Employment and Training

Join a dynamic cancer genomics and computational biology/bioinformatics research team at the BC Cancer Agency, Vancouver, BC CANADA

The following positions are immediately available in the Computational Biology Lab of Dr. Sohrab Shah (compbio.bccrc.ca).

If interested, send a CV, cover letter and contact information for three references to careersmolonc [at] bccrc [dot] ca and quote job number in the subject line.

Careers

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Post-doctoral researcher

….. to lead key research projects ongoing in the lab of Dr. Sohrab Shah.  The PDF should hold a PhD degree in bioinformatics, computer science, statistics or molecular biology and possess exceptional computational skills.   The research will focus on one or more of the following topics, all related to analysis of next generation whole genome sequencing data: i) quantifying and modeling tumour evolution; ii) germline genetics and hereditary cancer; iii) alternative splicing in cancer; iv) the mutational landscape of triple negative breast cancer.  The PDF will design and carry out experiments to address key questions in the above topics and lead the writing of scientific manuscripts.   Highly motivated individuals with a desire to make an impact in the field of cancer genomics are encouraged to apply.

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Selected recent papers from Dr. Shah’s lab:

  1. Eirew P, Steif A, Khattra J, et al., Shah SP, Aparicio S. Dynamics of genomic clones in breast cancer patient xenografts at single cell resolution. 2014 Nature Nov 26 doi: 1038/nature13952
  2. Ha G, Roth A, et al., Shah SP. TITAN: Inferring copy number architectures of clonal cell populations from tumour whole genome sequencing data. Genome Biology. 2014 Nov;24(11):1881-93.
  3. Chan FC, et al., Shah SP, Steidl C. An RCOR1 loss-associated gene expression signature identifies a prognostically significant DLBCL subgroup. Blood. 2014 Nov 13. DOI: http://dx.doi.org/10.1182/blood-2013-06-507152
  4. Roth A, et al., Shah SP. PyClone: statistical inference of clonal population structure in cancer. Nature Methods 2014 Apr;11(4):396-8.
  5. Bashashati A, et al., Shah SP. Distinct evolutionary trajectories of primary high grade serous ovarian cancers revealed through spatial mutational profiling. J Pathol. 2013 Sep;231(1):21-34.
  6. Ha G, et al., Shah SP. Integrative analysis of genome-wide loss of heterozygosity and mono-allelic expression at nucleotide resolution reveals disrupted pathways in triple negative breast cancer. Genome Res 2012 Oct;22(10):1995-2007.
  7. Curtis C*, Shah SP*, et al., Aparicio S. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012 Apr 18;486(7403):346-52. *Equal contribution.
  8. Shah SP, et al., Aparicio S. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012 Apr 4;486(7403):395-9.
  9. Roth A, et al., Shah SP. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 2012 Apr 1;28(7):907-13.
  10. Ding J, et al., Shah SP. Feature based classifiers for somatic mutation detection in tumour-normal paired sequencing data. Bioinformatics. 2012 Jan 15;28(2):167-75.
  11. McPherson A, et al., Shah SP. deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput Biol. 2011 May: 7(5):e1001138.
  12. Steidl C*, Shah SP*, et al., Gascoyne RD. MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers. Nature 2011 Mar 17;471(7338):377-81. *Equal contribution.
  13. Goya R, et al., Shah SP. SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics. 2010 Mar 15;26(6):730-6.
  14. Shah SP, et al., Aparicio S. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature. 2009 Oct 8;461(7265):809-13. Contribution: project lead
  15. Shah SP, et al., Huntsman DG. Mutation of FOXL2 in granulosa-cell tumors of the ovary. N Engl J Med. 2009 Jun 25;360(26):2719-29.