Lecture by Joseph N. Paulson, PhD Harvard Postdoctoral Fellow Advisor: John Quackenbush Host: Sean Davis
"Methods to account for sequencing artifacts in sparse high-throughput data" August 29, 3:00-4:00pm NIH Main Campus Bldg 50, First floor conference room(s) Please feel free to forward to interested parties, special interest groups, etc. ABSTRACT We present two methods accounting for sequencing artifacts in the analysis of high-throughput sequencing data. First, we introduce a methodology to assess differential abundance in sparse microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling—a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field. We motivate this on a large infant healthy/diarrheal cohort where we find novel disease associated pathogens fromStreptococcus mitis/pneumoniae groups. Second, although ultrahigh-throughput RNA-sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-seq data from hundreds or thousands of samples, often collected at multiple locations and from diverse tissues. We examine the effects of different preprocessing methods on downstream analyses. We find analysis of large RNA-seq data sets requires careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. We motivate our results using the GTEx cohort and look at the impact of age on gene expression in oncogenes vs tumor suppressors vs neither.
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