Accepted Posters
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Category P - 'Other'
Short Abstract: With prospects of re-engineering organisms to meet our increasing needs
for energy, cleanup of toxic waste sites, etc. as well as motivated by
understanding and synthesizing life itself, synthetic biology is an emerging area that involves the synthesis of artificial metabolic and signaling pathways, the assembly of chimeric genomes, and the engineered design of RNA, DNA and proteins to meet specified applications.
Thermoswitches, aka biological thermometers (RNATs) are RNA sequences which regulate gene expression through temperature-induced conformation change. Natural examples of thermoswitches are ROSE (repression of heat shock gene expression) elements, which trigger the transcription of protein chaperones by an RNA conformation change at high temperatures.
Here, we describe a new algorithm, RNAiFold2T, for the design of thermoswitches. Given two target structures A, B and two temperatures T1, T2, RNAiFold2T is an inverse folding based on Constraint Programming that determines those sequences which fold into structure A at temperature T1, and into structure B at temperature T2. RNAiFold2T is the only method capable of a complete solution of the 2-temperature inverse folding problem, which also includes the option of employing a faster search strategy using Large Neighbourhood Search if the goal is to return a single solution sequence.
RNAiFold2T is presented as a promising tool for the analysis and
design of synthetic RNA thermoswitches, as well as for the
evaluation of possible optimization measures used in the design of
thermoswitches. In particular, results lead us to speculate about the importance of folding kinetics in the design of natural thermoswitches.
Short Abstract: COSMIC (http://cancer.sanger.ac.uk) is the world’s largest resource of somatic mutations in human cancer, curating information from a wide range of sources including extensive scientific literature, genome resequencing data portals (eg ICGC, TCGA) and the Cancer Genome Project at the Wellcome Trust Sanger Institute.
The website provides multiple innovative yet simple ways to investigate cancer mutations in diseases, genes and genomes, providing graphic summaries of cancer mutation data, including a dedicated Genome Browser (JBrowse), gene centric histograms, and whole genome summaries using Circos. The cancer browser specifically allows the exploration of tissues and diseases in combination to study its mutation distribution. The new improved custom search function scrutinises the database to find specified terms and weighting the output to highlight most significantly mutated entities emphasizing the highest value data. Many filters are also implemented to analyze the data and enable users to deeply explore mutation trends. Download is also supported directly from the website, either in TSV or CSV format, from most onscreen tabulations. There is a dedicated FTP server to provide easy access to the complete dataset, allowing its offline exploration.
COSMIC continues to grow rapidly, with the incorporation of large genomic datasets and the integration of new types of cancer mutation. Most recently we have added Copy Number annotations, and we are now adding Gene Expression details, to enhance the analysis of which mutations cause which disease, supporting the discovery of new clinically useful biomarkers.
Short Abstract: An intelligent figure search engine will not only assist biocuration and allow individual biomedical researcher to access figures more efficiently from full-text biomedical articles, but also is an important step towards automatic validations of genome-wide high-throughput predictions. With more and more full-text biomedical articles becoming open access (the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities, the Bethesda Statement on Open Access Publishing, and PubMed Central), we are developing a figure search system (available at http://figuresearch.askhermes.org) that
integrates natural language processing, image processing, machine learning, and user interfacing “biomedical Natural Language figure Processing” approaches for intelligent biomedical figure search (iBioFigureSearch). Our iBioFigureSearch associates each figure with text that describes the content of the figure, summarizes the associated text, ranks figures by their importance, and integrates both image and text for improved information retrieval. We have evaluated iBioFigureSearch by both intrinsic and task-driven extrinsic evaluation and found that iBioFigureSearch improves user-centered information seeking.
Short Abstract: Transcription of protein-coding genes by RNA polymerase II (Pol II) involves multiple modes of regulation. Pol II poising has been proposed to be important for regulating processes that need to be activated “on demand” however genome-wide regulatory role Pol II poising is yet to be fully delineated. As a step this direction, we analyzed Pol II poising in resting and activated B-cells. Here we refer poised Pol II as the accumulation of Pol II around promoter independently of the precise mechanism. We found that while Pol II poised genes largely functionally overlap between different B-cell states. In contrast non-poised but transcriptionally active genes are B-cell state specific where enriched among others in RNA processing, metabolic processes, DNA repair, and transport. The changes in transcription activity of B-cell states depend more on the changes of Pol II in the gene body the the ones of Poll II in the promoter regions. However, The changes in transcription activity are not correlated with changes posing index. Interestingly, we found that some immediate early genes are spontaneously induced and fully elongating in resting cells but poised in activated cells, suggesting that posing not only “prepares genes for sprint” as it has been suggested before, but also provides a mechanism for putting breaks on sprinting genes.
