ArticlePloS one2009
Bi-directional SIFT predicts a subset of activating mutations.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 39 citations in OpenAlex.
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- Genomic analysis of the rhesus macaque (Ecology and evolution · 2023Article
- Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics.Molecular aspects of medicine · 2023Review
- Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.Handbook of experimental pharmacology · 2023Article
- Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference.iScience · 2022Article
- A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants.Human genomics · 2021Article
- The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs.Science advances · 2020Article
- A Rare Mutation in SPLUNC1 Affects Bacterial Adherence and Invasion in Meningococcal Disease.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020Article
- Article
- Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data.Frontiers in pharmacology · 2018Review
- AlloRep: A Repository of Sequence, Structural and Mutagenesis Data for the LacI/GalR Transcription Regulators.Journal of molecular biology · 2016Article
- HMMvar-func: a new method for predicting the functional outcome of genetic variants.BMC bioinformatics · 2015Article
- Identification of genomic features in the classification of loss- and gain-of-function mutation.BMC medical informatics and decision making · 2015Article
- Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective.Genome biology and evolution · 2014Article
- Computational approaches to identify functional genetic variants in cancer genomes.Nature methods · 2013Article
- Assessment of computational methods for predicting the effects of missense mutations in human cancers.BMC genomics · 2013Article
- Multiple co-evolutionary networks are supported by the common tertiary scaffold of the LacI/GalR proteins.PloS one · 2013Article
- Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP.PloS one · 2013Article
- Novel insights from hybrid LacI/GalR proteins: family-wide functional attributes and biologically significant variation in transcription repression.Nucleic acids research · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.