannes Kepler University of Linz, Austria
Djork-Arné Clevert, Johannes Kepler University of Linz, Austria
Andreas Mitterecker, Johannes Kepler University of Linz, Austria
Ulrich Bodenhofer, Johannes Kepler University of Linz, Austria
Sepp Hochreiter, Johannes Kepler University of Linz, Austria
Session Chair: Eran Halperin
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PP15 (HT) - RNA secondary structure mediates alternative 3’ss selection in Saccharomyces cerevisiae
Room: 104BDate: Sunday, July 15Author(s):
Eduardo Eyras, Universitat Pompeu Fabra, es
Mireya Plass, Universitat Pompeu Fabra, Spain
Josep Vilardell, CSIC, Spain
Session Chair: Janet Kelso
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PP16 (HT) - Identification of a Novel Class of Farnesylation Targets by Structure-Based Modeling of Binding Specificity
Room: Grand BallroomDate: Sunday, July 15
Author(s):
Ora Schueler-Furman, The Hebrew University, il
Nir London, Hebrew University, Israel
Hougland James, Syracuse University, United States
Carol Fierke, University of Michigan, United States
Yousef Abu-Kwaik, University of Louisville, United States
Tasneem Al-Qadan, University of Louisville, United States
Christopher Price, University of Louisville, United States
Session Chair: Lenore Cowen
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PP17 (HT) - Systematic Detection of Epistatic Interactions Based on Allele Pair Frequencies
Room: 104ADate: Sunday, July 15
Author(s):
Marit Ackermann, Technical University Dresden, de
Andreas Beyer, Technical University Dresden, Germany
Session Chair: Eran Halperin
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PP18 (HT) - Deciphering the Gene Translation Code and its Modeling
Room: 104BDate: Sunday, July 15Author(s):
Tamir Tuller, Tel-Aviv University, il
Hadas Zur, Tel Aviv University, Israel
Nir Gazit , Tel Aviv University, Israel
Marin Kupiec, Tel Aviv University, Israel
Eytan Ruppin , Tel Aviv University, Israel
Michal Ziv-Ukelson, Ben Gurion University, Israel
Isana Veksler-Lublinsky, Ben Gurion University, Israel
Session Chair: Janet Kelso
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PP19 (PT) - How networks change with time
Room: Grand BallroomDate: Sunday, July 15
Author(s):
Yongjin Park, Johns Hopkins University, United States
Joel Bader, Johns Hopkins University
Session Chair: Lenore Cowen
Abstract Show
Motivation: Biological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes, and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals.
Results: A new method predicts the dynamic state of protein complexes in a cell, with protein expression inferred from transcription profile time courses and protein complexes inferred by joint analysis of protein co-expression and protein-protein interaction maps. Two algorithmic advances are presented: a new method, DHAC (Dynamical Hierarchical Agglomerative Clustering), for clustering time-evolving networks; and a companion method, MATCH-EM, for matching corresponding clusters across time-points. With link prediction as an objective assessment metric, DHAC provides a substantial advance over existing clustering methods. An application to the yeast metabolic cycle demonstrates how waves of gene expression correspond to individual protein complexes. Our results suggest regulatory mechanisms for assembling the mitochondrial ribosome and illustrate dynamic changes in the components of the nuclear pore.
Availability: All source code and data will be available through a BSD open source license as supplementary material and at www.baderzone.org.
TOPPP20 (PT) - Leveraging Input and Output Structures For Joint Mapping of Epistatic and Marginal eQTLs
Room: 104ADate: Sunday, July 15
Author(s):
Seunghak Lee, Carnegie Mellon University, United States
Eric Xing, Carnegie Mellon University
Session Chair: Eran Halperin
Abstract Show
Motivation: Since many complex disease and expression phenotypes are the outcome of intricate perturbation of molecular networks underlying gene regulation resulted from interdependent genome variations, association mapping of causal QTLs or eQTLs must consider both additive and epistatic effects of multiple candidate genotypes. This problem poses a significant challenge to contemporary genome-wide-association (GWA) mapping technologies because of its computational complexity. Fortunately, a plethora of recent developments in biological network community, especially the availability of genetic interaction networks, make it possible to construct informative priors of complex interactions between genotypes, which can substantially reduce the complexity and increase the statistical power of GWA inference.
Results: In this paper, we consider the problem of learning a multi-task regression model while taking advantage of the prior information on structures on both the inputs (genetic variations) and outputs (expression levels). We propose a novel regularization scheme over multi-task regression called structured jointly input/output lasso based on an L1/L2 norm, which allows shared sparsity patterns for related inputs and outputs to be optimally estimated. Such patterns capture multiple related SNPs that jointly influence multiple related expression traits. In addition, we generalize this new multi-task regression to structurally regularized polynomial regression to detect epistatic interactions with manageable complexity by exploiting the prior knowledge on candidate epistatic SNPs from biological experiments. We demonstrate our method on simulated and yeast eQTL datasets.
TOPPP21 (PT) - Lineage based identification of cellular states and expression programs
Room: 104BDate: Sunday, July 15Author(s):
Tatsunori Hashimoto, Massachusetts Institute of Technology, United States
Tommi Jaakkola, Massachusetts Institute of Technology
Richard Sherwood, Brigham and Women's Hospita
Esteban Mazzoni, Columbia University Medical Center
Hynek Witchterle, Columbia University Medical Center
David Gifford, Massachusetts Institute of Technology
Session Chair: Janet Kelso
Abstract Show
We present a method, Lineage Program, that uses the developmental lineage relationship of observed gene expression measurements to improve the learning of developmentally relevant cellular states and expression programs. We find that incorporating lineage information allows us to significantly improve both the predictive power and interpretability of expression programs that are derived from expression measurements from in vitro differentiation experiments. The lineage tree of a differentiation experiment is a tree graph whose nodes describe all of the unique expression states in the input expression measurements, and edges describe the experimental perturbations applied to cells. Our method, LineageProgram, is based on a log-linear model with parameters that reflect changes along the lineage tree. Regularization with L1 based methods controls the parameters in three distinct ways: the number of genes which change between two cellular states, the number of unique cellular states, and the number of underlying factors responsible for changes in cell state. The model is estimated with proximal operators to quickly discover a small number of key cell states and gene sets. Comparisons with existing factorization techniques such as singular value decomposition and nonnegative matrix factorization show that our method provides higher predictive power in held-out tests while inducing sparse and biologically relevant gene sets.
TOPPP22 (PT) - A single-source k shortest paths algorithm to infer regulatory pathways in a gene network
Room: Grand BallroomDate: Sunday, July 15
Author(s):
Yu-Keng Shih, The Ohio State University, United States
Srinivasan Parthasarathy, The Ohio State University
Session Chair: Lenore Cowen
Abstract Show
Motivation: Inferring the underlying signaling pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the transmission and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential signaling pathways passing through this gene.
Results: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source $k$ shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines.
TOPPP23 (PT) - Incorporating Prior Information into Association Studies
Room: 104ADate: Sunday, July 15Author(s):
Gregory Darnell, UCLA , United States
Dat Duong, University of California Berkeley
Buhm Han, University of California
Eleazar Eskin, UCLA
Session Chair: Eran Halperin
Abstract Show
Recent technological developments in measuring genetic variation have ushered in an era of genome wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in a individuals who with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistics which takes into account prior information. Our method improves both power and resolution by 43.5% and 45%, repsectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are intepretable since the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power.
TOPPP24 (PT) - Matching experiments across species using expression values and textual information
Room: 104BDate: Sunday, July 15Author(s):
Aaron Wise, Carnegie Mellon University, United States
Zoltan Oltvai, University of Pittsburgh
Ziv Bar-Joseph, Carnegie Mellon University
Session Chair: Janet Kelso
Abstract Show
Motivation: With the vast increase in the number of gene expression datasets deposited in public databases, novel techniques are required to analyze and mine this wealth of data. Similar to the way BLAST enables cross-species comparison of sequence data, tools that enable cross-species expression comparison will allow us to better utilize these datasets: Cross-species expression comparison enables us to address questions in evolution and development, and further allows the identification of disease related genes and pathways that play similar roles in humans and model organisms. Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species.
