FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer

Genome Biol. 2014;15(10):480. doi: 10.1186/s13059-014-0480-5.


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

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Computational Biology
  • DNA Mutational Analysis / methods*
  • Gene Expression Regulation, Neoplastic
  • Genomics / methods
  • Humans
  • Neoplasms / genetics*
  • Software*