| Literature DB >> 23013645 |
Alejandro Sifrim1, Jeroen Kj Van Houdt2, Leon-Charles Tranchevent1, Beata Nowakowska2, Ryo Sakai1, Georgios A Pavlopoulos1, Koen Devriendt2, Joris R Vermeesch2, Yves Moreau1, Jan Aerts1.
Abstract
The increasing size and complexity of exome/genome sequencing data requires new tools for clinical geneticists to discover disease-causing variants. Bottlenecks in identifying the causative variation include poor cross-sample querying, constantly changing functional annotation and not considering existing knowledge concerning the phenotype. We describe a methodology that facilitates exploration of patient sequencing data towards identification of causal variants under different genetic hypotheses. Annotate-it facilitates handling, analysis and interpretation of high-throughput single nucleotide variant data. We demonstrate our strategy using three case studies. Annotate-it is freely available and test data are accessible to all users at http://www.annotate-it.org.Entities:
Year: 2012 PMID: 23013645 PMCID: PMC3580443 DOI: 10.1186/gm374
Source DB: PubMed Journal: Genome Med ISSN: 1756-994X Impact factor: 11.117