Results: To enable better cross-species comparisons, we developed methods for automatically identifying pairs of similar expression datasets across species. Our method uses a co-training algorithm to combine a model of expression similarity with a model of the text which accompanies the expression experiments. The co-training method outperforms previous methods based on expression similarity alone. Using expert analysis, we show that the new matches identified by our method indeed capture biological similarities across species. We then use the matched expression pairs between human and mouse to recover known and novel cycling genes as well as to identify genes with possible involvement in diabetes. By providing the ability to identify novel candidate genes in model organisms, our method opens the door to new models for studying diseases.
TOPPP25 (HT) - HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Room: Grand BallroomDate: Monday, July 16Author(s):
Johannes Soeding, Ludwig-Maximilians-Univeristaet Muenchen, de
Michael Remmert, Ludwig-Maximilians-Univeristaet Muenchen, Germany
Andreas Biegert, genedata.com, Germany
Andreas Hauser, Ludwig-Maximilians-Univeristaet Muenchen, Germany
Session Chair: David Gifford
TOP
PP26 (PT) - Toward 3D structure prediction of large RNA molecules: An integer programming framework to insert local 3D motifs in RNA secondary structure
Room: 104ADate: Monday, July 16Author(s):
Vladimir Reinharz, McGill University, Canada
Francois Major, Institute for Research in Immunology and Cancer
Jerome Waldispuhl, McGill University
Session Chair: Cenk Sahinalp
Abstract Show
Motivation: The prediction of RNA three-dimensional structures from its sequence only is a milestone to RNA function analysis and prediction. In recent years, many methods addressed this challenge, ranging from cycle decomposition and fragment assembly to molecular dynamics simulations. However, their predictions remain fragile and limited to small RNAs. To expand the range and accuracy of these techniques, we need to develop algorithms that will enable to use all the structural information available. In particular, the energetic contribution of secondary structure interactions is now well documented, but the quantification of non-canonical interactions – those shaping the tertiary structure – is poorly understood. Nonetheless, even if a complete RNA tertiary structure energy model is currently unavailable, we now have catalogues of local 3D structural motifs including non-canonical base pairings. A practical objective is thus to develop techniques enabling us to use this knowledge for robust RNA tertiary structure predictors. Results: In this work, we introduce RNA-MoIP, a program that benefits from the progresses made over the last 30 years in the field of RNA secondary structure prediction and expands these methods to incorporate the novel local motif information available in databases. Using an integer programming framework, our method refines predicted secondary structures (i.e. removes incorrect canonical base-pairs) to accommodate the insertion of RNA 3D motifs (i.e. hairpins, internal loops and k-way junctions). Then, we use predictions as templates to generate complete 3D structures with the MC-Sym program. We benchmarked RNA-MoIP on a set of 9 RNAs with sizes varying from 53 to 128 nucleotides. We show that our approach (i) improves the accuracy of canonical base pair predictions, (ii) identifies the best secondary structures in a pool of sub-optimal structures, and (iii) predicts accurate 3D structures of large RNA molecules. RNA-MoIP is publicly available at: http://csb.cs.mcgill.ca/RNAMoIP
TOPPP27 (HT) - Differential oestrogen receptor binding is associated with clinical outcome in breast cancer
Room: 104BDate: Monday, July 16Author(s):
Rory Stark, Cancer Research UK, uk
Caryn Ross-Innes, Hutchison/MRC, United Kingdom
Teschendorff Andrew, University College London, United Kingdom
Holmes Kelly, Cancer Research UK, United Kingdom
Raza Ali, Cancer Research UK, United Kingdom
Mark Dunning, Cancer Research UK, United Kingdom
Gordon Brown, Cancer Research UK, United Kingdom
Ondrej Gojis, Charles University, Cz
Ian Ellis, Nottingham University, United Kingdom
Andrew Green, Nottingham University, United Kingdom
Simak Ali, Imperial College London, United Kingdom
Suet-Feung Chin, Cancer Research UK, United Kingdom
Carlo Palmieri, Imperial College London, United Kingdom
Carlos Caldas, Cancer Research UK, United Kingdom
Jason Carroll, Cancer Research UK, United Kingdom
Session Chair: Carl Kingsford
TOP
PP28 (HT) - A structural systems biology approach to polypharmacological drug discovery
Room: Grand BallroomDate: Monday, July 16 Author(s):
Lei Xie, The City University of New York, us
Li Xie, University of California, San Diego , United States
Philip Bourne, University of California, San Diego , United States
Thomas Evangelidis, Biomedical Research Foundation Academy of Athens, Greece
Session Chair: David Gifford
TOP
PP29 (PT) - Identification of Sequence-Structure RNA Binding Motifs for SELEX Derived Aptamers
Room: 104ADate: Monday, July 16Author(s):
Jan Hoinka, NCBI, NIH, United States
Elena Zotenko, Garvan Institute for Medical Research
Adam Friedman, UNC Chapel Hill
Zuben E. Sauna, US Food and Drug Administration
Teresa Przytycka, NIH
Session Chair: Cenk Sahinalp
Abstract Show
Motivation: Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state of the art technology to isolate single stranded (ribo)nucleic acid fragments, named aptamers, that bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs, and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind it's wide application due to the lack of dedicated computational approaches. At the same time the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process.
Results: To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble based approach. Our new algorithmic solutions are accompanied with rigorous statistical analysis. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the authors findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.
TOPPP30 (HT) - Construction of regulatory networks using expression time-series data of a genotyped population
Room: 104BDate: Monday, July 16Author(s):
Ka Yee Yeung, University of Washington, us
Kenneth Dombek, University of Washington, United States
Kenneth Lo, University of Washington, United States
John Mittler, University of Washington, United States
Jun Zhu, Sage Bionetworks, United States
Eric Schadt, Pacific Biosciences, United States
Roger Bumgarner, University of Washington, United States
Adrian Raftery, University of Washington, United States
Session Chair: Carl Kingsford
TOP
PP31 (HT) - Integrating energy calculations with functional assays to decipher the specificity of G-protein inactivation by RGS proteins
Room: Grand BallroomDate: Monday, July 16Author(s):
Mickey Kosloff, University of Haifa, il
Vadim Arshavsky, Duke University, United States
Amanda Travis, Duke University, United States
Dustin Bosch, University of North Carolina at Chapel Hill, United States
David Siderovski, University of North Carolina at Chapel Hill, United States
Session Chair: David Gifford
TOP
PP32 (PT) - GraphClust: alignment-free structural clustering of local RNA secondary structures
Room: 104ADate: Monday, July 16Author(s):
Fabrizio Costa, Albert-Ludwigs-University Freiburg, Germany
Fabrizio Costa, Albert-Ludwigs-University Freiburg
Dominic Rose, Albert-Ludwigs-University Freiburg
Rolf Backofen, Albert-Ludwigs-University Freiburg
Session Chair: Cenk Sahinalp
Abstract Show
Motivation: Clustering according to sequence-structure similarity has now become a generally accepted scheme for ncRNA annotation. Its application to
complete genomic sequences as well as whole transcriptomes is therefore desirable but hindered by extremely high computational costs.
Results: We present a novel linear-time, alignment-free method for comparing
and clustering RNAs according to sequence and structure. The approach scales to datasets of hundreds of thousands of sequences. The quality of the retrieved clusters has been benchmarked against known ncRNA datasets and is comparable to state-of-the-art sequence-structure methods although achieving speed-ups of several orders of magnitude. A selection of applications aiming at the detection of novel structural non-coding RNAs are presented. Exemplarily, we predicted local structural elements specific to lincRNAs likely functionally associating involved transcripts to vital processes of the human nervous system. In total, we predicted 349 local structural RNA elements.
