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Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast
BACKGROUND: In analyzing the stability of DNA replication origins in Saccharomyces cerevisiae we faced the question whether one set of sequences is significantly enriched in the number and/or the quality of the matches of a particular position weight matrix relative to another set. RESULTS: We prese...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2566582/ https://www.ncbi.nlm.nih.gov/pubmed/18786274 http://dx.doi.org/10.1186/1471-2105-9-372 |
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author | Keich, Uri Gao, Hong Garretson, Jeffrey S Bhaskar, Anand Liachko, Ivan Donato, Justin Tye, Bik K |
author_facet | Keich, Uri Gao, Hong Garretson, Jeffrey S Bhaskar, Anand Liachko, Ivan Donato, Justin Tye, Bik K |
author_sort | Keich, Uri |
collection | PubMed |
description | BACKGROUND: In analyzing the stability of DNA replication origins in Saccharomyces cerevisiae we faced the question whether one set of sequences is significantly enriched in the number and/or the quality of the matches of a particular position weight matrix relative to another set. RESULTS: We present SADMAMA, a computational solution to a address this problem. SADMAMA implements two types of statistical tests to answer this question: one type is based on simplified models, while the other relies on bootstrapping, and as such might be preferable to users who are averse to such models. The bootstrap approach incorporates a novel "site-protected" resampling procedure which solves a problem we identify with naive resampling. CONCLUSION: SADMAMA's utility is demonstrated here by offering a plausible explanation to the differential ARS activity observed in our previous mcm1-1 mutant experiments [1], by suggesting the relevance of multiple weak ACS matches to efficient replication origin function in Saccharomyces cerevisiae, and by suggesting an explanation to the observed negative effect FKH2 has on chromatin silencing [2]. SADMAMA is available for download from . |
format | Text |
id | pubmed-2566582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-25665822008-10-14 Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast Keich, Uri Gao, Hong Garretson, Jeffrey S Bhaskar, Anand Liachko, Ivan Donato, Justin Tye, Bik K BMC Bioinformatics Research Article BACKGROUND: In analyzing the stability of DNA replication origins in Saccharomyces cerevisiae we faced the question whether one set of sequences is significantly enriched in the number and/or the quality of the matches of a particular position weight matrix relative to another set. RESULTS: We present SADMAMA, a computational solution to a address this problem. SADMAMA implements two types of statistical tests to answer this question: one type is based on simplified models, while the other relies on bootstrapping, and as such might be preferable to users who are averse to such models. The bootstrap approach incorporates a novel "site-protected" resampling procedure which solves a problem we identify with naive resampling. CONCLUSION: SADMAMA's utility is demonstrated here by offering a plausible explanation to the differential ARS activity observed in our previous mcm1-1 mutant experiments [1], by suggesting the relevance of multiple weak ACS matches to efficient replication origin function in Saccharomyces cerevisiae, and by suggesting an explanation to the observed negative effect FKH2 has on chromatin silencing [2]. SADMAMA is available for download from . BioMed Central 2008-09-12 /pmc/articles/PMC2566582/ /pubmed/18786274 http://dx.doi.org/10.1186/1471-2105-9-372 Text en Copyright © 2008 Keich et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Keich, Uri Gao, Hong Garretson, Jeffrey S Bhaskar, Anand Liachko, Ivan Donato, Justin Tye, Bik K Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
title | Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
title_full | Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
title_fullStr | Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
title_full_unstemmed | Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
title_short | Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
title_sort | computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2566582/ https://www.ncbi.nlm.nih.gov/pubmed/18786274 http://dx.doi.org/10.1186/1471-2105-9-372 |
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