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HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis
Use of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools a...
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/PMC8455540/ https://www.ncbi.nlm.nih.gov/pubmed/34548573 http://dx.doi.org/10.1038/s41598-021-98124-3 |
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author | Bedre, Renesh Avila, Carlos Mandadi, Kranthi |
author_facet | Bedre, Renesh Avila, Carlos Mandadi, Kranthi |
author_sort | Bedre, Renesh |
collection | PubMed |
description | Use of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Here, we developed HTSQualC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSQualC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSQualC for conducting batch analysis of HTS datasets with 322 samples with an average ~ 1 M (paired end) sequence reads per sample. HTSQualC accomplished the QC analysis in ~ 3 h in distributed mode and ~ 31 h in shared mode, thus underscoring its utility and robust performance. In addition to command-line execution, we integrated HTSQualC into the free, open-source, CyVerse cyberinfrastructure resource as a GUI interface, for wider access to experimental biologists who have limited computational resources and/or programming abilities. |
format | Online Article Text |
id | pubmed-8455540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84555402021-09-22 HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis Bedre, Renesh Avila, Carlos Mandadi, Kranthi Sci Rep Article Use of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Here, we developed HTSQualC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSQualC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSQualC for conducting batch analysis of HTS datasets with 322 samples with an average ~ 1 M (paired end) sequence reads per sample. HTSQualC accomplished the QC analysis in ~ 3 h in distributed mode and ~ 31 h in shared mode, thus underscoring its utility and robust performance. In addition to command-line execution, we integrated HTSQualC into the free, open-source, CyVerse cyberinfrastructure resource as a GUI interface, for wider access to experimental biologists who have limited computational resources and/or programming abilities. Nature Publishing Group UK 2021-09-21 /pmc/articles/PMC8455540/ /pubmed/34548573 http://dx.doi.org/10.1038/s41598-021-98124-3 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 Bedre, Renesh Avila, Carlos Mandadi, Kranthi HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis |
title | HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis |
title_full | HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis |
title_fullStr | HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis |
title_full_unstemmed | HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis |
title_short | HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis |
title_sort | htsqualc is a flexible and one-step quality control software for high-throughput sequencing data analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455540/ https://www.ncbi.nlm.nih.gov/pubmed/34548573 http://dx.doi.org/10.1038/s41598-021-98124-3 |
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