Accepted Posters

Category 'H'- Gene Prediction'

Poster H1

Searching for Genes in Novel Genomes

Brona Brejova- Comenius University Bratislava

Tomas Vinar (Comenius University Bratislava, Applied Informatics); Daniel G. Brown (University of Waterloo, Computer Science); Ming Li (University of Waterloo, Computer Science); Yan Zhou (Chinese National Human Genome Center at Shanghai, Shanghai-MOST Key Laboratory of Health and Disease Genomics);

Short Abstract: We have developed a novel iterative method for estimating parametersof hidden Markov models for gene finding in newly sequencedspecies. We have used our approach to produce initial annotation ofnewly sequenced Schistosoma japonicum draft genome. Our new gene setprovides a first glimpse at a gene complement of a flatworm (phylumplatyhelmintes).

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Poster H2

Improving Gene Finding Accuracy with RNA_Seq and Tiling Array Data

Jonas Behr- Max Planck Society

Gabriele Schweikert (Max Planck Society, Friedrich Miescher Laboratory);

Short Abstract: We have developed a new accurate gene finding system called mGene based on Hidden Semi Markov SVMs, that is very flexible in terms of incorporating different features. Exploiting tiling array and RNA-Seq transcriptome measurements as features in addition to the genome sequence leads to considerably improvements in prediction accuracy.

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Poster H3

The effect of sequencing errors on metagenomic gene prediction

Katharina Hoff- Georg-August-Universität Göttingen

Maike Tech (Georg-August-Universität Göttingen, Institut für Mikrobiologie und Genetik, Abteilung für Bioinformatik); Fabian Schreiber (Georg-August-Universität Göttingen, Institut für Mikrobiologie und Genetik, Abteilung für Bioinformatik); Peter Meinicke (Georg-August-Universität Göttingen, Institut für Mikrobiologie und Genetik, Abteilung für Bioinformatik);

Short Abstract: Gene prediction is essential during the annotation of metagenomic sequencing reads. In a benchmark test, we compared the performance of gene prediction tools on simulated reads with sequencing errors. Our results suggest that the incorporation of similar error-compensating methods into metagenomic gene prediction tools may improve their quality significantly.

Long Abstract: Click Here

Poster H4

A Fully Automatic AUGUSTUS Pipeline for Eukaryotic Genome Annotation Based on ESTs

Mario Stanke- Universit of Goettingen

No additional authors

Short Abstract: We are presenting an open source gene prediction pipeline that only requires a genome assembly and a set of ESTs as input. It trains AUGUSTUS fully automatically using the ESTs. The gene structure annotation is then basedon EST evidence where locally available or is performed ab initio.

Long Abstract: Click Here

Poster H5

mGene.web: A Web Service for Accurate Computational Gene Finding

Ratsch Gunnar- Friedrich Miescher Laboratory of the Max Planck Society

Gabriele Schweikert (Friedrich Miescher Laboratory, Machine Learning in Biology); Jonas Behr (Friedrich Miescher Laboratory, Machine Learning in Biology); Alexander Zien (Friedrich Miescher Laboratory, Machine Learning in Biology); Johannes Eichner (Friedrich Miescher Laboratory, Machine Learning in Biology); Soeren Sonnenburg (Friedrich Miescher Laboratory, Machine Learning in Biology); Gunnar Raetsch (Friedrich Miescher Laboratory, Machine Learning in Biology);

Short Abstract: We provide mGene.web, a web service for genomewideprediction of protein coding genes from DNAsequences. mGene.web additionally offers the functionalityto retrain the system on a new organism.It is integrated into the Galaxy framework forgenomic data analysis, is availableat http://www.mgene.org/webservice, freeof charge, and can be used for eukaryotic genomes ofmoderate size.

Long Abstract: Click Here

Accepted Posters

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• A) Bioinformatics of Health and Disease
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