Short Abstract: The development of safe and effective gene carriers is an important challenge in the clinical implementation of nucleic acid - based therapies. Current applications include nanoparticles such as Dendrimers, which have gained prominence as efficient gene carriers due to their customizable structure, however, one of the issues that must be improved is how to modulate dendrimer - DNA interaction in order to promote the unpacking of this complex inside the cells, allowing the release of the DNA. Therefore, many functional groups have been evaluated to modify the dendrimer and improve their affinity to nucleic acids. To this end, we decided to employ bioinformatics strategy, by analyzing the Protein Data Bank, to understand how proteins and nucleic acids interact, with the goal of identifying new functional groups. Using this platform, we have designed a new dendrimer-based nanoparticle with its surface conjugated with two or more amino acidic groups, called Synthetic Protein Based on Dendrimers (SPBD). By adjusting the type of amino acids and charge distribution, we can modulate the nucleic-acid binding properties of synthetic proteins. To test the ability of SPBD to bind efficiently a nucleic acid, we performed Molecular Dynamics simulations to study parameters such as free energy of binding involved in the complex formation. Coarse Grained Molecular Simulations were also evaluated to understand molecular self-assembly of the dendrimers in aqueous solution. This new nanoparticle have been synthesized and preliminary validated experimentally.
Short Abstract: The past decade have seen an amazing exponential growth in the ability to generate genetic and biomolecular data on patients in a variety of disease contexts. However, with a few exceptions, these investments have failed to significantly improve prevention or treatment of many common human diseases. For example, the numbers of new drugs approved by the FDA has actually declined over this period.
A fundamental reason for this discrepancy between data generation and clinical improvement is the immature development of reproducible analytical techniques and workflows to meaningfully interpret these new data types. The difficulty of accessing, evaluating, and reusing data, analysis methods, or models of disease across multiple labs with complementary fields of expertise is a major barrier to the effective interpretation of genomic data today.
We present Synapse(www.synapse.org) as a free and open source informatics platform for open data-driven collaborative research tool for the scientific community. Synapse is built from the ground up for rich data sharing experience providing tools for data versioning, complete provenance tracking, data annotation, governance, data security citation management.
We will highlight a recent example of The Cancer Genome atlas Pan-cancer collaboration, leveraging Synapse platform to share and evolve data, results and methodologies. A total of 18 papers were published in nature journals in a short span of 10 months. In all over 250 collaborators spread across 30 institutions participated working on 60 different research projects using Synapse as a single point resource for sharing data and results.
Short Abstract: Most of the current knowledge about the crucial process of protein folding is based on in vitro investigation of isolated polypeptide chains, which typically consider the refolding of full length proteins previously denaturated by various chemical or thermal conditions. However, in vitro folding is likely to differ from in vivo, as in the latter proteins start to fold while they are still gradually emerging through the ribosomal exit tunnel.Excellent system to study impact of protein vectorial synthesis on protein structure and dynamics are protein C-terminal truncations. Specifically, carried in our group NMR and computational investigations of C-terminal truncations (delta4 and delta6) of an immunoglobulin fold – ddFLN protein, are allowing us to present, the energy landscape that emerges from these co-translational folding mimetics
In my work I provide atomistic details to this energy landscape by carrying out bias exchange metadynamics simulations with chemical shifts used as structural restrains. Using this approach I overcome, in large part, the two main limitations of molecular dynamics simulations in structural studies of proteins, firstly the inaccuracies in the use of force field alone and secondly the limitations inherent in sampling of conformational space.
My study presents a structural and dynamical characterization of the free energy landscape of this C-terminal truncation as well as changes in the landscape, while a protein is folding in the vectorial manner. Hence, it is providing insights into a better understanding of co-translational folding, which still represents a major open problem in molecular biology.
Short Abstract: Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modelling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Our method also enables us to pin-point the critical regions of stability on an interface and shows how such distributions change for different types of complexes. This paves the way for more intelligent computational-interface-design algorithms and provides new insight into the interpretation of destabilizing mutations involved in complex diseases.
Short Abstract: Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential expression. Evidence of differential expression is measured by the informative/noninformative (I/NI) value, which allows differentially expressed transcripts to be extracted at a desired specificity (significance level) or sensitivity (power). DEXUS performed excellently in identifying differentially expressed transcripts in data with unknown conditions. On 2400 simulated data sets, I/NI value thresholds of 0.025, 0.05 and 0.1 yielded average specificities of 92, 97 and 99% at sensitivities of 76, 61 and 38%, respectively. On real-world data sets, DEXUS was able to detect differentially expressed transcripts related to sex, species, tissue, structural variants or quantitative trait loci.
Short Abstract: Our presentation will start with explaining the motivation of the project, which is to obtain a better understanding of the molecular mechanisms behind the metastatic process of breast cancer.
