GenomeGems: Evaluation of genetic variability from deep sequencing data

Sharon Ben-Zvi, Adi Givati, Noam Shomron*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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. 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.

Original languageEnglish
Article number338
JournalBMC Research Notes
StatePublished - 2012


FundersFunder number
Wolfson Family Charitable Fund
National Institutes of Health
National Institute on Deafness and Other Communication DisordersR01DC011835
Israel Cancer Association
Israel Science Foundation41/11
Ministry of Health, State of Israel


    • Data interpretation
    • Deep sequencing
    • Genetic analysis
    • Next generation sequencing
    • Software
    • Variance calling


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