Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Jul;40(Web Server issue):W71-5.
doi: 10.1093/nar/gks474. Epub 2012 May 27.

KD4v: Comprehensible Knowledge Discovery System for Missense Variant

Affiliations
Free PMC article

KD4v: Comprehensible Knowledge Discovery System for Missense Variant

Tien-Dao Luu et al. Nucleic Acids Res. .
Free PMC article

Abstract

A major challenge in the post-genomic era is a better understanding of how human genetic alterations involved in disease affect the gene products. The KD4v (Comprehensible Knowledge Discovery System for Missense Variant) server allows to characterize and predict the phenotypic effects (deleterious/neutral) of missense variants. The server provides a set of rules learned by Induction Logic Programming (ILP) on a set of missense variants described by conservation, physico-chemical, functional and 3D structure predicates. These rules are interpretable by non-expert humans and are used to accurately predict the deleterious/neutral status of an unknown mutation. The web server is available at http://decrypthon.igbmc.fr/kd4v.

Figures

Figure 1.
Figure 1.
ILP rules. The first column provides a link to the positive (deleterious mutations) and negative (neutral mutations) examples covered by a given rule and that can be seen by clicking on the + icon. The second column provides the rule identifier (Id). The next two columns provide the ‘if’ and ‘then’ clauses of the induced rules. The two right most columns indicate the number of positive and negative examples covered by the rule in each row.
Figure 2.
Figure 2.
(a) Screenshot of the input form of the prediction service. (b) Screenshot of the output page providing the prediction results as well as the multi-level characterizations of the mutation. The rules are described if the variant is ‘deleterious’. The annotated information related to the mutated position can be visualized in the MSV3d interface on the right.

Similar articles

See all similar articles

Cited by 8 articles

See all "Cited by" articles

References

    1. Thusberg J, Olatubosun A, Vihinen M. Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat. 2011;32:358–368. - PubMed
    1. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812–3814. - PMC - PubMed
    1. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13:2129–2141. - PMC - PubMed
    1. Bromberg Y, Yachdav G, Rost B. SNAP predicts effect of mutations on protein function. Bioinformatics. 2008;24:2397–2398. - PMC - PubMed
    1. Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 2009;30:1237–1244. - PubMed

Publication types

Feedback