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FunSAV specifications

Information


Unique identifier OMICS_24338
Name FunSAV
Alternative name FUNctional effect predictor of Single Amino acid Variants
Software type Application/Script
Interface Command line interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Stability Stable
Source code URL https://sunflower.kuicr.kyoto-u.ac.jp/sjn/FunSAV/data/FunSAV.zip
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Jiangning Song

Publication for FUNctional effect predictor of Single Amino acid Variants

FunSAV citations

 (4)
library_books

Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

2017
PLoS One
PMCID: 5470696
PMID: 28614374
DOI: 10.1371/journal.pone.0179314

[…] ng amino acid sequence features [], position-specific scoring matrices, residue-contact network features and 3-D structure information. This includes methods such as SIFT [, ], SNAP [], Polyphen2 [], FunSAV [] and SusPect []. SIFT uses sequence homology to predict phenotypic effect based on the assumption that amino acid variants in the evolutionarily conserved regions are more likely to have func […]

library_books

Computational assessment of feature combinations for pathogenic variant prediction

2016
PMCID: 4947862
PMID: 27468419
DOI: 10.1002/mgg3.214

[…] l properties, sequence neighborhood, protein disorder, residue accessibility, and secondary structure information have been used by the methods PMUT (Ferrer‐Costa et al. ), SNAP (Bromberg and Rost ), FunSAV (Wang et al. ), and PolyPhen‐2 (Adzhubei et al. ). Some methods use a large number of features, for example, CADD (Kircher et al. ) combines 63 features to provide an estimate of deleteriousnes […]

library_books

Analysis of Genetic Variation and Potential Applications in Genome Scale Metabolic Modeling

2015
Front Bioeng Biotechnol
PMCID: 4329917
PMID: 25763369
DOI: 10.3389/fbioe.2015.00013

[…] with the applications of metabolic modeling.The majority of algorithms (53%) for variant effect prediction listed in Table rely on machine-learning approaches [e.g., AUTO-MUTE (Masso and Vaisman, ), FunSAV (Wang et al., ), or HANSA (Acharya and Nagarajaram, )], which is a practical strategy given the huge amount of data available for human diseases. Regarding the selection of features, most metho […]

library_books

Status quo of annotation of human disease variants

2013
BMC Bioinformatics
PMCID: 4234487
PMID: 24305467
DOI: 10.1186/1471-2105-14-352

[…] er benign, possibly damaging, or probably damaging [,]. Along this line, other methods have been developed that use predictions by other methods and combine them with their own selection of features. FunSAV, for example, uses machine-learning techniques to analyse mutations using a wide selection of features []. In a second step the prediction is combined with that of other well-known methods such […]

Citations

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FunSAV institution(s)
National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China; Department of Computer Science, School of Electronics and Information Engineering, Tongji University, Shanghai, China; Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan; Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia
FunSAV funding source(s)
Supported by grants from the National Health and Medical Research Council of Australia (NHMRC), the Australian Research Council (ARC), the Japan Society for the Promotion of Science (JSPS), the Hundred Talents Program of the Chinese Academy of Sciences (CAS), the Knowledge Innovation Program of CAS (No. KSCX2-EW-G-8) and Tianjin Municipal Science & Technology Commission (No. 10ZCKFSY05600); by an NHMRC Peter Doherty Fellow and the Recipient of the Hundred Talents Program of CAS and the JSPS Short-term Invitation Fellowship to the Bioinformatics Center, Kyoto University, Japan.

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