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Genome Biol. 2014;15(10):480.

FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer.

Author information

1
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.

Abstract

Identification of noncoding drivers from thousands of somatic alterations in a typical tumor is a difficult and unsolved problem. We report a computational framework, FunSeq2, to annotate and prioritize these mutations. The framework combines an adjustable data context integrating large-scale genomics and cancer resources with a streamlined variant-prioritization pipeline. The pipeline has a weighted scoring system combining: inter- and intra-species conservation;loss- and gain-of-function events for transcription-factor binding; enhancer-gene linkages and network centrality; and per-element recurrence across samples. We further highlight putative drivers with information specific to a particular sample, such as differential expression. FunSeq2 is available from funseq2.gersteinlab.org.

PMID:
25273974
PMCID:
PMC4203974
DOI:
10.1186/s13059-014-0480-5
[Indexed for MEDLINE]
Free PMC Article

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