ARA: a flexible pipeline for automated exploration of NCBI SRA datasets.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES GigaScience Pub Date : 2022-12-28 Epub Date: 2023-08-17 DOI:10.1093/gigascience/giad067
Anand Maurya, Maciej Szymanski, Wojciech M Karlowski
{"title":"ARA: a flexible pipeline for automated exploration of NCBI SRA datasets.","authors":"Anand Maurya, Maciej Szymanski, Wojciech M Karlowski","doi":"10.1093/gigascience/giad067","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>One of the most effective and useful methods to explore the content of biological databases is searching with nucleotide or protein sequences as a query. However, especially in the case of nucleic acids, due to the large volume of data generated by the next-generation sequencing (NGS) technologies, this approach is often not available. The hierarchical organization of the NGS records is primarily designed for browsing or text-based searches of the information provided in metadata-related keywords, limiting the efficiency of database exploration.</p><p><strong>Findings: </strong>We developed an automated pipeline that incorporates the well-established NGS data-processing tools and procedures to allow easy and effective sampling of the NCBI SRA database records. Given a file with query nucleotide sequences, our tool estimates the matching content of SRA accessions by probing only a user-defined fraction of a record's sequences. Based on the selected parameters, it allows performing a full mapping experiment with records that meet the required criteria. The pipeline is designed to be easy to operate-it offers a fully automatic setup procedure and is fixed on tested supporting tools. The modular design and implemented usage modes allow a user to scale up the analyses into complex computational infrastructure.</p><p><strong>Conclusions: </strong>We present an easy-to-operate and automated tool that expands the way a user can access and explore the information contained within the records deposited in the NCBI SRA database.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":null,"pages":null},"PeriodicalIF":11.8000,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433097/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GigaScience","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/gigascience/giad067","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: One of the most effective and useful methods to explore the content of biological databases is searching with nucleotide or protein sequences as a query. However, especially in the case of nucleic acids, due to the large volume of data generated by the next-generation sequencing (NGS) technologies, this approach is often not available. The hierarchical organization of the NGS records is primarily designed for browsing or text-based searches of the information provided in metadata-related keywords, limiting the efficiency of database exploration.

Findings: We developed an automated pipeline that incorporates the well-established NGS data-processing tools and procedures to allow easy and effective sampling of the NCBI SRA database records. Given a file with query nucleotide sequences, our tool estimates the matching content of SRA accessions by probing only a user-defined fraction of a record's sequences. Based on the selected parameters, it allows performing a full mapping experiment with records that meet the required criteria. The pipeline is designed to be easy to operate-it offers a fully automatic setup procedure and is fixed on tested supporting tools. The modular design and implemented usage modes allow a user to scale up the analyses into complex computational infrastructure.

Conclusions: We present an easy-to-operate and automated tool that expands the way a user can access and explore the information contained within the records deposited in the NCBI SRA database.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ARA:自动探索 NCBI SRA 数据集的灵活管道。
背景:探索生物数据库内容最有效、最有用的方法之一是以核苷酸或蛋白质序列作为查询条件进行搜索。然而,特别是在核酸方面,由于新一代测序(NGS)技术产生了大量数据,这种方法往往无法使用。NGS 记录的分层组织主要是为浏览或基于文本搜索元数据相关关键词所提供的信息而设计的,这限制了数据库探索的效率:我们开发了一个自动化管道,它结合了成熟的 NGS 数据处理工具和程序,可以轻松有效地对 NCBI SRA 数据库记录进行采样。给定一个包含查询核苷酸序列的文件,我们的工具只探测用户定义的部分记录序列,从而估算出 SRA 记录的匹配内容。根据所选参数,它可以对符合所需标准的记录进行完整的映射实验。该管道的设计易于操作--提供全自动设置程序,并固定在经过测试的支持工具上。模块化设计和使用模式允许用户将分析扩展到复杂的计算基础设施中:我们介绍了一种易于操作的自动化工具,它扩展了用户访问和探索美国国家生物与基因组研究中心(NCBI)SRA数据库中记录信息的方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
期刊最新文献
IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants An effective strategy for assembling the sex-limited chromosome Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1