FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer Academic Article uri icon

Overview

MeSH Major

  • DNA Mutational Analysis
  • Neoplasms
  • Software

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.

publication date

  • January 2014

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4203974

PubMed ID

  • 25273974

Additional Document Info

start page

  • 480

volume

  • 15

number

  • 10