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Bioinformatics for Next Generation Sequencing Data
The emergence of next-generation sequencing (NGS) platforms imposes increasing demands on statistical methods and bioinformatic tools for the analysis and the management of the huge amounts of data generated by these technologies. Even at the early stages of their commercial availability, a large nu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954090/ https://www.ncbi.nlm.nih.gov/pubmed/24710047 http://dx.doi.org/10.3390/genes1020294 |
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author | Magi, Alberto Benelli, Matteo Gozzini, Alessia Girolami, Francesca Torricelli, Francesca Brandi, Maria Luisa |
author_facet | Magi, Alberto Benelli, Matteo Gozzini, Alessia Girolami, Francesca Torricelli, Francesca Brandi, Maria Luisa |
author_sort | Magi, Alberto |
collection | PubMed |
description | The emergence of next-generation sequencing (NGS) platforms imposes increasing demands on statistical methods and bioinformatic tools for the analysis and the management of the huge amounts of data generated by these technologies. Even at the early stages of their commercial availability, a large number of softwares already exist for analyzing NGS data. These tools can be fit into many general categories including alignment of sequence reads to a reference, base-calling and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection and genome browsing. This manuscript aims to guide readers in the choice of the available computational tools that can be used to face the several steps of the data analysis workflow. |
format | Online Article Text |
id | pubmed-3954090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-39540902014-03-26 Bioinformatics for Next Generation Sequencing Data Magi, Alberto Benelli, Matteo Gozzini, Alessia Girolami, Francesca Torricelli, Francesca Brandi, Maria Luisa Genes (Basel) Review The emergence of next-generation sequencing (NGS) platforms imposes increasing demands on statistical methods and bioinformatic tools for the analysis and the management of the huge amounts of data generated by these technologies. Even at the early stages of their commercial availability, a large number of softwares already exist for analyzing NGS data. These tools can be fit into many general categories including alignment of sequence reads to a reference, base-calling and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection and genome browsing. This manuscript aims to guide readers in the choice of the available computational tools that can be used to face the several steps of the data analysis workflow. MDPI 2010-09-14 /pmc/articles/PMC3954090/ /pubmed/24710047 http://dx.doi.org/10.3390/genes1020294 Text en © 2010 by the authors; licensee MDPI, Basel, Switzerland http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Magi, Alberto Benelli, Matteo Gozzini, Alessia Girolami, Francesca Torricelli, Francesca Brandi, Maria Luisa Bioinformatics for Next Generation Sequencing Data |
title | Bioinformatics for Next Generation Sequencing Data |
title_full | Bioinformatics for Next Generation Sequencing Data |
title_fullStr | Bioinformatics for Next Generation Sequencing Data |
title_full_unstemmed | Bioinformatics for Next Generation Sequencing Data |
title_short | Bioinformatics for Next Generation Sequencing Data |
title_sort | bioinformatics for next generation sequencing data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954090/ https://www.ncbi.nlm.nih.gov/pubmed/24710047 http://dx.doi.org/10.3390/genes1020294 |
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