FunSeq statistics

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Associated diseases

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Protocols

FunSeq specifications

Information


Unique identifier OMICS_08575
Name FunSeq
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input format BED, VCF
Operating system Unix/Linux
Computer skills Advanced
Version 2.1.4
Stability Stable
Requirements
sed, awk, grep, bedtools, tabix, VAT
Maintained Yes

Taxon


  • Primates
    • Homo sapiens

Versioning


No version available

Maintainer


  • person_outline Mark Gerstein

Additional information


http://info.gersteinlab.org/Funseq2

Information


Unique identifier OMICS_08575
Name FunSeq
Interface Web user interface
Restrictions to use None
Input format BED, VCF
Computer skills Basic
Version 2.0
Stability Stable
Maintained Yes

Taxon


  • Primates
    • Homo sapiens

Maintainer


  • person_outline Mark Gerstein

Additional information


http://info.gersteinlab.org/Funseq2

Publications for FunSeq

FunSeq citations

 (22)
library_books

Cancer driver mutation prediction through Bayesian integration of multi omic data

2018
PLoS One
PMCID: 5940219
PMID: 29738578
DOI: 10.1371/journal.pone.0196939

[…] a phenomenological score for each mutation based on the physical-chemical properties or the extent of evolutionary conservation of the amino acid sequences affected by the mutation. oncodrivefm and funseq search for positive selection and purifying selection patterns to identify cancer driver genes [, ]. these approaches focus on local sequence features, missing the gene regulatory effect […]

library_books

Highly similar genomic landscapes in monoclonal B cell lymphocytosis and ultra stable chronic lymphocytic leukemia with low frequency of driver mutations

2018
Haematologica
PMCID: 5927998
PMID: 29449433
DOI: 10.3324/haematol.2017.177212

[…] highest mutational enrichment in the ig loci and within sites known to be recurrently affected by off-target somatic hypermutation (e.g. btg2, bcl6 and tcl1a) (online supplementary figure s1)., funseq2, a bioinformatics tool investigating the linkage between ncvs and target genes using integrated bisulfite sequencing, chip-sequencing, and rna-sequencing data from the roadmap epigenomics […]

library_books

Identifying noncoding risk variants using disease relevant gene regulatory networks

2018
Nat Commun
PMCID: 5816022
PMID: 29453388
DOI: 10.1038/s41467-018-03133-y

[…] network-based features, we built a random forest (rf) classifier using these features and sequence-based features used by two state-of-the-art methods, genome-wide annotation of variants (gwava) and funseq2. we evaluated the relative importance of all features (six from this study and 182 from gwava and funseq2 combined) by using a recursive feature elimination (rfe) approach. applying the rfe […]

library_books

Identification of Single Nucleotide Non coding Driver Mutations in Cancer

2018
Front Genet
PMCID: 5801294
PMID: 29456552
DOI: 10.3389/fgene.2018.00016

[…] expressed and regulatory elements active in the tissues of interest (; ; ; ) (table ). these approaches include regulomedb () that considers functional annotations for the regulatory regions, and funseq2 () that also considers sequence conservation across species and recurrence of somatic mutations in cancer (table )., although motif analyses have been instrumental to predict altered tf […]

library_books

Using DIVAN to assess disease/trait associated single nucleotide variants in genome wide scale

2017
BMC Res Notes
PMCID: 5663107
PMID: 29084591
DOI: 10.1186/s13104-017-2851-y

[…] been developed to annotate genetic variants using genome-wide profiling data including gwava [], cadd [], genocanyon [], eigen, eigenpc [], dann [], fitcons [], fathmm [], deltasvm [], dbnsfp [], funseq 2 [] and icages []. a common feature of those methods is that they are disease/trait neutral, which means they only predict if a variant is deleterious or not, but not able to tell […]

library_books

Differential analysis between somatic mutation and germline variation profiles reveals cancer related genes

2017
Genome Med
PMCID: 5574113
PMID: 28841835
DOI: 10.1186/s13073-017-0465-6

[…] method developed by youn and simon [], oncodriveclust [], oncodrivefml [], and madgic [], we always ran these programs using default parameters on the same maf file we used for our method. we ran funseq2 [] by submitting identical maf files to their web server using default parameters., to evaluate whether the uemd of a gene is significant, we test whether it has a significantly higher uemd […]


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FunSeq institution(s)
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; School of Life Science, Fudan University, Shanghai, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Molecular Biophysics and Biochemistry Department, Yale University, New Haven, CT, USA
FunSeq funding source(s)
Supported by the National Institutes of Health, AL Williams Professorship, the Yale University Faculty of Arts and Sciences High Performance Computing Center (Grant Number RR029676-01) and the National Science Foundation - Graduate Research Fellowship Program (Grant Number 1346837).

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