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NGSQC: cross-platform quality analysis pipeline for deep sequencing data
BACKGROUND: While the accuracy and precision of deep sequencing data is significantly better than those obtained by the earlier generation of hybridization-based high throughput technologies, the digital nature of deep sequencing output often leads to unwarranted confidence in their reliability. RES...
Autores principales: | , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005923/ https://www.ncbi.nlm.nih.gov/pubmed/21143816 http://dx.doi.org/10.1186/1471-2164-11-S4-S7 |
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author | Dai, Manhong Thompson, Robert C Maher, Christopher Contreras-Galindo, Rafael Kaplan, Mark H Markovitz, David M Omenn, Gil Meng, Fan |
author_facet | Dai, Manhong Thompson, Robert C Maher, Christopher Contreras-Galindo, Rafael Kaplan, Mark H Markovitz, David M Omenn, Gil Meng, Fan |
author_sort | Dai, Manhong |
collection | PubMed |
description | BACKGROUND: While the accuracy and precision of deep sequencing data is significantly better than those obtained by the earlier generation of hybridization-based high throughput technologies, the digital nature of deep sequencing output often leads to unwarranted confidence in their reliability. RESULTS: The NGSQC (Next Generation Sequencing Quality Control) pipeline provides a set of novel quality control measures for quickly detecting a wide variety of quality issues in deep sequencing data derived from two dimensional surfaces, regardless of the assay technology used. It also enables researchers to determine whether sequencing data related to their most interesting biological discoveries are caused by sequencing quality issues. CONCLUSIONS: Next generation sequencing platforms have their own share of quality issues and there can be significant lab-to-lab, batch-to-batch and even within chip/slide variations. NGSQC can help to ensure that biological conclusions, in particular those based on relatively rare sequence alterations, are not caused by low quality sequencing. |
format | Text |
id | pubmed-3005923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30059232010-12-22 NGSQC: cross-platform quality analysis pipeline for deep sequencing data Dai, Manhong Thompson, Robert C Maher, Christopher Contreras-Galindo, Rafael Kaplan, Mark H Markovitz, David M Omenn, Gil Meng, Fan BMC Genomics Proceedings BACKGROUND: While the accuracy and precision of deep sequencing data is significantly better than those obtained by the earlier generation of hybridization-based high throughput technologies, the digital nature of deep sequencing output often leads to unwarranted confidence in their reliability. RESULTS: The NGSQC (Next Generation Sequencing Quality Control) pipeline provides a set of novel quality control measures for quickly detecting a wide variety of quality issues in deep sequencing data derived from two dimensional surfaces, regardless of the assay technology used. It also enables researchers to determine whether sequencing data related to their most interesting biological discoveries are caused by sequencing quality issues. CONCLUSIONS: Next generation sequencing platforms have their own share of quality issues and there can be significant lab-to-lab, batch-to-batch and even within chip/slide variations. NGSQC can help to ensure that biological conclusions, in particular those based on relatively rare sequence alterations, are not caused by low quality sequencing. BioMed Central 2010-12-02 /pmc/articles/PMC3005923/ /pubmed/21143816 http://dx.doi.org/10.1186/1471-2164-11-S4-S7 Text en Copyright ©2010 Dai 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 | Proceedings Dai, Manhong Thompson, Robert C Maher, Christopher Contreras-Galindo, Rafael Kaplan, Mark H Markovitz, David M Omenn, Gil Meng, Fan NGSQC: cross-platform quality analysis pipeline for deep sequencing data |
title | NGSQC: cross-platform quality analysis pipeline for deep sequencing data |
title_full | NGSQC: cross-platform quality analysis pipeline for deep sequencing data |
title_fullStr | NGSQC: cross-platform quality analysis pipeline for deep sequencing data |
title_full_unstemmed | NGSQC: cross-platform quality analysis pipeline for deep sequencing data |
title_short | NGSQC: cross-platform quality analysis pipeline for deep sequencing data |
title_sort | ngsqc: cross-platform quality analysis pipeline for deep sequencing data |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005923/ https://www.ncbi.nlm.nih.gov/pubmed/21143816 http://dx.doi.org/10.1186/1471-2164-11-S4-S7 |
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