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BMC Res Notes. 2012 Jul 2;5:338. doi: 10.1186/1756-0500-5-338.

GenomeGems: evaluation of genetic variability from deep sequencing data.

Author information

1
Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Tel Aviv, Israel.

Abstract

BACKGROUND:

Detection of disease-causing mutations using Deep Sequencing technologies possesses great challenges. In particular, organizing the great amount of sequences generated so that mutations, which might possibly be biologically relevant, are easily identified is a difficult task. Yet, for this assignment only limited automatic accessible tools exist.

FINDINGS:

We developed GenomeGems to gap this need by enabling the user to view and compare Single Nucleotide Polymorphisms (SNPs) from multiple datasets and to load the data onto the UCSC Genome Browser for an expanded and familiar visualization. As such, via automatic, clear and accessible presentation of processed Deep Sequencing data, our tool aims to facilitate ranking of genomic SNP calling. GenomeGems runs on a local Personal Computer (PC) and is freely available at http://www.tau.ac.il/~nshomron/GenomeGems.

CONCLUSIONS:

GenomeGems enables researchers to identify potential disease-causing SNPs in an efficient manner. This enables rapid turnover of information and leads to further experimental SNP validation. The tool allows the user to compare and visualize SNPs from multiple experiments and to easily load SNP data onto the UCSC Genome browser for further detailed information.

PMID:
22748151
PMCID:
PMC3499170
DOI:
10.1186/1756-0500-5-338
[Indexed for MEDLINE]
Free PMC Article

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