Wasserman Lab

Wyeth W. Wasserman

The Wasserman laboratory focuses on the creation, evaluation and application of computational methods for the analysis of genome sequences, with international strength in the study of cis-regulatory elements regulating gene expression. The lab creates widely used software and databases, performs applied analyses of genome sequences, and partners with diverse research teams on projects at the intersection of the computational and life sciences.

Genome Sequencing has disrupted health research. The lab has been developing computational methods and tools to allow researchers and clinicians to identify functional consequences of genetic variations within cis-regulatory elements such as transcription factor binding sites. The 1500 transcription factors (TFs) within the human genome perform a key role in determining the set of active genes within a specific cell, as well as the magnitude of activity. Defects within TFs are widely recognized to contribute to genetic disorders – such changes may block the formation of certain cell types. Alterations in the DNA sequences bound by these factors can contribute causally to phenotypes, but much work remains to develop the essential computational detection methods.

The lab studies gene regulation via multiple lines. First, the lab creates novel algorithms and software to predict interactions between TFs and DNA. The software incorporates diverse types of data to maximize prediction quality. Second, the lab collaborates internationally on the analysis of emerging data, such as an international FANTOM project to map regulatory sequence positions through the study of the 5’ end of RNA transcripts. Third, the lab engages in the applied analysis of genomes for pediatric disorders, in part to improve the delivery of information to clinicians. Such work is key to translating basic research advances into clinical impacts.

Computational biology is a critical need in an era of high-throughput methods. The lab’s trainees have done well, taking on positions at leading institutions.


UBC Killam Teaching Prize, University of British Columbia – 2013

Basic Science Teaching Award, Department of Medical Genetics, UBC – 2011

Michael Smith Foundation for Health Research Scholar Award – 2004

Canadian Institutes for Health Research New Investigator Award – 2004

Mathelier A, Xin B, Chiu t-p, Yang L, Rohs R*, and Wasserman WW*, 2016, “DNA shape features improve transcription factor binding site predictions in vivo”, Cell Systems, 3, 278-286. (*Co-corresponding authors)  (PMID: 27546793)  [This article was highlighted by Stormo GD and Roy B, 2016, Cell Systems, 3(3), 216-218. (PMID: 27684185)]

Chen CY, Shi W, Balaton BP, Matthews AM, Li Y, Arenillas DJ, Mathelier A, Itoh M, Kawaji H, Lassmann T, Hayashizaki Y, Carninci P, Forrest ARR, Brown CJ*, Wasserman WW*, 2016, “YY1 binding association with sex-biased transcription revealed through X-linked transcript levels and allelic binding analyses”, Scientific Reports, 6, 37324. (*Co-corresponding authors)  (PMID: 27857184)

Shi W, Fornes O, Mathelier A, and Wasserman WW, 2016, “Evaluating the impact of single nucleotide variants on transcription factor binding”, Nucleic Acids Research, 44, 10106-10116.  (PMID: 27492288)

Arenillas DJ, Forrest ARR, Kawaji H, Lassmann T, FANTOM Consortium, Wasserman WW*, and Mathelier A*, 2016, “CAGEd-oPOSSUM: motif enrichment analysis from CAGE-derived TSSs”, Bioinformatics, 32, 2858-2860. (*co-corresponding authors)  (PMID:  27334471)

Mathelier A, Fornes O, Arenillas DJ, Chen CY, Denay G, Lee J, Shi W, Shyr C, Tan G, Worsley-Hunt R, Zhang AW, Parcy F, Lenhard B*, Sandelin A*, Wasserman WW*, 2016, “JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles”, Nucleic Acids Research, 44, D110-115. (*Co-corresponding authors)   (PMID: 26531826)

Tarailo-Graovac M, Shyr C, Ross CJ, Horvath GA, Salvarinova R, Ye XC, Zhang LH, Bhavsar AP, Lee JJ, Drögemöller BI, Abdelsayed M, Alfadhel M, Armstrong L, Baumgartner MR, Burda P, Connolly MB, Cameron J, Demos M, Dewan T, Dionne J, Evans AM, Friedman JM, Garber I, Lewis S, Ling J, Mandal R, Mattman A, McKinnon M, Michoulas A, Metzger D, Ogunbayo OA, Rakic B, Rozmus J, Ruben P, Sayson B, Santra S, Schultz KR, Selby K, Shekel P, Sirrs S, Skrypnyk C, Superti-Furga A, Turvey SE, Van Allen MI, Wishart D, Wu J, Wu J, Zafeiriou D, Kluijtmans L, Wevers RA, Eydoux P, Lehman AM, Vallance H, Stockler-Ipsiroglu S, Sinclair G, Wasserman WW, van Karnebeek CD, 2016, “Exome Sequencing and the Management of Neuro-metabolic Disorders”, New England Journal of Medicine, 374, 2246-2255.  (PMID: 27276562)

Shyr C, Kushniruk A, van Karnebeek CD, Wasserman WW, 2015, “Dynamic software design for clinical exome and genome analyses: insights from bioinformaticians, clinical geneticists and genetic counselors”, Journal of the American Medical Informatics Association, 23, 257-268.  (PMID: 26117142)