{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T14:11:20Z","timestamp":1692281480615},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003478","name":"Ministry of Health, Labour and Welfare","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003478","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"Optimizing and automating a protocol for 16S microbiome data analysis with QIIME2 is a challenging task. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.<\/jats:p>","DOI":"10.3389\/fbinf.2022.893933","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T07:34:14Z","timestamp":1656660854000},"update-policy":"http:\/\/dx.doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Snaq: A Dynamic Snakemake Pipeline for Microbiome Data Analysis With QIIME2"],"prefix":"10.3389","volume":"2","author":[{"given":"Attayeb","family":"Mohsen","sequence":"first","affiliation":[]},{"given":"Yi-An","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Rodolfo S.","family":"Allendes Osorio","sequence":"additional","affiliation":[]},{"given":"Chihiro","family":"Higuchi","sequence":"additional","affiliation":[]},{"given":"Kenji","family":"Mizuguchi","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"W537","DOI":"10.1093\/nar\/gky379","article-title":"The Galaxy Platform for Accessible, Reproducible and Collaborative Biomedical Analyses: 2018 Update","volume":"46","author":"Afgan","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"B2","unstructured":"Anaconda Software Distribution2020"},{"key":"B3","unstructured":"Fastqc: A Quality Control Tool for High Throughput Sequence Data\n AndrewsS.\n 2010"},{"key":"B4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s40168-018-0470-z","article-title":"Optimizing Taxonomic Classification of Marker-Gene Amplicon Sequences with QIIME 2's Q2-Feature-Classifier Plugin","volume":"6","author":"Bokulich","year":"","journal-title":"Microbiome"},{"key":"B5","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1186\/s40168-018-0470-z","article-title":"Optimizing Taxonomic Classification of Marker-Gene Amplicon Sequences with QIIME 2's Q2-Feature-Classifier Plugin","volume":"6","author":"Bokulich","year":"","journal-title":"Microbiome"},{"key":"B6","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s41587-019-0209-9","article-title":"Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2","volume":"37","author":"Bolyen","year":"2019","journal-title":"Nat. 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