Dahlquist:BOSC ISMB 2016 Notes

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Notes from the Bioinformatics Open Source Conference (BOSC) 2016, NetBio SIG 2016, and Intelligent Systems for Molecular Biology (ISMB) 2016 held in Orlando, Florida from July 8-12, 2016.

BOSC/NetBio SIG Day 1 2016-07-08

NetBio SIG



BOSC SIG Day 2 2016-07-09

Inclusion and Diversity

ISMB Day 1 2016-07-10



  • N13 - Resampling-Based Read-Level Normalization of RNA-Seq for Differential Expression Analysis by Gregory Grant Lab, University of Pennsylvania
  • E05 - Reliable differential expression calls across labs, by use of a simple reference sample by Paweł P. Łabaj, Chair of Bioinformatics, Boku University Vienna, Austria and David P. Kreil, Chair of Bioinformatics, Boku University Vienna, Austria
    • Short Abstract copied from ISMB 2016 site here.
      • Genome-scale expression profiling has become a key tool of functional genomics, critically supporting progress in molecular biology and biomedical research in the post-genomic era. The deduction of gene function remains a major bottleneck in improving our understanding of living systems at the molecular level. Typical applications include the acceleration of unbiased genome-wide screens for candidate genes that are implicated in phenotypes and processes of interest by differential expression calling. The rapid improvement of next generation sequencing (NGS) platforms has triggered a wave of new findings based on whole transcriptome sequencing (RNA-Seq). NGS technology, however, has been shown to suffer from different sources of unwanted variation affecting interpretation of the results. In the controlled setup of the SEQC benchmark study, we have recently shown that unwanted variation is largely due to library preparation. Appropriate tools for factor analysis like PEER or SVASeq can identify and remove confounding factors. With such corrections for site effects we could improve specificity without any loss of sensitivity. Going beyond comparisons in the original SEQC study, we here present results for a range of realistic effect strengths. Moreover, we demonstrate the benefits that can be gained by analysing novel results in the context of other experiments. In particular, use of a standardized reference sample much improves reliability across labs.
  • O05 - Learning biological networks from gene knockdown data by Yuriy Sverchkov, University of Wisconsin--Madison, United States of America
  • O71 - Assessing the Differential Significance of Transcription Factors by Leslie D. Seitz, Fairview High School
  • O69 - Differentially expressed genes are not uniformly distributed by Debra Goldberg, University of Colorado
  • O83 - LoTo: A Method for the Comparison of Local Topology between Gene Regulatory Networks by Tomas Perez-Acle, Computational Biology Lab. Fundación Ciencia para la Vida and and Centro Interdisciplinario de Neurociencia de Valparaiso, Chile


ISMB Day 2 2016-07-11

Workshop on Education in Bioinformatics (WEB)



Other notes