TOPPP33 (HT) - A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information
Room: 104BDate: Monday, July 16Author(s):
Xiaotu Ma Ma, The University of Texas at Dallas, us
Ashwinikumar Kulkarni, The University of Texas at Dallas, United States
Zhihua Zhang, The University of Texas at Dallas, United States
Zhenyu Xuan, The University of Texas at Dallas, United States
Michael Zhang, The University of Texas at Dallas, United States
Session Chair: Carl Kingsford
TOP
PP34 (HT) - Text Mining Improves Prediction of Protein Functional Sites
Room: Grand BallroomDate: Monday, July 16Author(s):
Karin Verspoor, National ICT Australia, au
Michael Wall, Los Alamos National Laboratory, United States
Judith Cohn, Los Alamos National Laboratory, United States
Komandur Ravikumar, Mayo Clinic, United States
Session Chair: David Gifford
TOP
PP35 (PT) - Detection of Allele-Specific Methylations through a Generalized Heterogeneous Epigenome Model
Room: 104ADate: Monday, July 16Author(s):
Qian Peng, UCSD, United States
Joseph Ecker, The Salk Institute for Biological Studies
Session Chair: Cenk Sahinalp
Abstract Show
Motivations: High throughput sequencing has made it possible to sequence DNA methylation of a whole genome at the single-base resolution. A sample however may contain a number of distinct methylation patterns. For instance, cells of different types and in different developmental stages may have different methylation patterns. Alleles may be differentially methylated, which may partially explain that the large portions of epigenomes from single cell types are partially methylated, and may have ma jor effects on transcriptional output. Approaches relying on DNA sequence polymorphism to identify individual patterns from a mixture of heterogeneous epigenomes are insufficient as methylcytosines occur at a much higher density than SNPs.
Results: We have developed a mixture model-based approach for resolving distinct epigenomes from a heterogeneous sample. In particular, the model is applied for the detection of allele-specific methylations (ASM). The methods are tested on a synthetic methylome and applied to an Arabidopsis single root cell methylome.
TOPPP36 (HT) - Functional conservation of enhancers without sequence conservation
Room: 104BDate: Monday, July 16Author(s):
Ivan Ovcharenko, NIH, us
Leila Taher, NIH, United States
Andrew McCallion, JHU, United States
Marcelo Nobrega, University of Chicago, United States
Session Chair: Carl Kingsford
TOP
PP37 (HT) - Why CDRs are not what you think they are or How to identify the real antigen binding sites
Room: 202 B/CDate: Monday, July 16Author(s):
Vered Kunik, Bar Ilan University, il
Yanay Ofran, Bar Ilan University, Israel
Bjoern Peters, La Jolla Institute for Allergy and Immunology, United States
Session Chair: Bonnie Berger
TOP
PP38 (HT) - Putative amino acid determinants of the emergence of the 2009 influenza A (H1N1) virus in the human population
Room: 104ADate: Monday, July 16Author(s):
Nir Ben-Tal, Tel Aviv University, il
Session Chair: Burkhard Rost
TOP
PP39 (HT) - Mapping and analysis of chromatin state dynamics in nine human cell types
Room: 104BDate: Monday, July 16Author(s):
Jason Ernst, University of California Los Angelels, us
Pouya Kheradpour, Massachusetts Institute of Technology, United States
Tarjei Mikkelsen, Broad Institute, United States
Noam Shoresh, Broad Institute, United States
Lucas Ward, Broad Institute, United States
Charles Epstein, Broad Institute, United States
Xiaolan Zhang, Broad Institute, United States
Li Wang, Broad Institute, United States
Robyn Issner, Broad Institute, United States
Michael Coyne, Broad Institute, United States
Manching Ku, Massachusetts General Hospital, United States
Timothy Durham, Broad Institute, United States
Manolis Kellis, Massachusetts Institute of Technology, United States
Bradley Bernstein, Massachusetts General Hospital, United States
Session Chair: Reinhard Schneider
TOP
PP40 (HT) - Image-derived, Three-dimensional Generative Models of Cellular Organization
Room: 104CDate: Monday, July 16Author(s):
Robert Murphy, Carnegie Mellon University, us
Tao Peng, Microsoft Research, United States
Session Chair: Hagit Shatkay
TOP
PP41 (HT) - Molecular architecture of the 26S proteasome holocomplex determined by an integrative approach
Room: 202 B/CDate: Monday, July 16Author(s):
Keren Lasker, Stanford University, us
Friedrich Förster, Max-Planck-Institute of Biochemistry, Germany
Stefan Bohn, Max-Planck-Institute of Biochemistry, Germany
Thomas Walzthoeni, University of Zürich, Switzerland
Elizabeth Villa, Max-Planck-Institute of Biochemistry, Germany
Pia Unverdorben, Max-Planck-Institute of Biochemistry, Germany
Florian Beck, Max-Planck-Institute of Biochemistry, Germany
Ruedi Aebersold, University of Zürich, Switzerland
Andrej Sali, University of California San Francisco, United States
Wolfgang Baumeister, Max-Planck-Institute of Biochemistry, Germany
Session Chair: Bonnie Berger
TOP
PP42 (HT) - Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels
Room: 104ADate: Monday, July 16Author(s):
Marcel Schulz, Carnegie Mellon University, us
Daniel Zerbino, University of California Santa Cruz, United States
Martin Vingron, Max Planck Institute for Molecular Genetics, Germany
Ewan Birney, European Bioinformatics Institute, United Kingdom
Session Chair: Burkhard Rost
TOP
PP43 (HT) - Proteomics Signature Profiling (PSP): A novel contextualization approach applied towards cancer proteomics
Room: 104BDate: Monday, July 16
Author(s):
Wilson Wen Bin Goh, Imperial College London, uk
Session Chair: Reinhard Schneider
TOP
PP44 (HT) - Toward interoperable bioscience data
Room: 104CDate: Monday, July 16Author(s):
Susanna-Assunta Sansone, University of Oxford, uk
Philippe Rocca-Serra, University of Oxford, United Kingdom
Eamonn Maguire, University of Oxford, United Kingdom
Dawn Field, NERC, United Kingdom
Chris Taylor, EMBL, United Kingdom
Oliver Hofmann, Harvard School of Public Health, United States
Hong Fang, ICF International Company, United States
Steffen Neumann, Leibniz Institute of Plant Biochemistry, Germany
Weida Tong, FDA, United States
Linda Amaral-Zettler, Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, International Census of Marine Microbes, United States
Kimberly Begley, Ontario Institute for Cancer Research, Canada
Tim Booth, NERC, United Kingdom
Lydie Bougueleret, SIB, Switzerland
Gully Burns, Information Sciences Institute, United States
Brad Chapman, Harvard School of Public Health, United States
Tim Clark, Harvard Medical School, United States
Lee-Ann Coleman, The British Library, United Kingdom
Jay Copeland, Harvard Medical School, United States
Sudeshna Das, Harvard Medical School, United States
Antoine de Daruvar, Université de Bordeaux, France
Paula de Matos, EMBL, United Kingdom
Ian Dix, AstraZeneca, United Kingdom
Scott Edmunds, GigaScience, China
Chris T. Evelo, The Netherlands Bioinformatics Centre, Netherlands
Mark J. Forster, Syngenta, United Kingdom
Pascale Gaudet, SIB, Switzerland
Jack Gilbert, Argonne National Laboratory, United States
Carole Goble, University of Manchester, United Kingdom
Julian L. Griffin, University of Cambridge, United Kingdom
Daniel Jacob, Université de Bordeaux, CBiB , France
Jos Kleinjans, Netherlands Toxicogenomics Centre, Netherlands
Lee Harland, ConnectedDiscovery Ltd, United Kingdom
Kenneth Haug, EMBL, United Kingdom
Henning Hermjakob, EMBL, United Kingdom
Shannan J. Ho Sui, Harvard School of Public Health, United States
Alain Laederach, University of North Carolina, United States
Shaoguang Liang, GigaScience, China
Stephen Marshall, The Novartis Institutes for BioMedical Research, United Kingdom
Annette McGrath, CSIRO, Australia
Emily M. Merrill, Massachusetts General Hospital, United States
Dorothy Reilly, The Novartis Institutes for BioMedical Research, United States
Magali Roux, University of Pierre and Marie Curie CNRS UMS 7606, France
Caroline E. Shamu, Harvard Medical School, United States
Catherine A. Shang, Bioplatforms Australia Ltd, Australia
Christoph Steinbeck Christoph, EMBL, United Kingdom
Anne Trefethen, University of Oxford, United Kingdom
Bryn Williams-Jones, ConnectedDiscovery Ltd, United Kingdom
Ioannis Xenarios, SIB, Switzerland
Katherine Wolstencroft, University of Manchester, United Kingdom
Session Chair: Hagit Shatkay
TOP
PP45 (PT) - A Conditional Neural Fields model for protein threading
Room: 202 B/CDate: Monday, July 16Author(s):
Jianzhu Ma, Toyota Technological Institute at Chicago, United States
Jian Peng, Toyota Technological Institute at Chicago
Sheng Wang, Toyota Technological Institute at Chicago
Jinbo Xu, Toyota Technological Institute at Chicago
Session Chair: Bonnie Berger
Abstract Show
Motivation: Alignment errors are still the main bottleneck of current template-based protein modeling (TM) methods including protein threading and homology modeling, especially when the sequence identity between two proteins under consideration is low (<30%).
