Accepted Posters
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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 |
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| 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. |
| Long Abstract:Click Here |
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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. |
| Long Abstract:Click Here |
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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 |
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| 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 |
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| 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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Accepted Posters
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