Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0077940
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dc.titleSIFT Indel: Predictions for the Functional Effects of Amino Acid Insertions/Deletions in Proteins
dc.contributor.authorHu J.
dc.contributor.authorNg P.C.
dc.date.accessioned2019-11-05T02:11:08Z
dc.date.available2019-11-05T02:11:08Z
dc.date.issued2013
dc.identifier.citationHu J., Ng P.C. (2013). SIFT Indel: Predictions for the Functional Effects of Amino Acid Insertions/Deletions in Proteins. PLoS ONE 8 (10) : e77940. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0077940
dc.identifier.issn1932-6203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161458
dc.description.abstractIndels in the coding regions of a gene can either cause frameshifts or amino acid insertions/deletions. Frameshifting indels are indels that have a length that is not divisible by 3 and subsequently cause frameshifts. Indels that have a length divisible by 3 cause amino acid insertions/deletions or block substitutions; we call these 3n indels. The new amino acid changes resulting from 3n indels could potentially affect protein function. Therefore, we construct a SIFT Indel prediction algorithm for 3n indels which achieves 82% accuracy, 81% sensitivity, 82% specificity, 82% precision, 0.63 MCC, and 0.87 AUC by 10-fold cross-validation. We have previously published a prediction algorithm for frameshifting indels. The rules for the prediction of 3n indels are different from the rules for the prediction of frameshifting indels and reflect the biological differences of these two different types of variations. SIFT Indel was applied to human 3n indels from the 1000 Genomes Project and the Exome Sequencing Project. We found that common variants are less likely to be deleterious than rare variants. The SIFT indel prediction algorithm for 3n indels is available at http://sift-dna.org/. © 2013 Hu, Ng.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectamino acid
dc.subjectprotein
dc.subjectamino acid substitution
dc.subjectarea under the curve
dc.subjectarticle
dc.subjectclassifier
dc.subjectcorrelation coefficient
dc.subjectdecision tree
dc.subjectframeshift mutation
dc.subjectgene construct
dc.subjectgenetic algorithm
dc.subjectgenetic variability
dc.subjecthuman
dc.subjectindel mutation
dc.subjectmeasurement accuracy
dc.subjectprediction
dc.subjectprotein function
dc.subjectprotein structure
dc.subjectsensitivity and specificity
dc.subjectSIFT Indel
dc.subjectvalidation study
dc.subjectalgorithm
dc.subjectamino acid sequence
dc.subjectbiological model
dc.subjectgenetics
dc.subjectindel mutation
dc.subjectAlgorithms
dc.subjectAmino Acid Sequence
dc.subjectArea Under Curve
dc.subjectHumans
dc.subjectINDEL Mutation
dc.subjectModels, Genetic
dc.subjectProteins
dc.subjectSensitivity and Specificity
dc.typeArticle
dc.contributor.departmentMEDICINE
dc.description.doi10.1371/journal.pone.0077940
dc.description.sourcetitlePLoS ONE
dc.description.volume8
dc.description.issue10
dc.description.pagee77940
dc.published.statePublished
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