Abstract for Biological Data Science 2014 [i0biods14]
Title: A computational framework to prioritize regulatory variants
from whole-genome sequencing in cancer
Mark Gerstein1, Yao Fu1, Zhu Liu2, Shaoke Lou3, Jason Bedford1,
Xinmeng J Mu4, Kevin Y Yip3, Ekta Khurana1
1Yale University, Molecular Biophysics & Biochemistry, New Haven, CT,
2Fudan University, School of Life Science, Shanghai, China, 3The
Chinese University of Hong Kong, Department of Computer Science and
Engineering, Shatin, Hong Kong, 4 Broad Institute of Harvard and MIT,
Broad Institute of Harvard and MIT, Cambridge, MA
Identification of noncoding cancer "drivers" from thousands of somatic
alterations is a difficult and unsolved problem. Here, we developed a
computational framework to annotate and prioritize cancer regulatory
mutations. The framework combines an adjustable data context
summarizing large-scale genomics and cancer-relevant datasets with an
efficient variant prioritization pipeline. To prioritize high impact
variants, we developed a weighted scoring scheme to score each
mutation's impact through analyzing conservation, loss-of and gain-of
function events, gene associations, network topology and across-sample
recurrence. Cancer specific information is used to further highlight
potential oncogenic relevant candidates.
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