PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

PredictSNP2: Platforma pro přesné ohodnocení vlivu nukleotidového polymorfizmu využívající specifické charakteristiky variant podle genomických regionů

journal article

English

Bendl Jaroslav, Ing., Ph.D.
Musil Miloš, Ing., Ph.D. (DIFS)
Štourač Jan
Zendulka Jaroslav, doc. Ing., CSc. (UIFS)
Damborský Jiří, prof. Mgr., Dr. (UMEL)
Brezovský Jan

• http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004962

SNP effect; deleteriousness prediction; SNP prediction; mutation analysis;
Mendelian diseases

 An important message taken from human genome sequencing projects is that the
human population exhibits approximately 99.9% genetic similarity. Variations in
the remaining parts of the genome determine our identity, trace our history and
reveal our heritage. The precise delineation of phenotypically causal variants
plays a key role in providing accurate personalized diagnosis, prognosis, and
treatment of inherited diseases. Several computational methods for achieving such
delineation have been reported recently. However, their ability to pinpoint
potentially deleterious variants is limited by the fact that their mechanisms of
prediction do not account for the existence of different categories of variants.
Consequently, their output is biased towards the variant categories that are most
strongly represented in the variant databases. Moreover, most such methods
provide numeric scores but not binary predictions of the deleteriousness of
variants or confidence scores that would be more easily understood by users. We
have constructed three datasets covering different types of disease-related
variants, which were divided across five categories: (i) regulatory, (ii)
splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These
datasets were used to develop category-optimal decision thresholds and to
evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons,
FunSeq2 and GWAVA. This evaluation revealed some important advantages of the
category-based approach. The results obtained with the five best-performing tools
were then combined into a consensus score. Additional comparative analyses showed
that in the case of missense variations, protein-based predictors perform better
than DNA sequence-based predictors. A user-friendly web interface was developed
that provides easy access to the five tools predictions, and their consensus
scores, in a user-understandable format tailored to the specific features of
different categories of variations. To enable comprehensive evaluation of
variants, the predictions are complemented with annotations from eight databases.
The web server is freely available to the community at
http://loschmidt.chemi.muni.cz/predictsnp2.

2016

1–18

PLoS Computational Biology, vol. 12, no. 5, ISSN 1553-7358

10.1371/journal.pcbi.1004962

000379348100043

2-s2.0-84975801185