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Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data
The Meningococcus Genome Informatics Platform (MGIP) is a suite of computational tools for the analysis of multilocus sequence typing (MLST) data, at http://mgip.biology.gatech.edu. MLST is used to generate allelic profiles to characterize strains of Neisseria meningitidis, a major cause of bacteria...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703879/ https://www.ncbi.nlm.nih.gov/pubmed/19468047 http://dx.doi.org/10.1093/nar/gkp288 |
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author | Katz, Lee S. Bolen, Chris R. Harcourt, Brian H. Schmink, Susanna Wang, Xin Kislyuk, Andrey Taylor, Robert T. Mayer, Leonard W. Jordan, I. King |
author_facet | Katz, Lee S. Bolen, Chris R. Harcourt, Brian H. Schmink, Susanna Wang, Xin Kislyuk, Andrey Taylor, Robert T. Mayer, Leonard W. Jordan, I. King |
author_sort | Katz, Lee S. |
collection | PubMed |
description | The Meningococcus Genome Informatics Platform (MGIP) is a suite of computational tools for the analysis of multilocus sequence typing (MLST) data, at http://mgip.biology.gatech.edu. MLST is used to generate allelic profiles to characterize strains of Neisseria meningitidis, a major cause of bacterial meningitis worldwide. Neisseria meningitidis strains are characterized with MLST as specific sequence types (ST) and clonal complexes (CC) based on the DNA sequences at defined loci. These data are vital to molecular epidemiology studies of N. meningitidis, including outbreak investigations and population biology. MGIP analyzes DNA sequence trace files, returns individual allele calls and characterizes the STs and CCs. MGIP represents a substantial advance over existing software in several respects: (i) ease of use—MGIP is user friendly, intuitive and thoroughly documented; (ii) flexibility—because MGIP is a website, it is compatible with any computer with an internet connection, can be used from any geographic location, and there is no installation; (iii) speed—MGIP takes just over one minute to process a set of 96 trace files; and (iv) expandability—MGIP has the potential to expand to more loci than those used in MLST and even to other bacterial species. |
format | Text |
id | pubmed-2703879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-27038792009-07-01 Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data Katz, Lee S. Bolen, Chris R. Harcourt, Brian H. Schmink, Susanna Wang, Xin Kislyuk, Andrey Taylor, Robert T. Mayer, Leonard W. Jordan, I. King Nucleic Acids Res Articles The Meningococcus Genome Informatics Platform (MGIP) is a suite of computational tools for the analysis of multilocus sequence typing (MLST) data, at http://mgip.biology.gatech.edu. MLST is used to generate allelic profiles to characterize strains of Neisseria meningitidis, a major cause of bacterial meningitis worldwide. Neisseria meningitidis strains are characterized with MLST as specific sequence types (ST) and clonal complexes (CC) based on the DNA sequences at defined loci. These data are vital to molecular epidemiology studies of N. meningitidis, including outbreak investigations and population biology. MGIP analyzes DNA sequence trace files, returns individual allele calls and characterizes the STs and CCs. MGIP represents a substantial advance over existing software in several respects: (i) ease of use—MGIP is user friendly, intuitive and thoroughly documented; (ii) flexibility—because MGIP is a website, it is compatible with any computer with an internet connection, can be used from any geographic location, and there is no installation; (iii) speed—MGIP takes just over one minute to process a set of 96 trace files; and (iv) expandability—MGIP has the potential to expand to more loci than those used in MLST and even to other bacterial species. Oxford University Press 2009-07-01 2009-05-25 /pmc/articles/PMC2703879/ /pubmed/19468047 http://dx.doi.org/10.1093/nar/gkp288 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Katz, Lee S. Bolen, Chris R. Harcourt, Brian H. Schmink, Susanna Wang, Xin Kislyuk, Andrey Taylor, Robert T. Mayer, Leonard W. Jordan, I. King Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
title | Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
title_full | Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
title_fullStr | Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
title_full_unstemmed | Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
title_short | Meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
title_sort | meningococcus genome informatics platform: a system for analyzing multilocus sequence typing data |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703879/ https://www.ncbi.nlm.nih.gov/pubmed/19468047 http://dx.doi.org/10.1093/nar/gkp288 |
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