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Productive visualization of high-throughput sequencing data using the SeqCode open portable platform
Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486768/ https://www.ncbi.nlm.nih.gov/pubmed/34599234 http://dx.doi.org/10.1038/s41598-021-98889-7 |
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author | Blanco, Enrique González-Ramírez, Mar Di Croce, Luciano |
author_facet | Blanco, Enrique González-Ramírez, Mar Di Croce, Luciano |
author_sort | Blanco, Enrique |
collection | PubMed |
description | Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode. |
format | Online Article Text |
id | pubmed-8486768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84867682021-10-04 Productive visualization of high-throughput sequencing data using the SeqCode open portable platform Blanco, Enrique González-Ramírez, Mar Di Croce, Luciano Sci Rep Article Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode. Nature Publishing Group UK 2021-10-01 /pmc/articles/PMC8486768/ /pubmed/34599234 http://dx.doi.org/10.1038/s41598-021-98889-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Blanco, Enrique González-Ramírez, Mar Di Croce, Luciano Productive visualization of high-throughput sequencing data using the SeqCode open portable platform |
title | Productive visualization of high-throughput sequencing data using the SeqCode open portable platform |
title_full | Productive visualization of high-throughput sequencing data using the SeqCode open portable platform |
title_fullStr | Productive visualization of high-throughput sequencing data using the SeqCode open portable platform |
title_full_unstemmed | Productive visualization of high-throughput sequencing data using the SeqCode open portable platform |
title_short | Productive visualization of high-throughput sequencing data using the SeqCode open portable platform |
title_sort | productive visualization of high-throughput sequencing data using the seqcode open portable platform |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486768/ https://www.ncbi.nlm.nih.gov/pubmed/34599234 http://dx.doi.org/10.1038/s41598-021-98889-7 |
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