GenomeGems: evaluation of genetic variability from deep sequencing data
<p>Abstract</p> <p>Background</p> <p>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,...
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| Format: | Article |
| Language: | English |
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BMC
2012-07-01
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| Series: | BMC Research Notes |
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| Online Access: | http://www.biomedcentral.com/1756-0500/5/338 |
| _version_ | 1818359080391016448 |
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| author | Ben-Zvi Sharon Givati Adi Shomron Noam |
| author_facet | Ben-Zvi Sharon Givati Adi Shomron Noam |
| author_sort | Ben-Zvi Sharon |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Findings</p> <p>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 <url>http://www.tau.ac.il/~nshomron/GenomeGems</url>.</p> <p>Conclusions</p> <p>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.</p> |
| first_indexed | 2024-12-13T20:39:12Z |
| format | Article |
| id | doaj.art-71c2353ad52841c0b865c67a445c6deb |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-12-13T20:39:12Z |
| publishDate | 2012-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-71c2353ad52841c0b865c67a445c6deb2022-12-21T23:32:12ZengBMCBMC Research Notes1756-05002012-07-015133810.1186/1756-0500-5-338GenomeGems: evaluation of genetic variability from deep sequencing dataBen-Zvi SharonGivati AdiShomron Noam<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Findings</p> <p>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 <url>http://www.tau.ac.il/~nshomron/GenomeGems</url>.</p> <p>Conclusions</p> <p>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.</p>http://www.biomedcentral.com/1756-0500/5/338Deep sequencingNext generation sequencingSoftwareGenetic analysisData interpretationVariance calling |
| spellingShingle | Ben-Zvi Sharon Givati Adi Shomron Noam GenomeGems: evaluation of genetic variability from deep sequencing data BMC Research Notes Deep sequencing Next generation sequencing Software Genetic analysis Data interpretation Variance calling |
| title | GenomeGems: evaluation of genetic variability from deep sequencing data |
| title_full | GenomeGems: evaluation of genetic variability from deep sequencing data |
| title_fullStr | GenomeGems: evaluation of genetic variability from deep sequencing data |
| title_full_unstemmed | GenomeGems: evaluation of genetic variability from deep sequencing data |
| title_short | GenomeGems: evaluation of genetic variability from deep sequencing data |
| title_sort | genomegems evaluation of genetic variability from deep sequencing data |
| topic | Deep sequencing Next generation sequencing Software Genetic analysis Data interpretation Variance calling |
| url | http://www.biomedcentral.com/1756-0500/5/338 |
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