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Updated: May 20, 2026
09:34
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
GenomeGems: evaluation of genetic variability from deep sequencing data
Sharon Ben-Zvi1, Adi Givati, Noam Shomron
1Department of Biomedical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Tel Aviv, Israel.
BMC Research Notes
|July 4, 2012
Summary
GenomeGems facilitates the identification of disease-causing Single Nucleotide Polymorphisms (SNPs) from deep sequencing data. This tool aids researchers in efficiently comparing and visualizing SNP data for further validation.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Deep sequencing technologies generate vast amounts of data, posing challenges for identifying disease-causing mutations.
- Limited automated tools exist for organizing and analyzing large-scale sequencing data to pinpoint biologically relevant mutations.
Purpose of the Study:
- To develop an accessible tool for organizing and visualizing genetic variations from deep sequencing data.
- To facilitate the identification and ranking of Single Nucleotide Polymorphisms (SNPs) associated with diseases.
Main Methods:
- Development of GenomeGems software for local PC use.
- Enables viewing and comparison of SNPs from multiple datasets.
- Integration with the UCSC Genome Browser for enhanced visualization.
Main Results:
- GenomeGems provides automatic, clear, and accessible presentation of processed deep sequencing data.
- Facilitates the ranking of genomic SNP calling.
- Allows users to compare and visualize SNPs across multiple experiments.
Conclusions:
- GenomeGems enhances the efficiency of identifying potential disease-causing SNPs.
- Accelerates the process of experimental SNP validation.
- Supports researchers in detailed analysis by loading SNP data onto the UCSC Genome Browser.
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