DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins

By Daniele Raimondi, I. Tanyalcin, Julien Ferté, A. Gazzo, Gabriele Orlando, Tom Lenaerts, Marianne Rooman, Wim F. Vranken. 2017

176 citations recorded by the source corpus.

Abstract High-throughput sequencing methods are generating enormous amounts of genomic data, giving unprecedented insights into human genetic variation and its relation to disease. An individual human genome contains millions of Single Nucleotide Variants: to discriminate the deleterious from the benign ones, a variety of methods have been developed that predict whether a protein-coding variant likely affects the carrier individual's health. We present such a method, DEOGEN2, which incorporates heterogeneous information about the molecular effects of the variants, the domains involved, the relevance of the gene and the interactions in which it participates. This extensive contextual information is non-linearly mapped into one single deleteriousness score for each variant. Since for the non-expert user it is sometimes still difficult to assess what this score means, how it relates to the encoded protein, and where it originates from, we developed an interactive online framework (http://deogen2.mutaframe.com/) to better present the DEOGEN2 deleteriousness predictions of all possible variants in all human proteins. The prediction is visualized so both expert and non-expert users can gain insights into the meaning, protein context and origins of each prediction.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, background): “Indeed, the performance of DEOGEN2 [54], which incorporates heterogeneous information such as the relevance of the gene and the number of protein interactions, is among the best (ROC AUC: 0.”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, unknown): “We therefore highlight SNAP2, DEOGEN2, SNPs&GO and SuSPect, which also tended to perform well against the DMS data sets, and have simple-to-use web interfaces.”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “1 added 18 deleteriousness prediction scores (BayesDel_addAF and 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…”