Presenter: Joseph N. Paulson, Ph.D., Postdoctoral Research Fellow, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA
Title: “Methods to account for sequencing artifacts in sparse high-throughput data”
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 from Streptococcus 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 differential pathways of cell lines from their progenitor tissues.
The Jackson Laboratory for Genomic Medicine Leo Holt Conference Room
1:00 pm
Hosted by: Dr. Julia Oh
For more information, contact: Miriam Rodriguez at miriam.rodriguez@jax.org