Better prediction of functional effects for sequence variants

By Maximilian Hecht, Yana Bromberg, Burkhard Rost. 2015

245 citations recorded by the source corpus.

Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web Delta, input feature that results from computing the difference feature scores for native amino acid and feature scores for variant amino acid; nsSNP, non-synoymous SNP; PMD, Protein Mutant Database; SNAP, Screening for non-acceptable polymorphisms; SNP, single nucleotide polymorphism; variant, any amino acid changing sequence variant.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics, Medical & Health AI

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, neutral): “Computational variant effect predictors are useful for assessing the effect of point mutations (Gray et al., 2018; Adzhubei et al., 2013; Kumar et al., 2009; Hecht et al., 2015; Rentzsch et al., 2018).”
Quantitative missense variant effect prediction using large-scale mutagenesis data (2017, positive): “Thus, variant effect predictors such as PolyPhen2 (Adzhubei et al., 2010), SIFT (Sim et al., 2012), SNAP2 (Hecht et al., 2015), Evolutionary Action (Katsonis and Lichtarge, 2014), CADD (Kircher et al., 2014), and a host of others (Tang and Thomas, 2016) will continue to be widely used to predict…”
Predicting Secretory Proteins with SignalP. (2017, unknown): “1 and some of the newer alternatives to SIFT, such as PolyPhen-2 [38], PON-P2 [39], SNAP2 [40], etc.”
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.”