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SSRome: an integrated database and pipelines for exploring microsatellites in all organisms
Over the past decade, many databases focusing on microsatellite mining on a genomic scale were released online with at least one of the following major deficiencies: (i) lacking the classification of microsatellites as genic or non-genic, (ii) not comparing microsatellite motifs at both genic and no...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323889/ https://www.ncbi.nlm.nih.gov/pubmed/30365025 http://dx.doi.org/10.1093/nar/gky998 |
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author | Mokhtar, Morad M Atia, Mohamed A M |
author_facet | Mokhtar, Morad M Atia, Mohamed A M |
author_sort | Mokhtar, Morad M |
collection | PubMed |
description | Over the past decade, many databases focusing on microsatellite mining on a genomic scale were released online with at least one of the following major deficiencies: (i) lacking the classification of microsatellites as genic or non-genic, (ii) not comparing microsatellite motifs at both genic and non-genic levels in order to identify unique motifs for each class or (iii) missing SSR marker development. In this study, we have developed ‘SSRome’ as a web-based, user-friendly, comprehensive and dynamic database with pipelines for exploring microsatellites in 6533 organisms. In the SSRome database, 158 million microsatellite motifs are identified across all taxa, in addition to all the mitochondrial and chloroplast genomes and expressed sequence tags available from NCBI. Moreover, 45.1 million microsatellite markers were developed and classified as genic or non-genic. All the stored motif and marker datasets can be downloaded freely. In addition, SSRome provides three user-friendly tools to identify, classify and compare motifs on either a genome- or transcriptome-wide scale. With the implementation of PHP, HTML and JavaScript, users can upload their data for analysis via a user-friendly GUI. SSRome represents a powerful database and mega-tool that will assist researchers in developing and dissecting microsatellite markers on a high-throughput scale. |
format | Online Article Text |
id | pubmed-6323889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-63238892019-01-10 SSRome: an integrated database and pipelines for exploring microsatellites in all organisms Mokhtar, Morad M Atia, Mohamed A M Nucleic Acids Res Database Issue Over the past decade, many databases focusing on microsatellite mining on a genomic scale were released online with at least one of the following major deficiencies: (i) lacking the classification of microsatellites as genic or non-genic, (ii) not comparing microsatellite motifs at both genic and non-genic levels in order to identify unique motifs for each class or (iii) missing SSR marker development. In this study, we have developed ‘SSRome’ as a web-based, user-friendly, comprehensive and dynamic database with pipelines for exploring microsatellites in 6533 organisms. In the SSRome database, 158 million microsatellite motifs are identified across all taxa, in addition to all the mitochondrial and chloroplast genomes and expressed sequence tags available from NCBI. Moreover, 45.1 million microsatellite markers were developed and classified as genic or non-genic. All the stored motif and marker datasets can be downloaded freely. In addition, SSRome provides three user-friendly tools to identify, classify and compare motifs on either a genome- or transcriptome-wide scale. With the implementation of PHP, HTML and JavaScript, users can upload their data for analysis via a user-friendly GUI. SSRome represents a powerful database and mega-tool that will assist researchers in developing and dissecting microsatellite markers on a high-throughput scale. Oxford University Press 2019-01-08 2018-10-26 /pmc/articles/PMC6323889/ /pubmed/30365025 http://dx.doi.org/10.1093/nar/gky998 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Database Issue Mokhtar, Morad M Atia, Mohamed A M SSRome: an integrated database and pipelines for exploring microsatellites in all organisms |
title | SSRome: an integrated database and pipelines for exploring microsatellites in all organisms |
title_full | SSRome: an integrated database and pipelines for exploring microsatellites in all organisms |
title_fullStr | SSRome: an integrated database and pipelines for exploring microsatellites in all organisms |
title_full_unstemmed | SSRome: an integrated database and pipelines for exploring microsatellites in all organisms |
title_short | SSRome: an integrated database and pipelines for exploring microsatellites in all organisms |
title_sort | ssrome: an integrated database and pipelines for exploring microsatellites in all organisms |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323889/ https://www.ncbi.nlm.nih.gov/pubmed/30365025 http://dx.doi.org/10.1093/nar/gky998 |
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