Then we will introduce some key concepts regarding our methodology. We will give brief definitions about protein interfaces and structurally resolved protein protein interaction (PPI) networks, then we will picture what mapping a mutation on a protein interface corresponds to.
We will continue with describing our systems biology approach that combines PPIs, structural knowledge and genetic variations. We will explain how we built phenotype specific PPI networks, modeled protein-protein interfaces, integrated interface structure to PPI networks, mapped mutations on the structurally resolved PPI networks and located interactions that are possibly affected by the mutations.
After that we will discuss our findings and conclude with the future implications of our work.
Short Abstract: PANTHER-PSEP is a new software tool for predicting deleterious non-synonymous SNPs. PANTHER-PSEP uses a novel methodology to process and interpret homologous alignments called "evolutionary preservation": homologous proteins are used to reconstruct the likely sequences of ancestral proteins at nodes in a phylogenetic tree, and the history of each amino acid can be traced back in time from its current state to estimate how long that state has been preserved in its ancestors. Here we show that the longer a position in a current human protein has been preserved by tracing back to its direct ancestors, the more likely that a mutation at that site will have a deleterious effect, and that this apparently simple metric outperforms a conventional measure of evolutionary conservation in predicting deleterious variants in humans. The method is completely general, and can also be applied to genetic variants in all 81 other species in PANTHER.
Short Abstract: Genes with common profiles of the presence and absence in disparate genomes tend to function in the same pathway. By mapping all human genes into about 1000 clusters of genes with similar patterns of conservation across eukaryotic phylogeny, we determined that sets of genes associated with particular diseases have similar phylogenetic profiles. By focusing on those human phylogenetic gene clusters that significantly overlap some of the thousands of human gene sets defined by their coexpression or annotation to pathways or other molecular attributes, we reveal the evolutionary map that connects molecular pathways and human diseases. The other genes in the phylogenetic clusters enriched for particular known disease-genes or molecular pathways identify candidate genes for roles in those same disorders and pathways. Focusing on proteins coevolved with the microphthalmia-associated transcription factor(MITF), we identified the Notch pathway suppressor of hairless (RBP-Jk/SuH) transcription factor, and showed that RBP-Jk functions as an MITF cofactor.
Short Abstract: We will introduce Pathview, a novel powerful tool set that maps, integrates and renders biological data onto pathways. First, we will survey the existing tools and our motivation to develop Pathview.
We will then describe the overall design of Pathview, including functional modules, user interface and data flow. Three main features will be covered with real examples:
1) two graphic styles: KEGG view and Graphviz view;
2) strong support for data integration. Pathview works with a wide variety of biological data, molecular IDs, data attributes and formats, thousands of species;
3) easy to automate and integrate into functional analysis pipelines. For example, we will describe GAGE/Pathview workflows for RNA-Seq and microarray pathway analysis.
Pathview demonstrated great potentials and applications in a short time. It has been widely adopted by thousands of scientists and multiple popular analysis tools. In numerous recent or unpublished studies, Pathview accurately pinpoints disease causing pathways and reveals mechanistic changes.
Short Abstract: South America is the center of origin of the cultivated peanut and of more than 80 species of Arachis wild relatives. These wild species, differently to the cultivated species (Arachis hypogea L.), are diploids, and some of them harbor resistances to nematodes and fungi. The difference in ploidy and the genome size has hindered the characterization and introgression of wild alleles intro cultivated species. We used RNAseq technology and other NGS, to produce transcriptome profiles from resistant A. stenosperma challenged with the gall nematode M. arenaria, and A. duranensis under hydric stress. Analysis of differential expression between challenged and control plants showed a number of candidate genes of tolerance/resistance to these constraints.
Results from this analysis were organized on a CMS using Drupal (http://drupal.org), with a Tripal module (http://www.gmod.org/wiki/Tripal). Tripal is an important GMOD tool which allows the establishment of online genomic database.
Using Drupal/Tripal, the user can retrieve information from the created database such as clustering statistics, fasta sequences for each analysis, results for the KEGG orthology, InterPro and Blast search. The user also can access some other tools like blast search a viroblast implementation (http://indra.mullins.microbiol.washington.edu/viroblast/viroblast.php) and e-PCR both using the same local database.
