FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer

By Yao Fu, Zhu Liu, S. Lou, Jason Bedford, X. Mu, Kevin Y. Yip, Ekta Khurana, Mark Gerstein. 2014

337 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, methodology): “Non-coding variants in Figure S1A were selected from all variants in the two set that were available in the Eigen, FunSeq2 (2) and LINSIGHT wholegenome files.”
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015, background): “The most successful prior approaches to analyze noncoding variants compare them to the broad regions that functional genomics experiments have annotated to have reproducible accessibility, protein binding, and/or histone modifications (Kircher et al. 2014; Fu et al. 2014; Ritchie et al. 2014).”
Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data (2016, neutral): “20), and fitCons 3 ).”
GAVIN: Gene-Aware Variant INterpretation for medical sequencing (2017, methodology): “From the output VCF, we used the INFO fields “ PSNPE, ” “ FATE, ” “ GWAVAE, ” “ DANNE, ” and “ FUNE ” for the pathogenicity estimation outcomes according to the PredictSNP protocol for PredictSNP2 consensus, FATHMM, GWAVA, DANN, and FunSeq, respectively.”