Results: We present a novel protein threading method for much more accurate sequence-template alignment by employing a probabilistic graphical model Conditional Neural Fields (CNF), which aligns one protein sequence to its remote template using a nonlinear scoring function. This scoring function can account for correlation among a variety of protein sequence and structure features, make use of information in the neighborhood of two residues to be aligned, and thus, is much more sensitive than the widely-used linear function or profile-based scoring function. To train this CNF threading model, we employ a novel quality-sensitive method that can directly maximize the expected quality of a set of training alignments, instead of the standard maximum-likelihood method. Experimental results show that our CNF method generates significantly better alignments than the best profile-based and threading methods on several public (but small) benchmarks and very large in-house datasets. Our method outperforms others regardless of protein classes and lengths and works particularly well for proteins with sparse sequence profile due to the effective utilization of structure information. The methodology presented here can also be adapted to protein sequence alignment.
TOPPP46 (HT) - Multiple reference genomes and transcriptomes for Arabidopsis thaliana
Room: 104ADate: Monday, July 16Author(s):
Gunnar Ratsch, Memorial Sloan-Kettering Cancer Center, us
Xiangchao Gan, University of Oxford, United Kingdom
Oliver Stegle, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, Germany
Jonas Behr, Friedrich Miescher Laboratory, Germany
Philipp Drewe, Friedrich Miescher Laboratory, Germany
Joshua G. Steffen, University of Utah, United States
Richard Clark, University of Utah, United States
Edward J. Osborne, University of Utah, United States
Sebastian Schultheiss, Friedrich Miescher Laboratory, Germany
Vipin T. Sreedharan, Friedrich Miescher Laboratory, Germany
Andre Kahles, Friedrich Miescher Laboratory, Germany
Regina Bohnert, Friedrich Miescher Laboratory, Germany
Geraldine Jean, Friedrich Miescher Laboratory, Germany
Katie L. Hildebrand, Kansas State University, United States
Christopher Toomajian, Kansas State University, United States
Rune Lyngsoe, University of Oxford, United Kingdom
Paul Derwent, European Bioinformatics Institute, United Kingdom
Paul Kersey, European Bioinformatics Institute, United Kingdom
Eric Belfield, University of Oxford, United Kingdom
Nicholas Harberd, University of Oxford, United Kingdom
Eric Kemen, The Sainsbury Laboratory, United Kingdom
Paula X. Kover, University of Bath, United Kingdom
Session Chair: Burkhard Rost
TOP
PP47 (HT) - Identifying the unknowns by aligning fragmentation trees
Room: 104BDate: Monday, July 16
Author(s):
Sebastian Böcker, Friedrich-Schiller-University Jena, de
Florian Rasche, Friedrich-Schiller-University Jena, Germany
Kerstin Scheubert, Friedrich-Schiller-University Jena, Germany
Franziska Hufsky, Friedrich-Schiller-University Jena, Germany
Thomas Zichner, European Molecular Biology Laboratory, Germany
Marco Kai, Max Planck Institute for Chemical Ecology, Germany
Session Chair: Reinhard Schneider
TOP
PP48 (HT) - The Three-Dimensional Architecture of a Bacterial Genome and Its Alteration by Genetic Perturbation
Room: 104CDate: Monday, July 16Author(s):
Davide Bau, National Center for Genomic Analysis, es
Mark Umbarger, Harvard Medical School, United States
Esteban Toro, School of Medicine, United States
Matthew Wright, Harvard Medical School, United States
Gregory Porreca, Harvard Medical School, United States
Sun-Hae Hong, School of Medicine, United States
Michael Fero, School of Medicine, United States
Lihua Zhu, Program in Gene Function and Expression, United States
Marc Marti-Renom, National Center for Genomic Analysis , Spain
Harley McAdams, School of Medicine, United States
Lucy Shapiro, School of Medicine, United States
Job Dekker, University of Massachusetts Medical School, United States
George Church, Harvard Medical School, United States
Session Chair: Hagit Shatkay
TOP
PP49 (PT) - Novel domain combinations in proteins encoded by chimeric transcripts
CancelledRoom: TBADate: Monday, July 16Author(s):
Milana Frenkel-Morgenstern, Spain Spanish National Cancer Research Centre (CNIO), Spain
Alfonso Valencia, Spain Spanish National Cancer Research Centre (CNIO)
Abstract Show
Chimeric RNA transcripts are generated by different mechanisms, including pre-mRNA trans-splicing, chromosomal translocation and/or gene fusion, and it was recently shown that at least some chimeric transcripts may be translated into functional chimeric proteins. To gain a better understanding of the design principles behind the production of chimeric proteins, we have analyzed 7,424 chimeric RNAs from humans. We focused on the specific domains present in these proteins, comparing their permutations with those of known human proteins. We found that chimeras contain complete protein domains more often than in random datasets and specifically, that eight different types of domains are over represented among all chimeras, as well as in those chimeras confirmed by RNA-seq experiments. Moreover, we discovered that some chimeras potentially encode proteins with novel and unique combinations of such domains. Given the prevalence of complete protein domains observed in chimeras, we predict that putative chimeras that lack activation domains may actively compete with their parental proteins, thereby exerting a dominant negative effect. In more general terms, the generation of chimeric transcripts produces a combinatorial increase in the number of protein products available, which may disturb the function of parental genes and influence their protein-protein interaction network.
TOPPP50 (PT) - Xenome - A Tool for Classifying Reads from Xenograft Samples
Room: 104ADate: Monday, July 16Author(s):
Thomas Conway, NICTA, Australia
Jeremy Wazny, NICTA
Andrew Bromage, NICTA
Martin Tymms, Monash Institute for Medical Research
Dhanya Sooraj, Monash Institute for Medical Research
Elizabeth Williams, Monash Institute for Medical Research
Bryan Beresford-Smith, NICTA
Session Chair: Burkhard Rost
Abstract Show
Motivation: Shotgun sequence read data derived from xenograft
material contains a mixture of reads arising from the host and reads
arising from the graft. Classifying the read mixture to separate the two
allows for more precise analysis to be performed.
Results: We present a technique, with an associated tool Xenome,
which performs fast, accurate and specific classification of xenograft
derived sequence read data. We have evaluated it on RNA-Seq data
from human, mouse and human-in-mouse xenograft data sets.
Availability: Xenome is available for non-commercial use from
http://www.nicta.com.au/bioinformatics
TOPPP51 (PT) - Fast alignment of fragmentation trees
Room: 104BDate: Monday, July 16Author(s):
Franziska Hufsky, Friedrich-Schiller-University Jena, Germany
Kai Dührkop, Friedrich-Schiller-University Jena
Florian Rasche, Friedrich-Schiller-University Jena
Markus Chimani, Friedrich-Schiller-University Jena
Sebastian Böcker, Friedrich-Schiller-University Jena
Session Chair: Reinhard Schneider
Abstract Show
Mass spectrometry allows sensitive, automated and high- throughput analysis of small molecules such as metabolites. One major bottleneck in metabolomics is the identification of "unknown" small molecules not in any database. Recently, fragmentation tree alignments have been introduced for the automated comparison of the fragmentation patterns of small molecules. Fragmentation pat- tern similarities are strongly correlated with the chemical similarity of molecules, and allow us to cluster compounds based solely on their fragmentation patterns.
