{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:02:32Z","timestamp":1778259752983,"version":"3.51.4"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T00:00:00Z","timestamp":1608508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2020YJ0490"],"award-info":[{"award-number":["2020YJ0490"]}]},{"DOI":"10.13039\/100017513","name":"Sichuan Association for Science and Technology","doi-asserted-by":"crossref","award":["2018RCTJ"],"award-info":[{"award-number":["2018RCTJ"]}],"id":[{"id":"10.13039\/100017513","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>FASTA and FASTQ are the most widely used biological data formats that have become the de facto standard to exchange sequence data between bioinformatics tools. With the avalanche of next-generation sequencing data, the amount of sequence data being deposited and accessed in FASTA\/Q formats is increasing dramatically. However, the existing tools have very low efficiency at random retrieval of subsequences due to the requirement of loading the entire index into memory. In addition, most existing tools have no capability to build index for large FASTA\/Q files because of the limited memory. Furthermore, the tools do not provide support to randomly accessing sequences from FASTA\/Q files compressed by gzip, which is extensively adopted by most public databases to compress data for saving storage. In this study, we developed pyfastx as a versatile Python package with commonly used command-line tools to overcome the above limitations. Compared to other tools, pyfastx yielded the highest performance in terms of building index and random access to sequences, particularly when dealing with large FASTA\/Q files with hundreds of millions of sequences. A key advantage of pyfastx over other tools is that it offers an efficient way to randomly extract subsequences directly from gzip compressed FASTA\/Q files without needing to uncompress beforehand. Pyfastx can easily be installed from PyPI (https:\/\/pypi.org\/project\/pyfastx) and the source code is freely available at https:\/\/github.com\/lmdu\/pyfastx.<\/jats:p>","DOI":"10.1093\/bib\/bbaa368","type":"journal-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T12:25:22Z","timestamp":1605702322000},"source":"Crossref","is-referenced-by-count":22,"title":["Pyfastx: a robust Python package for fast random access to sequences from plain and gzipped FASTA\/Q files"],"prefix":"10.1093","volume":"22","author":[{"given":"Lianming","family":"Du","sequence":"first","affiliation":[{"name":"Institute for Advanced Study, Chengdu University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Life Sciences and Food Engineering, Yibin University, Yibin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenxin","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Institute for Advanced Study, Chengdu University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Megan","family":"Price","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bisong","family":"Yue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bio-Resources and Eco-Environment, Ministry of Education, College of Life Science, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute for Advanced Study, Chengdu University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"issue":"19","key":"2021072117013385600_ref1","doi-asserted-by":"crossref","first-page":"3547","DOI":"10.1093\/bioinformatics\/btz272","article-title":"Evolution of biosequence search algorithms: a brief 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