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Current challenges and best-practice protocols for microbiome analysis
Abstract Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by expe...
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Published in: | Briefings in bioinformatics 2021-01, Vol.22 (1), p.178-193 |
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description | Abstract
Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview). |
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Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).</description><identifier>ISSN: 1477-4054</identifier><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbz155</identifier><identifier>PMID: 31848574</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Animals ; Annotations ; Assembly ; Computer applications ; Design of experiments ; DNA Barcoding, Taxonomic - methods ; DNA Barcoding, Taxonomic - standards ; Experimental design ; Humans ; Metagenomics ; Metagenomics - methods ; Metagenomics - standards ; Microbiomes ; Microbiota - genetics ; Microorganisms ; Next-generation sequencing ; Practice Guidelines as Topic ; Quality control ; Review ; RNA, Ribosomal, 16S - genetics ; rRNA 16S ; Sequence Analysis, DNA - methods ; Sequence Analysis, DNA - standards ; Species diversity ; Statistical analysis</subject><ispartof>Briefings in bioinformatics, 2021-01, Vol.22 (1), p.178-193</ispartof><rights>The Author(s) 2019. Published by Oxford University Press. 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c543t-3830b55c24d465327135505d95535ed815a670dcaaba243c93279251e87278903</citedby><cites>FETCH-LOGICAL-c543t-3830b55c24d465327135505d95535ed815a670dcaaba243c93279251e87278903</cites><orcidid>0000-0003-2085-4591</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820839/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820839/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,1598,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31848574$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bharti, Richa</creatorcontrib><creatorcontrib>Grimm, Dominik G</creatorcontrib><title>Current challenges and best-practice protocols for microbiome analysis</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. 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Grimm, Dominik G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c543t-3830b55c24d465327135505d95535ed815a670dcaaba243c93279251e87278903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Animals</topic><topic>Annotations</topic><topic>Assembly</topic><topic>Computer applications</topic><topic>Design of experiments</topic><topic>DNA Barcoding, Taxonomic - methods</topic><topic>DNA Barcoding, Taxonomic - standards</topic><topic>Experimental design</topic><topic>Humans</topic><topic>Metagenomics</topic><topic>Metagenomics - methods</topic><topic>Metagenomics - standards</topic><topic>Microbiomes</topic><topic>Microbiota - genetics</topic><topic>Microorganisms</topic><topic>Next-generation sequencing</topic><topic>Practice Guidelines as Topic</topic><topic>Quality control</topic><topic>Review</topic><topic>RNA, Ribosomal, 16S - genetics</topic><topic>rRNA 16S</topic><topic>Sequence Analysis, DNA - methods</topic><topic>Sequence Analysis, DNA - standards</topic><topic>Species diversity</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bharti, Richa</creatorcontrib><creatorcontrib>Grimm, Dominik G</creatorcontrib><collection>Oxford Open</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bharti, Richa</au><au>Grimm, Dominik G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Current challenges and best-practice protocols for microbiome analysis</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2021-01-18</date><risdate>2021</risdate><volume>22</volume><issue>1</issue><spage>178</spage><epage>193</epage><pages>178-193</pages><issn>1477-4054</issn><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>Abstract
Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>31848574</pmid><doi>10.1093/bib/bbz155</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-2085-4591</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Animals Annotations Assembly Computer applications Design of experiments DNA Barcoding, Taxonomic - methods DNA Barcoding, Taxonomic - standards Experimental design Humans Metagenomics Metagenomics - methods Metagenomics - standards Microbiomes Microbiota - genetics Microorganisms Next-generation sequencing Practice Guidelines as Topic Quality control Review RNA, Ribosomal, 16S - genetics rRNA 16S Sequence Analysis, DNA - methods Sequence Analysis, DNA - standards Species diversity Statistical analysis |
title | Current challenges and best-practice protocols for microbiome analysis |
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