Aligning fragmentation trees is computationally hard. Nevertheless, we present three exact algorithms for the problem: A dynamic pro- gramming (DP) algorithm, a sparse variant of the DP, and an Integer Linear Program (ILP). Evaluation of our methods on three different datasets showed that thousands of alignments can be computed in a matter of minutes using DP, even for "challenging" instances. Run- ning times of the sparse DP were an order of magnitude better than for the classical DP. The ILP was clearly outperformed by both DP approaches. We also found that for both DP algorithms, computing the 1 % slowest alignments required as much time as computing the 99 % fastest.
TOPPP52 (PT) - Dissect: Detection and Characterization of Novel Structural Alterations in Transcribed Sequences
Room: 104CDate: Monday, July 16Author(s):
Deniz Yorukoglu, Massachusetts Institute of Technology, United States
Faraz Hach, Simon Fraser University
Lucas Swanson, Simon Fraser University
Colin C. Collins, Vancouver Prostate Centre
Inanc Birol, Genome Sciences Centre
S. Cenk Sahinalp, Simon Fraser University
Session Chair: Hagit Shatkay
Abstract Show
Motivation: Computational identification of genomic structural variants via high throughput sequencing is an important problem for which a number of highly sophisticated solutions have been developed recently. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention.
In this paper, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model; a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect, which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs.
TOPPP53 (PT) - MoRFpred, a computational tool for sequence-based prediction and characterization of disorder-to-order transitioning binding sites in proteins
Room: Grand BallroomDate: Tuesday, July 17Author(s):
Fatemeh Miri Disfani, University of Alberta, Canada
Wei-Lun Hsu, Indiana University
Marcin J. Mizianty, University of Alberta
Christopher J. Oldfield, Indiana University
Bin Xue, University of South Florida
A. Keith Dunker, Indiana University
Vladimir N. Uversky, University of South Florida
Lukasz Kurgan, University of Alberta
Session Chair: Nir Ben-Tal
Abstract Show
Motivation: Molecular Recognition Feature (MoRF) regions are disordered binding sites that become structured upon binding. MoRFs are implicated in important biological processes, including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains.
Results: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (alpha, beta, coil, and complex). We develop a comprehensive dataset of annotated MoRFs and use it to build and empirically compare our method. MoRFpred is based on a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary pro-files, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility, and B-factors. Empirical evaluation shows that MoRFpred statistically significantly outperforms existing predictors, alpha-MoRF-Pred and ANCHOR, by 0.07 in AUC and 10% in success rate. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We present case studies to analyze these putative MoRFs. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues.
Availability: http://biomine.ece.ualberta.ca/MoRFpred/
Supplementary information:
http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf
Contact: lkurgan@ece.ualberta.ca
TOPPP54 (HT) - Uncovering Ancient Networks from Present-Day Interactions
Room: 104ADate: Tuesday, July 17Author(s):
Carl Kingsford, University of Maryland, College Park, us
Saket Navlakha, Carnegie Mellon University, United States
Rob Patro, University of Maryland, College Park, United States
Emre Sefer, University of Maryland, College Park, United States
Justin Malin, University of Maryland, College Park, United States
Guillaume Marçais, University of Maryland, College Park, United States
Session Chair: Alex Bateman
TOP
PP55 (HT) - An effective statistical evaluation of ChIPseq dataset similarity
Room: 104BDate: Tuesday, July 17Author(s):
Maria Chikina, Mount Sinai Medical School, us
Olga G. Troyanskaya, Princeton University, United States
Session Chair: Terry Gaasterland
TOP
PP56 (PT) - Extending ontologies by finding siblings using set expansion techniques
Room: 104CDate: Tuesday, July 17Author(s):
Götz Fabian, Technische Universität Dresden, Germany
Thomas Wächter, Technische Universität Dresden
Michael Schroeder, Technische Universität Dresden
Session Chair: Michal Linial
Abstract Show
Motivation: Ontologies are an everyday tool in biomedicine to capture and represent knowledge. However, many ontologies lack a high degree of coverage in their domain and need to improve their overall quality and maturity. Automatically extending sets of existing terms will enable ontology engineers to systematically improve text- based ontologies level by level.
Results: We developed an approach to extend ontologies by discovering new terms which are in a sibling relationship to existing terms of an ontology. For this purpose, we combined two approaches which retrieve new terms from the web. The first approach extracts siblings by exploiting the structure of HTML documents, whereas the second approach uses text mining techniques to extract siblings from unstructured text. Our evaluation against MeSH shows that our method for sibling discovery is able to suggest first-class ontology terms and can be used as an initial step towards assessing the completeness of ontologies. The evaluation yields a recall of 80% at a precision of 61% where the two independent approaches are complementing each other. For MeSH in particular, we show that it can be considered complete in its medical focus area. We integrated the work into DOG4DAG, an ontology generation plugin for the editors OBO-Edit and Protégé, making it the first plugin that supports sibling discovery on-the-fly.
Availability: Sibling discovery for ontology is available as part of DOG4DAG (www.biotec.tu-dresden.de/research/schroeder/dog4dag) for both Proteégé 4.1 and OBO-Edit 2.1.
TOPPP57 (PT) - Recognition Models to Predict DNA-binding Specificities of Homeodomain Proteins
Room: Grand BallroomDate: Tuesday, July 17Author(s):
Ryan Christensen, Washington University School of Medicine, United States
Metewo Selase Enuameh, University of Massachusetts Medical School
Marcus B. Noyes, University of Massachusetts Medical School
Michael H. Brodsky, University of Massachusetts Medical School
Scot A. Wolfe, University of Massachusetts Medical School
Gary D. Stormo, Washington University School of Medicine
Session Chair: Nir Ben-Tal
Abstract Show
Recognition models for protein-DNA interactions, which allow the prediction of specificity for a DNA-binding domain based only on its sequence or the alteration of specificity through rational design, have long been a goal of computational biology. There has been some progress in constructing useful models, especially for C2H2 zinc finger proteins, but it remains a challenging problem with ample room for improvement. For most families of transcription factors the best available methods utilize k-nearest neighbor algorithms to make specificity predictions based on the average of the specificities of the k most similar proteins with defined specificities. Homeodomain proteins are the second most abundant family of transcription factors, after zinc fingers, in most metazoan genomes, and as a consequence an effective recognition model for this family would facilitate predictive models of many transcriptional regulatory networks within these genomes. Using extensive experimental data, we have tested several machine learning approaches and find that both support vector machines and random forests can produce recognition models for homeodomain proteins that are significant improvements over k-nearest neighbor based methods. Cross-validation analyses show that the resulting models are capable of predicting specificities with high accuracy. We have produced a web-based prediction tool, PreMoTF (Predicted Motifs for Transcription Factors) (http://stormo.wustl.edu/PreMoTF), for predicting PFMs from protein sequence using a random forest based model.
TOPPP58 (HT) - A three-dimensional map of protein networks within and between species
Room: 104ADate: Tuesday, July 17Author(s):
Yu Xia, Boston University, us
Eric Franzosa, Boston University, United States
Session Chair: Alex Bateman
TOP
PP59 (PT) - DELISHUS: An Efficient and Exact Algorithm for Genome-Wide Detection of Deletion Polymorphism in Autism
Room: 104BDate: Tuesday, July 17Author(s):
Derek Aguiar, Brown University, United States
Bjarni Halldorsson, Reykjavik University
Eric Morrow, Brown University
Sorin Istrail, Brown University
Session Chair: Terry Gaasterland
Abstract Show
The understanding of the genetic determinants of complex disease is undergoing a paradigm shift. Genetic heterogeneity of rare mutations with deleterious effects is more commonly being viewed as a major component of disease. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A substantial portion of autism appears to be correlated with copy number variation which is not directly probed by single nucleotide polymorphism (SNP) array technologies. Identifying the genetic heterogeneity of small deletions remains a major unresolved computational problem due, in part, to the inability of algorithms to detect them.
