FYI.... Sean
---------- Forwarded message ---------- From: Peterson, Katherine (NIH/NEI) [E] <[email protected]> Date: Mon, Sep 8, 2014 at 1:20 PM Subject: September journal club reminder Greetings, This is to remind you that the Microarray Users' SIG journal club, led by Chris and Alan, will be learning to deal with the practical considerations of bias in analyses of genomic sequencing data. Here are the details: When: Wednesday September 10, 2014, 11:00 a.m. Where: NIH, Bethesda, Building 10 Library OD Conference Room, 1L25H (turn right at the waterfall, it's the first door on the right.) What: Matthew D Young, Matthew J Wakefield, Gordon K Smyth and Alicia Oshlack Gene ontology analysis for RNA-seq: accounting for selection bias Genome Biology 2010, 11:R14 We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology. Paul Geeleher, Lori Hartnett, Laurance J. Egan, Aaron Golden, Raja Affendi Raja Ali and Cathal Seoighe Gene-set analysis is severely biased when applied to genome-wide methylation data Bioinformatics Vol. 29 no. 15 2013, pages 1851-1857 Motivation: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genomewide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters. Results: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis. All are welcome. Cheers, Katherine
