Accepted Posters

Category 'H'- Gene Prediction'
Poster H1
Identification of Prokaryotic Small Proteins using a Comparative Genomic Approach
Josue Samayoa- Johns Hopkins University
Kevin Karplus (UC Santa Cruz, Biomolecular Engineering ); Fitnat Yildiz (UC Santa Cruz, Microbiology and Environmental Toxicology);
Short Abstract: Accurate prediction of genes encoding short proteins remains an elusive open problem in bioinformatics. Our method incorporates neural-net predictions for 3 local structure alphabets within a comparative genomic approach to generate predictions for whether or not a given open reading frame encodes for a short protein.
Long Abstract:Click Here

Poster H2
RGASP: Assessment of Gene-Finding Tools in the High-throughput Era
Josep Abril- Universitat de Barcelona
Felix Kokocinski (Wellcome Trust Sanger Institute, ENCODE Team); Josep F Abril (Universitat de Barcelona / IBUB, Genetics); Tim Hubbard (Wellcome Trust Sanger Institute, Vertebrate Genome Analysis Team ); Jennifer Harrow (Wellcome Trust Sanger Institute, HAVANA Team );
Short Abstract: Several groups have improved their gene-finders or have developed new tools to incorporate RNASeq data as evidence, to better define gene-loci and alternatively spliced transcripts. We organised the RNASeq Genome Annotation Assessment Project (RGASP). We present evaluation results for those tools on whole-genome annotated features for human, worm and fly.
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Poster H3
Prediction of microRNA precursor hairpins with improved sensitivity and specificity
Goro Terai- INTEC Systems Institute, Inc.
Hiroaki Okida (INTEC Systems Institute, Inc., Bio Business Division); Kiyoshi Asai (Graduate School of Frontier Sciences, University of Tokyo, Department of Computational Biology); Toutai Mituyama (Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), RNA informatics team);
Short Abstract: We developed a method miRRim2 for detecting conserved microRNA. In our method, a miRNA is represented by a sequence of multi-dimensional vectors, which is modeled using hidden markov model. Our method can not only accurately predict miRNA genes but also infer 5'-end of mature miRNA.
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Poster H4
Exon definition by entropy minimization of bipartite sequence patterns
Peter Rogan- University of Western Ontario
Kandasamy Ganeshamoorthy (University of Western Ontario, Computer Science);
Short Abstract: Exon boundaries were detected with a method previously applied for prediction of bipartite transcription factor binding sites. The algorithm minimizes overall Shannon entropy across a pair of motifs separated by a variable length gap. Performance and accuracy were improved by parallelization of a previous sequential algorithm.
Long Abstract:Click Here

Poster H5
FragGeneScan: Predicting Genes in Short and Error-prone Reads
Mina Rho- Indiana University
Haixu Tang (Indiana University, School of informatics and Computing); Yuzhen Ye (Indiana University, School of informatics and Computing);
Short Abstract: We have developed a novel gene predictor FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein coding regions in short reads. FragGeneScan has demonstrated improved performance on simulated short reads (with and without sequencing errors), and real metagenomic sequences.
Long Abstract:Click Here

Poster H6
A Compendium of Causal Gene Predictions for Mendelian, Complex, and Somatic Copy Number Alteration Disease
Rahul Deo- Harvard Medical School
No additional authors
Short Abstract: Novel, improved therapies for human disease will require the identification of additional genetic targets. Towards this end, genome-wide studies of human disease have recently implicated hundreds of multigenic loci in disease causation. In parallel, an explosion has occurred in gene characterization by experimental and computational methods. To facilitate the identification of disease genes, we have developed and implemented a new approach, CADET, to predict the association of genes with Mendelian, complex, and somatic copy number alteration disease phenotypes. CADET uses ensemble recursive partitioning for gene classification based on multiple predictive feature types, with positive training examples derived from individual disease genes and multigenic loci. CADET shows excellent performance for predicting causal genes for a range of complex and Mendelian disease phenotypes, including autoimmune diseases, bone mineral density, cancer subtypes and cardiomyopathies. We implement a probabilistic approach for integrating genome-wide CADET scores with other population-based evidence of disease association to prioritize genes for experimental validation, with the goal of facilitating drug discovery.
Long Abstract:Click Here

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