In this paper we present an algorithmic framework, which we term DELISHUS, that implements three highly efficient algorithms for inferring genomic deletions of all sizes and frequencies in SNP array data. We implement a polynomial-time backtracking algorithm -- that finishes on a 1 billion entry genome-wide association study (GWAS) SNP matrix in a few minutes -- to compute all potential deletions in a dataset. Given a set of called deletions, we also give a polynomial time algorithm for detecting regions that contain multiple recurrent deletions. Finally, we give an algorithm for detecting de novo deletions. Because our algorithms consider all individuals in the sample at once, they achieve significantly lower false positive rates and higher power when compared to previously published single individual algorithms. Our method may be used to identify the deletion spectrum for GWAS where deletion polymorphism was previously not analyzed.
DELISHUS is available at http://www.brown.edu/Research/Istrail_Lab/
TOPPP60 (PT) - Ranking of Multidimensional Drug Profiling Data by Fractional Adjusted Bi-Partitional Scores
Room: 104CDate: Tuesday, July 17
Author(s):
Dorit Hochbaum, University of California at Berkeley, United States
Chun-Nan Hsu, University of Southern California, Marina del Rey
Yan T. Yang, University of California at Berkeley
Session Chair: Michal Linial
Abstract Show
Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut – normalized cut prime (FABS-NC′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.
Results: We compare the performance of FABS-NC′ to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC′ also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC′ consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC′: In some cases FABSNC ′ produces over half correctly predicted ranking experiment trials than FABS-SVM.
Availablility: The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.
PP61 (PT) - TMBMODEL: Toward 3D modeling of transmembrane beta barrel proteins based on z-coordinate and topology prediction
Room: Grand BallroomDate: Tuesday, July 17Author(s):
Sikander Hayat, Stockholm University, Sweden
Arne Elofsson, Stockholm University
Session Chair: Nir Ben-Tal
Abstract Show
Motivation: Two types of transmembrane proteins exist, alpha-helical membrane proteins and transmembrane beta-barrels. The later type exists in the outer membrane of gram-negative bacteria and in chloroplast and mitochondria where they play a major role in the translocation machinery. Here, we aim to build three-dimensional models for transmembrane beta-barrels based on a large set of predicted topologies used to generate alternative three-dimensional models.Thereafter, the predicted Z-coordinate, i.e. the distance of a residue from the membrane center, is used to identify the best model.
Results: We present TMBMODEL; a method for generating three-dimensional models based on predicted topologies. TMBMODEL employs theoretic principles from known structures to construct a model for a barrel of a given transmembrane beta-barrel sequence. Firstly, different topologies are obtained from running the BOCTOPUS topology predictor and then three-dimensional models are constructed for different shear numbers. The best model is then selected based on a novel Z-coordinate predictor. Based on a leave-one-out cross-validation, the Z-coordinate predictor predicts 74% residues within 2 Å on a non-redundant dataset of 36 transmembrane beta-barrels. The average error and correctly identified membrane residues is 1.61 Å and 71%, respectively. TMBMODEL chose the correct topology for 75% proteins in the data set, which is a slight improvement over BOCTOPUS. More importantly TMBMODEL provides a C-alpha template for more detailed structural analysis. The average RMSD for this template is 7.24 Å.
Availability: TMBMODEL is freely available as a web-server at: http://tmbmodel.cbr.su.se/. The data sets used for training and evaluations are also available from this site.
Contact: arne@bioinfo.se
Abbreviations: TMB, transmembrane beta-barrel protein; HMM, Hidden
Markov Model; SVM, support vector machine.
PP62 (HT) - Network-Based Prediction and Analysis of HIV Dependency Factors
Room: 104ADate: Tuesday, July 17Author(s):
T. Murali, Virginia Tech, us
Matthew Dyer, Applied Biosystems, United States
David Badger, Virginia Tech, United States
Brett Tyler, Virginia Tech, United States
Michael Katze, University of Washington, United States
Session Chair: Alex Bateman
TOP
PP63 (PT) - SEQuel: Improving the Accuracy of Genome Assemblies
Room: 104BDate: Tuesday, July 17Author(s):
Roy Ronen, University of California, San Diego, United States
Christina Boucher, University of California, San Diego
Hamidreza Chitsaz, Wayne State University
Pavel Pevzner, University of California, San Diego
Session Chair: Terry Gaasterland
Abstract Show
Motivation: Assemblies of next generation sequencing data, while
accurate, still contain a substantial number of errors that need to be
corrected after the assembly process. We develop SEQuel, a tool
that corrects errors (i.e., insertions, deletions, and substitution errors)
in the assembled contigs. Fundamental to the algorithm behind SEQuel
is the positional de Bruijn graph, a graph structure that models
k-mers within reads while incorporating the approximate positions of
reads into the model.
Results: SEQuel reduced the number of small insertions and
deletions in the assemblies of standard multi-cell E. coli data by
almost half, and corrected between 30% and 94% of the substitution
errors. Further, we show SEQuel is imperative to improving
single-cell assembly, which is inherently more challenging due to
higher error rates and non-uniform coverage; over half of the
small indels, and substitution errors in the single-cell assemblies
were corrected. We apply SEQuel to the recently-assembled
Deltaproteobacterium SAR324 genome, which is the first bacterial
genome with a comprehensive single-cell genome assembly, and
make over 800 changes (insertions, deletions and substitutions) to
refine this assembly.
Availability: SEQuel can be used as a post-processing step
in combination with any NGS assembler and is freely available at
http://bix.ucsd.edu/SEQuel/.
TOPPP64 (PT) - DACTAL: divide-and-conquer trees (almost) without alignments
Room: 104CDate: Tuesday, July 17
Author(s):
Serita Nelesen, Calvin College, United States
Kevin Liu, Rice University
Li-San Wang, University of Pennsylvania
C. Randal Linder, University of Texas at Austin
Tandy Warnow, University of Texas at Austin
Session Chair: Michal Linial
Abstract Show
We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset. DACTAL combines iteration with a novel divide-and-conquer approach, so that each iteration begins with a tree produced in the prior iteration, decomposes the taxon set into overlapping subsets, estimates trees on each subset, and then combines the smaller trees into a tree on the full taxon set using a new supertree method. We prove that DACTAL is guaranteed to produce the true tree under certain conditions. We compare DACTAL to SATe and maximum likelihood trees on estimated alignments using simulated and real datasets with 1000 to 27,643 taxa. Our studies show that DACTAL dramatically outperforms two-phase methods with respect to tree accuracy. The comparison to SAT\{e} shows that both have the same tree accuracy, but that DACTAL achieves this accuracy in a fraction of the time. Furthermore, DACTAL can analyze larger datasets than SAT\'{e}, including a dataset with almost 28,000 sequences.
DACTAL source code is available at www.cs.utexas.edu/users/phylo/software/dactal
PP65 (PT) - Minimum Message Length Inference of Secondary Structure from Protein Coordinate Data.
Room: Grand BallroomDate: Tuesday, July 17Author(s):
Arun Konagurthu, Monash University, Australia
Arthur Lesk, Pennsylvania State University
Lloyd Allison, Monash University
Session Chair: Nir Ben-Tal
Abstract Show
Motivation: Secondary structure underpins the folding pattern and architecture of most proteins. Accurate assignment of the secondary structure elements is therefore an important problem. Although many approximate solutions of the secondary structure assignment problem exist, the statement of the problem has resisted a consistent and mathematically rigorous definition. A variety of comparative studies have highlighted major disagreements in the way the available methods define and assign secondary structure to coordinate data. Results: We report a new method to infer secondary structure based on the Bayesian method of Minimum Message Length (MML) inference. It treats assignments of secondary structure as hypotheses that explain the given coordinate data. The method seeks to maximise the joint probability of a hypothesis and the data. There is a natural null hypothesis and any assignment that cannot better it is unacceptable. We developed a program SST based on this approach and compared it to popular programs such as DSSP and STRIDE amongst others. Our evaluation suggests that SST gives reliable assignments even on low resolution structures.
