Seminar: Two Stories about Computers and Biology
2015-08-18: by Larry Ruzzo, University of Washington. The seminar will take place August 18th, 14.00-14.45 at University of Copenhagen, Orangeriet, Dyrlægevej 36, Frederiksberg C.
Registration is not necessary. Refreshments will be served.
RNAseq data is now widely used for analysis of gene expression, and is widely viewed as simple and quantitatively accurate. “You just count reads. What could go wrong?” A closer look at RNAseq data, however, quickly reveals extensive technical bias of unknown origin and consequence, as well as difficult problems inferring, e.g., gene isoform (alternative splicing) levels from read counts. The first of my stories will outline an approach for quantifying and (partially) correcting for sequence-dependent biases and for robustly estimating isoform changes in a set of RNAseq measurements. The second story involves use of this computational tool as one component in the analysis of stem cell maturation data. Specifically, collaborators have shown that the let-7 microRNA is a key driver of maturation of human embryonic cardiomyocytes. Among other things, RNAseq analysis identified several interesting isoform changes accompanying this transition.
A new approach to bias correction in RNA-Seq. D.C. Jones, W.L. Ruzzo, X. Peng, M.G. Katze, Bioinformatics 28:7, 2012.
Let-7 family of microRNA is required for maturation and adult-like metabolism in stem cell-derived cardiomyocytes. K.T. Kuppusamy, D.C. Jones, H. Sperber, A. Madan, K.A. Fischer, M.L. Rodriguez, L. Pabon, W.Z. Zhu, N.L. Tulloch, X. Yang, N.J. Sniadecki, M.A. Laflamme, W.L. Ruzzo, C.E. Murry, H. Ruohola-Baker, Proc Natl Acad Sci U S A, 2015