Bioinformatics for Personal Genome Interpretation

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2012 Jan 17

PMID 22247263

Citations 43

Authors

Emidio Capriotti

Nathan L Nehrt

Maricel G Kann

Yana Bromberg

Affiliations

Soon will be listed here.

Abstract

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.

Citing Articles

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler.

Alvarez-Machancoses O, Faraggi E, deAndres-Galiana E, Fernandez-Martinez J, Kloczkowski A Curr Genomics. 2024; 25(3):171-184.

PMID: 39086995 PMC: 11288160. DOI: 10.2174/0113892029236347240308054538.

PhD-SNPg: updating a webserver and lightweight tool for scoring nucleotide variants.

Capriotti E, Fariselli P Nucleic Acids Res. 2023; 51(W1):W451-W458.

PMID: 37246737 PMC: 10320148. DOI: 10.1093/nar/gkad455.

Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.

Capriotti E, Fariselli P Hum Genet. 2022; 141(10):1649-1658.

PMID: 35098354 DOI: 10.1007/s00439-021-02419-4.

MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins.

Ge F, Zhu Y, Xu J, Muhammad A, Song J, Yu D Comput Struct Biotechnol J. 2021; 19:6400-6416.

PMID: 34938415 PMC: 8649221. DOI: 10.1016/j.csbj.2021.11.024.

Identification of driver genes based on gene mutational effects and network centrality.

Tang Y, Wei P, Zhao J, Xia J, Cao R, Zheng C BMC Bioinformatics. 2021; 22(Suppl 3):457.

PMID: 34560840 PMC: 8461858. DOI: 10.1186/s12859-021-04377-0.

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