TOPPP66 (PT)
- Weighted Pooling - Practical and Cost Effective Techniques for Pooled High Throughput Sequencing
Room: 104BDate: Tuesday, July 17Author(s):
David Golan, Tel Aviv University , Israel
Saharon Rosset, Tel Aviv University
Yaniv Erlich, Whitehead Institute
Session Chair: Terry Gaasterland
Abstract Show
Motivation: Despite the rapid decline in sequencing costs, sequencing large
cohorts of individuals is still prohibitively expensive. Recently, several
sophisticated pooling designs were suggested that can identify carriers
of rare alleles in large cohorts with a significantly smaller number
of pools, thus dramatically reducing the cost of such large scale
sequencing projects (Erlich et al. 2009). These approaches use combinatorial pooling designs where each individual is either present or absent from a pool. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers.
Results: We show that one can gain further efficiency and cost reduction by
using "weighted" designs, in which different individuals donate
different amounts of DNA to the pools. Intuitively, in this situation
the number of mutant reads in a pool does not only indicate the number
of carriers, but also their identity. We describe and study a powerful example of such weighted designs, using non-overlapping pools. We demonstrate that this approach is not only easier to implement and analyze but is also competitive in terms of accuracy with combinatorial designs when identifying rare variants, and is superior when sequencing common variants.
We then discuss how weighting can be incorporated into existing combinatorial designs to increase their accuracy and demonstrate the resulting improvement using simulations. Finally, we argue that weighted designs have enough power to facilitate detection of common alleles, so they can be used as a cornerstone of whole-exome sequencing projects.
TOPPP67 (HT) - Predicting relapse in medulloblastoma patients by integrating evidence from clinical and genomic features
Room: Grand BallroomDate: Tuesday, July 17
Author(s):
Pablo Tamayo, Broad Institute, us
Yoon-Jae Cho, Stanford University, United States
Aviad Tscherniak Tsherniak, Broad INstitute, United States
Marcel Kool, Amsterdam Medical Center, Netherlands
Scott Pomeroy, Children's Hospital, United States
Jill Mesirov, Broad Institute, United States
Session Chair: Serafim Batzoglou
TOP
PP68 (HT)
- Chemical-Protein Interactome and its Application in Personalized Medicine and Drug Repositioning
Room: 104ADate: Tuesday, July 17Author(s):
Lun Yang, GlaxoSmithKline, us
Lin He, Shanghai Jiao Tong U, China
Kejian Wang, Shanghai Jiao Tong U, China
Heng Luo, Shanghai Jiao Tong U, China
Session Chair: Terry Gaasterland
TOP
PP69 (HT) - Large-scale DNA editing of retrotransposons accelerates mammalian genome evolution.
Room: 104BDate: Tuesday, July 17
Author(s):
Erez Levanon, Bar-Ilan University, il
Shai Carmi, Columbia University., United States
George Church, Harvard Medical School , United States
Session Chair: Jaques Reifman
TOP
PP70 (HT) - Interpreting human disease associations using comparative genomic and epigenomic signatures
Room: Grand BallroomDate: Tuesday, July 17
Author(s):
Manolis Kellis, MIT, us
Luke Ward, MIT, United States
29-mammals Consortium, Broad Institute, United States
Session Chair: Serafim Batzoglou
TOP
PP71 (HT) - A data integration approach illustrates evolutionary mechanisms of ligand selectivity between related protein targets
Room: 104ADate: Tuesday, July 17Author(s):
Felix Kruger, European Bioinformatics Institute, uk
John P Overington, European Bioinformatics Institute, United Kingdom
Session Chair: Terry Gaasterland
TOP
PP72 (HT) - Domain architecture conservation in orthologs
Room: 104BDate: Tuesday, July 17
Author(s):
Erik Sonnhammer, Stockholm University, se
Kristoffer Forslund, SBC, Stockholm University, Sweden
Session Chair: Jaques Reifman
TOP
PP73 (PT) - Statistical model-based testing to evaluate the recurrence of genomic aberrations
Room: Grand BallroomDate: Tuesday, July 17
Author(s):
Atsushi Niida, University of Tokyo, Japan
Seiya Imoto, University of Tokyo
Teppei Shimamura, University of Tokyo
Satoru Miyano, University of Tokyo
Session Chair: Serafim Batzoglou
Abstract Show
Motivation: In cancer genomes, chromosomal regions harboring
cancer genes are often subjected to genomic aberrations like copy
number alteration and loss of heterozygosity (LOH). Given this,
finding recurrent genomic aberrations is considered an apt approach
for screening cancer genes. Although several permutation-based
tests have been proposed for this purpose, none of them are
designed to find recurrent aberrations from the genomic data set
without paired normal sample controls. Their application to unpaired
genomic data may lead to false discoveries, because they retrieve
pseudo-aberrations that exist in normal genomes as polymorphisms.
Results: We develop a new parametric method named parametric
aberration recurrence test (PART) to test for the recurrence of
genomic aberrations. The introduction of Poisson-binomial statistics
allow us to compute small p-values more efficiently and precisely than
the previously proposed permutation-based approach. Moreover, we
extended PART to cover unpaired data (PART-up) so that there is
a statistical basis for analyzing unpaired genomic data. PART-up
utilizes information from unpaired normal sample controls to remove
pseudo-aberrations in unpaired genomic data. Using PART-up, we
successfully predict recurrent genomic aberrations in cancer cell
line samples whose paired normal sample controls are unavailable.
This paper thus proposes a powerful statistical framework for the
identification of driver aberrations, which would be applicable to ever-increasing amounts of cancer genomic data seen in the era of next
generation sequencing.
TOPPP74 (HT) - Materiomics: instructing cell fate using topographical biomaterials
Room: 104ADate: Tuesday, July 17Author(s):
Marc Hulsman, TU Delft, nl
Hemant Unadkat, University of Twente, Netherlands
Kamiel Cornelissen, University of Twente, Netherlands
Bernke Papenburg, University of Twente, Netherlands
Roman Truckenmüller, University of Twente, Netherlands
Gerhard Post, University of Twente, Netherlands
Marc Uetz, University of Twente, Netherlands
Marcel Reinders, Delft University of Technology, Netherlands
Dimitrios Stamatialis, University of Twente, Netherlands
Clemens van Blitterswijk, University of Twente, Netherlands
Jan de Boer, University of Twente, Netherlands
Session Chair: Terry Gaasterland
TOP
PP75 (HT) - Viral-host coevolution: Playing 'seek and hide'
Room: 104BDate: Tuesday, July 17
Author(s):
Michal Linial, The Hebrew University of Jerusalem, il
Nadav Rappoport, The Hebrew University of Jerusalem, Israel
Session Chair: Jaques Reifman
TOP
PP76 (PT) - Data-Driven Integration Of Epidemiological And Toxicological Data To Select Candidate Interacting Genes And Environmental Factors In Association With Disease
Room: Grand BallroomDate: Tuesday, July 17
Author(s):
Chirag Patel, Stanford University, United States
Rong Chen, Stanford University
Atul Butte, Stanford University
Session Chair: Serafim Batzoglou
Abstract Show
Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies either investigate either the genetics or the environment in context of disease and there are a few that study their possible interaction. One key challenge in documenting interactions between genes and environment includes choosing which of each to test jointly. Here, we attempt to address this challenge through a data-driven integration of epidemiological and toxicological studies. Specifically, we derive lists of candidate interacting genetic and environmental factors by integrating findings from genome-wide and environment-wide association studies (GWAS and EWAS). Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for Type 2 Diabetes.
TOPPP77 (PT) - Identifying Disease Sensitive and Quantitative Trait Relevant Biomarkers from Heterogeneous Imaging Genetics Data via Sparse Multi-Modal Multi-Task Learning
Room: 104ADate: Tuesday, July 17Author(s):
Hua Wang, University of Texas at Arlington, United States
Feiping Nie, University of Texas at Arlington
Heng Huang, University of Texas at Arlington
Shannon Leigh Risacher, Indiana University
Andrew Saykin, Indiana University School of Medicine
Li Shen, Indiana University School of Medicine
Session Chair: Terry Gaasterland
Abstract Show
Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores, and disease status, and ignore the important underlying interacting relationships between these units.
Results: To overcome this limitation, in this paper, we propose a new sparse multi-modal multi-task learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: 1) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; 2) introducing combined structured sparsity regularizations into multimodal multi-task learning to integrate heterogenous imaging genetics data and identify multi-modal biomarkers; 3) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer’s Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multi-modal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.
