Using FunSeq2 for Coding and Non-Coding Variant Annotation and Prioritization

Priyanka Dhingra; Yao Fu; Mark Gerstein; Ekta Khurana

doi:10.1002/cpbi.23

Using FunSeq2 for Coding and Non-Coding Variant Annotation and Prioritization

Curr Protoc Bioinformatics. 2017 May 2:57:15.11.1-15.11.17. doi: 10.1002/cpbi.23.

Authors

Priyanka Dhingra^{1

2}, Yao Fu³, Mark Gerstein^{4

5

6}, Ekta Khurana^{1

2

7

8}

Affiliations

¹ Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York.
² Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York 10021.
³ Bina Technologies, Roche Sequencing, Redwood City, California.
⁴ Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut.
⁵ Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut.
⁶ Department of Computer Science, Yale University, New Haven, Connecticut.
⁷ Meyer Cancer Center, Weill Cornell Medical College, New York, New York.
⁸ Englander Institute for Precision Medicine, Weill Cornell Medical College, New York, New York.

PMID: 28463398
DOI: 10.1002/cpbi.23

Abstract

The identification of non-coding drivers remains a challenge and bottleneck for the use of whole-genome sequencing in the clinic. FunSeq2 is a computational tool for annotation and prioritization of somatic mutations in coding and non-coding regions. It integrates a data context made from large-scale genomic datasets and uses a high-throughput variant prioritization pipeline. This unit provides guidelines for installing and running FunSeq2 to (a) annotate and prioritize variants, (b) incorporate user-defined annotations, and (c) detect differential gene expression. © 2017 by John Wiley & Sons, Inc.

Keywords: cancer drivers; differential gene expression; disease-causing; indels; non-coding variants; single nucleotide variants.

MeSH terms

Genome / genetics
Genomics
Humans
Molecular Sequence Annotation / methods*
Software*