LIST-S2: taxonomy based sorting of deleterious missense mutations across species
By Nawar Malhis, Matthew Jacobson, Steven J. M. Jones, Jörg Gsponer. 2020
90 citations recorded by the source corpus.
Abstract The separation of deleterious from benign mutations remains a key challenge in the interpretation of genomic data. Computational methods used to sort mutations based on their potential deleteriousness rely largely on conservation measures derived from sequence alignments. Here, we introduce LIST-S2, a successor to our previously developed approach LIST, which aims to exploit local sequence identity and taxonomy distances in quantifying the conservation of human protein sequences. Unlike its predecessor, LIST-S2 is not limited to human sequences but can assess conservation and make predictions for sequences from any organism. Moreover, we provide a web-tool and downloadable software to compute and visualize the deleteriousness of mutations in user-provided sequences. This web-tool contains an HTML interface and a RESTful API to submit and manage sequences as well as a browsable set of precomputed predictions for a large number of UniProtKB protein sequences of common taxa. LIST-S2 is available at: https://list-s2.msl.ubc.ca/
Subfields: Artificial Intelligence, Genetics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Genetics
In Molecular Biology
Citations in the corpus
- dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…BayesDel_noAF [15], CADD_hg19 [16], ClinPred [17], DEOGEN2 [18], Eigen and Eigen PC [19], FATHMM-XF [20], GenoCanyon [21], LINSIGHT [22], LIST-S2 [23], M-CAP [24], MPC [25], MutPred [26], MVP [27], PrimateAI [28], REVEL [29], SIFT4G [30]), one score for loss of function prediction (ALoFT [31]),…”