TOPPP78 (PT) - Efficient Algorithms for the Reconciliation Problem with Gene Duplication, Horizontal Transfer, and Loss
Room: 104BDate: Tuesday, July 17
Author(s):
Mukul S. Bansal, Massachusetts Institute of Technology, United States
Eric J. Alm, Massachusetts Institute of Technology
Manolis Kellis, Massachusetts Institute of Technology
Session Chair: Jaques Reifman
Abstract Show
Motivation: Gene family evolution is driven by evolutionary events like speciation, gene duplication, horizontal gene transfer, and gene loss, and inferring these events in the evolutionary history of a given gene family is a fundamental problem in comparative and evolutionary genomics with numerous important applications. Solving this problem requires the use of a reconciliation framework, where the input consists of a gene family phylogeny and the corresponding species phylogeny, and the goal is to reconcile the two by postulating speciation, gene duplication, horizontal gene transfer, and gene loss events. This reconciliation problem is referred to as Duplication-Transfer-Loss (DTL) reconciliation and has been extensively studied in the literature. Yet, even the fastest existing algorithms for DTL-reconciliation are too slow for reconciling large gene families and for use in more sophisticated applications such as gene tree or species tree reconstruction.
Results: We present two new algorithms for the DTL-reconciliation problem that are dramatically faster than existing algorithms, both asymptotically and in practice. We also extend the standard DTL-reconciliation model by considering distance-dependent transfer costs, that allow for more accurate reconciliation, and give an efficient algorithm for DTL-reconciliation under this extended model. We implemented our new algorithms and demonstrate up to 100,000-fold speed-up over existing methods, using both simulated and biological datasets. This dramatic improvement makes it possible to use DTL-reconciliation for performing rigorous evolutionary analyses of large gene families, and enables its use in advanced reconciliation-based gene and species tree reconstruction methods.
TOPSS2_partB - Reconstructing the Regulatory Network of TB: Deconstruction of the Hypoxic Response
Room: 202BCDate: Sunday, July 15Author(s):
Elham Azizi, Boston University, United States
Abstract Show
We have generated the first genome scale model of the in M. tuberculosis regulatory network and combined this network with the first comprehensive profiling of mRNA, proteins, metabolites and lipids in MTB during hypoxia and re-aeration. We have developed a high-throughput system based on ChIP-Seq for comprehensively mapping regulatory binding, and integrated this with expression data from the induction of the same factors. Our method allows us to map DNA binding of all MTB regulators in a consistent and comparable manner independent of regulatory function. Using this method we have reconstructed a regulatory network model based on over 50 transcriptions factors. The network doubles the number of regulators whose interactions have been studied in MTB, discovers thousands of interactions and assigns functions to a substantial number, suggests many more potentially functional interactions for even well-studied regulators, and displays predictive power for gene expression.
The network model also reveals a direct and interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of known immunomodulatory lipids, and protein degradation. Consistent with this, we observe substantial alterations in lipid, amino acid, and protein content in response to oxygen availability. The regulator network provides insight into the transcription factors underlying these changes. Using our regulatory network data – generated under independent normoxic conditions - we are able to generate models of steady state gene expression that allow us to predict MTB gene expression during hypoxia and re-aeration.
TOPSS2_partD - Systems Biology of Infectious Disease
Room: 202BCDate: Sunday, July 15Author(s):
Jason McDermott, Pacific Northwest National Laboratory, United States
Abstract Show
The study of infectious disease and the complex interplay between pathogens and their hosts has benefitted greatly from the ability to generate many different high-throughput measurements of systems, including transcriptomics, proteomics, and metabolomics. Ways to represent, interpret, and model such multimodal datasets allow improved understanding of the host-pathogen relationship at a systems biology level. We present recent results from systems biology studies of bacterial enteropathogens, Salmonella Typhimurium and Yersinia pestis, as well as respiratory viruses, influenza H5N1 and SARS coronavirus, interacting with their hosts. We will describe the use of network-based approaches to interpretation of high-throughput data and prediction of important components of the system, including experimental validation of some of these predictions. We will also describe how predictive modeling approaches can be used to model important aspects of the interaction and provide predictions of control points for pathogenesis and host response. Finally, we will discuss critical gaps that exist in the systems biology study of infectious diseases and future directions to address those gaps.
TOPSS7_partA - Assessing the contribution of scientists to Wikipedia for Pfam and Rfam annotation
Room: 104CDate: Tuesday, July 17Author(s):
Alex Bateman, Wellcome Trust Sanger Institute, United Kingdom
Abstract Show
In this presentation I will show the latest survey of the scientific community's engagement with Wikipedia and its relevance to the annotation in Pfam and Rfam. Major challenges remain in: (1) educating experts in the field that Wikipedia contributions are a valuable communication tool; (2) giving non-technical scientists the confidence and knowledge of how to edit Wikipedia content.
TOPSS7_partB - WikiPathways and How to Change the World (or at least your small corner of the world)
Room: 104CDate: Tuesday, July 17Author(s):
Alexander Pico, Gladstone Institutes, United States
Abstract Show
WikiPathways is a collaborative platform for collecting, curating and distributing biological pathway knowledge in the research community. We started WikiPathways with almost a decade of experience archiving pathway models as a conventional resource maintained by a small internal team of experts. Switching to a community curation approach has dramatically increased the size, quality and relevance of our content. Increased relevance is a particularly unique advantage of ‘community intelligence’ efforts that directly engage researchers in real-time. More and more, we are finding research communities eager to participate in data and knowledge repositories that utilize their contributions directly and transparently. Over the past 4 years, WikiPathways has grown from 100 registered users to over 2000, with a steadily increasing percentage making edits and contributing new content. The number of visits has doubled in the last year to over 10,000 per month. In this special session, we will present the lessons we have gleaned from launching and developing WikiPathways as a ‘community intelligence’ effort: how to set milestones for early success, how to utilize open source code and culture, how to tap into already established communities and resources, how to build data mining and analytical tools and services around your content, how to make use of new models of data sharing and publishing.
TOPSS7_partC - The Gene Wiki: Crowdsourcing the annotation of human gene function
Room: 104CDate: Tuesday, July 17Author(s):
Andrew Su, The Scripps Research Institute, United States
Abstract Show
Comprehensively annotating the function of human genes is a formidable challenge for the biomedical research community. The goal of the Gene Wiki project is to create a continuously updated, community-reviewed and collaboratively-written review article for every human gene. The Gene Wiki currently takes the form of 10,000 articles in the online encyclopedia Wikipedia. This collection of articles is viewed over 50 million times and edited over 15,000 times per year. In this talk, we will describe our efforts to create a critical mass of users, to mine structured gene annotations from Gene Wiki text, and to integrate these data in bioinformatics analyses.
TOPSS7_partD - Distributed Community Intelligence through the Scientific Discovery Game Foldit
Room: 104CDate: Tuesday, July 17Author(s):
Firas Khatib, University of Washington, United States
Abstract Show
Foldit is a graphical user interface representation of the Rosetta algorithm where players manipulate protein structures with the corresponding Rosetta energy shown in real time as their score. By leveraging human puzzle solving, pattern-recognition, and 3D spatial reasoning, humans are able to outperform many state of the art prediction methods. Foldit players have generated models accurate enough for successful molecular replacement and subsequent structure determination of a monomeric retroviral protease, despite not being given any experimental data. Foldit players have also been provided tools to encode their folding strategies, and within seven months one of these player-developed folding algorithms outperformed a previously published algorithm. Most recently, players were challenged to remodel the backbone of a computationally designed bimolecular Diels-Alderase to enable additional interactions with substrates. Several iterations of design and characterization generated a 24 residue helix-turn-helix motif, including a 13 residue insertion, that increased enzyme activity over 18-fold. X-ray crystallography showed that the large insertion adopts a helix-turn-helix structure positioned as in the Foldit model. The ability of an online gaming community to successfully guide large-scale protein structure prediction and design problems suggests that human creativity can extend down to molecular scale when given the appropriate tools.
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