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Repliscan: a tool for classifying replication timing regions
BACKGROUND: Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547489/ https://www.ncbi.nlm.nih.gov/pubmed/28784090 http://dx.doi.org/10.1186/s12859-017-1774-x |
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author | Zynda, Gregory J. Song, Jawon Concia, Lorenzo Wear, Emily E. Hanley-Bowdoin, Linda Thompson, William F. Vaughn, Matthew W. |
author_facet | Zynda, Gregory J. Song, Jawon Concia, Lorenzo Wear, Emily E. Hanley-Bowdoin, Linda Thompson, William F. Vaughn, Matthew W. |
author_sort | Zynda, Gregory J. |
collection | PubMed |
description | BACKGROUND: Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. RESULTS: To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. CONCLUSIONS: Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage. |
format | Online Article Text |
id | pubmed-5547489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55474892017-08-09 Repliscan: a tool for classifying replication timing regions Zynda, Gregory J. Song, Jawon Concia, Lorenzo Wear, Emily E. Hanley-Bowdoin, Linda Thompson, William F. Vaughn, Matthew W. BMC Bioinformatics Software BACKGROUND: Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. RESULTS: To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. CONCLUSIONS: Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage. BioMed Central 2017-08-07 /pmc/articles/PMC5547489/ /pubmed/28784090 http://dx.doi.org/10.1186/s12859-017-1774-x Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Zynda, Gregory J. Song, Jawon Concia, Lorenzo Wear, Emily E. Hanley-Bowdoin, Linda Thompson, William F. Vaughn, Matthew W. Repliscan: a tool for classifying replication timing regions |
title | Repliscan: a tool for classifying replication timing regions |
title_full | Repliscan: a tool for classifying replication timing regions |
title_fullStr | Repliscan: a tool for classifying replication timing regions |
title_full_unstemmed | Repliscan: a tool for classifying replication timing regions |
title_short | Repliscan: a tool for classifying replication timing regions |
title_sort | repliscan: a tool for classifying replication timing regions |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547489/ https://www.ncbi.nlm.nih.gov/pubmed/28784090 http://dx.doi.org/10.1186/s12859-017-1774-x |
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