Colin Dewey

Professor of Biostatistics & Medical Informatics

2128 Genetics-Biotechnology Center, colin.dewey@wisc.edu, 608.263.7610

Departments

Biostatistics & Medical Informatics, Computer Sciences

Education

PhD (2006), University of California Berkeley

Research Interests

Development of statistical and algorithmic approaches to the analysis of biological sequence data.

Lab Website

http://www.biostat.wisc.edu/~cdewey/

Representative Awards

  • 2016 Vilas Associate Award, University of Wisconsin-Madison

Research

My research focuses primarily on the computational analysis of biological sequence data, particularly large-scale nucleotide sequence sets, such as the output of the latest DNA sequencers and the genome sequences that may be assembled with them. This area of research is crucial to the advancement of biology, which has become increasingly dependent on high-throughput sequencing for a wide variety of applications. My projects generally fall into one of two main lines of research: (i) transcriptome analysis with RNA sequencing (RNA-seq) data, and (ii) evolutionary analysis of whole genomes. In both lines of my research, I focus on the development of new computational methodology that is both efficient and statistically-grounded.

Representative Publications  (Google Scholar | PUBMED)

  • Bernstein, M. N., Doan, A. & Dewey, C. N. MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive. Bioinformatics (Oxford, England) 33, 2914-2923, doi:10.1093/bioinformatics/btx334 (2017).
  • Liu, P., Sanalkumar, R., Bresnick, E. H., Keles, S. & Dewey, C. N. Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq. Genome Res 26, 1124-1133, doi:10.1101/gr.199174.115 (2016).
  • Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature protocols 8, 1494-1512, doi:10.1038/nprot.2013.084 (2013).
  • Stewart, R. et al. Comparative RNA-seq analysis in the unsequenced axolotl: the oncogene burst highlights early gene expression in the blastema. PLoS computational biology 9, e1002936, doi:10.1371/journal.pcbi.1002936 (2013).
  • Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics 12, 323, doi:10.1186/1471-2105-12-323 (2011).