The use of Drupal/Tripal is a good strategy to organize and distribute information integrating the existing data in one unique address. The use of tools like blast and e-PCR using local databases improves the quality of the results. The platform provides an important resource facilitating further studies for the scientific community working with Arachis
Short Abstract: microRNAs (miRNAs) cause changes in gene expression through repression of target mRNA and are highly dysregulated in cancer. Recent analysis of miRNA expression changes has focused on the effects of miRNA changes on computationally predicted target mRNAs. However, analysis of patient data and experimental datasets suggests that miRNAs can cause changes in mRNA expression through targetting intermediate signaling proteins and transcription factors. As such, we introduce an integrative approach that combines mRNA and miRNA expression data across cancer patient samples with protein interaction networks and transcriptional regulatory networks. We use a multi-commodity flow-based algorithm to identify miRNA-specific interaction networks that correlate with poor patient prognosis. This tool identifies the expanded role of miRNAs in cancer, enabling us to study their functional role. Furthermore, it highlights proteins and interactions shared by multiple miRNA interaction networks that play a role in cancer prognosis. Our initial success in breast cancer has led us to expand our approach, using resources such as TCGA and ENCODE, across publicly available cancer datasets to determine the role of miRNAs in distinct cancers through understanding the interaction networks they target.
Short Abstract: The overwhelming majority of the proteins function when they are part not only of binary interactions, but of multimolecular assemblies. Modeling protein assemblies is essential to figure out cellular mechanisms. Computational methods are essential to obtain the structures of protein assemblies. the capabilities of current procedures to construct multimolecular protein assemblies are limited. Integrative procedures mainly depend on the experimental data, and manual adjustment and curation are necessary. Ab initio docking procedures are computationally expensive; others are limited by considerations of symmetry. There is a need for a procedure that can construct homo-/hetero-complexes and symmetric/asymmetric complexes without the computational cost of ab initio docking, considers possible conformational changes, and is applicable to large-scale studies. This study aims to take steps toward addressing this need. Here, we exploit a template based protein interaction prediction tool, PRISM, to predict binary interactions, and use these predictions to construct protein assemblies.
Short Abstract: Structural similarity methods for proteins from NCBI MMDB can be used to identify similar protein 3D structures by geometric criteria and to identify homologs that cannot be detected by sequence comparison. One obstacle to wider adoption of structural similarity methods are the complexity of computations and the required CPU time. Here, we present a fast and efficient structural similarity method based on the spatial orientation of helices and strands, e.g. relative angle between two helices, the distance between strands and the count of helices, strands and supersecondary structure elements. This method does not require the superimposition of protein structures and relies on encoding information about geometry into 1D fingerprints instead. Once the fingerprints are calculated, chemoinformatics similarity and information retrieval methods can could be used for fast searches of similar structures. Here we used the commonly adopted Tanimoto score for neighbouring and implemented multithreaded elementary operations on fingerprints. We validated the method with our VAST structural neighbours and saw that the method is of practical importance for finding highly similar structures with orders of magnitude speedup compared to other structure-based methods. The proposed method can also serve as a filter for other CPU intensive methods, for example, the VAST algorithm.
Short Abstract: We have developed a method to integrate available genome-wide datasets in order to understand chromatin regulation and gene expression. We have then applied this method to identify unknown functions of the CBP protein in Drosophila melanogaster. To achieve this aim, we have used genome-wide mapping of CBP, several other chromatin proteins, histone modifications and gene feature annotations in the S2 cell line. CREB-binding protein (p300/CBP) has a histone acetyltransferase activity, is a transcriptional co-regulator that interact with multiple transcription factors and have been used to predict novel enhancers. In this approach, we classified the binding sites of CBP into nine different classes using two multivariate methods: Principal Component Analysis (PCA) and Hierarchichal Clustering Analysis (HCA). We then validated the classification with enhancer annotations and with our recent finding that CBP together with GAF can induce high polymerase II pausing in active promoters. Further analysis of the nine classes using a data integrative approach enabled us to identify previously unknown functions of CBP. One of our CBP classes are enriched in polycomb proteins, indicating a role of CBP in Polycomb repression and three of the classes show enrichment of insulator proteins, indicating that CBP protein play a role in insulator function. The success of this method to identify and verify different functions of CBP also shows its potential for a general use in studying the function of other chromatin associated proteins.
Short Abstract: Symmetry is a common and significant feature of protein structures. Symmetry has been found to be important for understanding protein evolution, DNA binding, allosteric regulation, cooperativity, and folding. We have compiled a census of internal symmetry, conducted using the novel CE-Symm algorithm. We find that internal symmetry is present in at least 18% of superfamilies. To elucidate the relationship between symmetry and protein function, the census is analyzed with respect to structural classification, enzyme activity, and ligand binding. The CE-Symm algorithm was benchmarked against a manually curated set of ~1000 domains.
Myers-Turnbull, D., Bliven, S. E., Rose, P. W., Aziz, Z. K., Youkharibache, P., Bourne, P. E., & Prlić, A. (2014). Systematic Detection of Internal Symmetry in Proteins Using CE-Symm. Journal of Molecular Biology, 426(11), 2255–2268. PMID: 24681267
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