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Yet, all too often, nuances that are critical for an experiment’s success are not captured in the primary literature but exist only as part of a lab’s oral tradition. The aim of each Current Protocols title is to provide the clearest, most detailed and reliable step-by-step instructions for all protocols relevant to a given subject area. Written by experts in the field and extensively edited to our exacting standards, the protocols include all of the information necessary to complete an experiment in the laboratory—introduction, resources lists, detailed step-by-step procedures with helpful annotations, illustrative figures and information-packed tables. Each article also provides invaluable discussions of background information, applications of the methods, important assumptions, key parameters, time considerations, and tips to help avoid common pitfalls and troubleshoot experiments. 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Current Protocols in Bioinformatics is the comprehensive resource for all types of protocols in bioinformatics, enabling the life scientist to become \"fluent\" in bioinformatics and master the exciting new frontiers opened up by the advent of large-scale, genome-wide, high-throughput technologies.",{"VOID":19},"1934340X",{"EN":21},"Current Protocols in Bioinformatics","PUBLISHER","PENDING",null,0,[27,35,43],{"id":28,"createTime":29,"updateTime":30,"relativeEntities":31,"label":32,"description":34,"parentId":24,"standard":24,"scholarHubFieldId":24},"e601f748-4aab-4948-8adb-900da408c391","2023-05-29T10:24:27.916+00:00","2023-11-21T07:35:37.264+00:00",[],{"EN":33},"Structural Biology",{},{"id":36,"createTime":37,"updateTime":38,"relativeEntities":39,"label":40,"description":42,"parentId":24,"standard":24,"scholarHubFieldId":24},"73d2c682-bae9-48e0-a003-8e0ed8c9715d","2023-05-29T10:24:27.365+00:00","2023-11-21T07:48:23.815+00:00",[],{"EN":41},"Biochemistry",{},{"id":44,"createTime":45,"updateTime":46,"relativeEntities":47,"label":48,"description":50,"parentId":24,"standard":24,"scholarHubFieldId":24},"0254e3e5-c28d-4a33-83da-81bc94d29ffd","2023-05-29T10:24:00.931+00:00","2023-11-21T07:56:41.438+00:00",[],{"EN":49},"Medicine (miscellaneous)",{},[52],{"id":53,"createTime":54,"updateTime":55,"relativeEntities":56,"slug":57,"properties":58,"entityType":61,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":62,"url":24,"parentIds":63,"statistic":24},"2ea55c76-ecc2-4e81-94b5-baabc3f11863","2023-05-29T10:24:16.606+00:00","2024-02-20T15:28:41.845+00:00",[],"John-Wiley-Sons-Inc-",{"title":59},{"EN":60},"John Wiley & Sons Inc.","AFFILIATION",8,[],[65],{"id":66,"indexDatabase":67,"url":79,"indexYears":80,"academicFieldIds":81,"indexDatabaseRanking":85},"0bbf5184-3727-4414-96e6-b7491b6ac681",{"id":68,"createTime":69,"updateTime":70,"relativeEntities":71,"label":72,"description":74,"key":76,"publicationTags":77,"standard":24},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-11-21T10:07:52.274+00:00",[],{"EN":73,"VI":73},"Scopus - Elsevier",{"EN":73,"VI":75},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[78],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F11600154092","2002-2020",[82,83,84],"79038ce0-d679-4a67-853e-6cfed6a39b0a","e89cebf4-fecf-4b74-8ab4-88fca891e4c2","578ed71b-dd6b-46c6-8851-2657dbd3629e","SCOPUS__Q4","https:\u002F\u002Fcurrentprotocols.onlinelibrary.wiley.com\u002Fjournal\u002F1934340x",{"impactFactor":25,"impactFactorByYear":88,"i10Index":93,"i10IndexLast5Year":94,"totalPublication":93,"totalPublicationByYear":95,"totalCitation":96,"totalCitationByYear":97,"totalCitationPerPublication":101,"totalCitationPerPublicationByYear":102,"hindexLast5Year":93,"hindex":93},{"2017":89,"2018":90,"2021":91,"2022":92},216,237,32,71,3,1,{"2003":94,"2016":94,"2020":94},6413,{"2003":98,"2016":99,"2020":100},2557,3583,273,2137.67,{"2003":98,"2016":99,"2020":100},{"meta":104,"data":106},{"total":105},"11",[107,295,410,750,903,1540,2333,2536,2828,2936],{"id":108,"createTime":109,"updateTime":109,"relativeEntities":110,"slug":111,"properties":112,"entityType":126,"verifyStatus":127,"verifyTime":109,"verifyNote":128,"syncStatus":23,"languages":129,"translateLanguages":24,"viewCount":25,"primaryUrl":131,"fullTextUrl":24,"authors":132,"publicationType":172,"publisherRelationship":173,"citationCount":202,"citationInfo":203,"publishDate":211,"publishYear":212,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":213,"isForceReanalyzing":294},"dd9cfbbd-c466-46a2-b4c7-306fe4d45070","2024-09-25T23:06:35.030+00:00",[],"Predicting-Genes-in-Single-Genomes-with-AUGUSTUS",{"mag":113,"keywords":115,"openalex":116,"abstract":118,"title":120,"pm":122,"doi":124},{"VOID":114},"2901620691",{},{"VOID":117},"W2901620691",{"EN":119},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>AUGUSTUS is a tool for finding protein‐coding genes and their exon‐intron structure in genomic sequences. It does not necessarily require additional experimental input, as it can be applied in so‐called \u003Cjats:italic>ab initio\u003C\u002Fjats:italic> mode. However, extrinsic evidence from various sources such as transcriptome sequencing or the annotations of closely related genomes can be integrated in order to improve the accuracy and completeness of the annotation. AUGUSTUS can be applied to single genomes, or simultaneously to several aligned genomes. Here, we describe steps required for training AUGUSTUS for the annotation of individual genomes and the steps to do the actual structural annotation. Further, we describe the generation and integration of evidence from various sources of extrinsic evidence. © 2018 by John Wiley & Sons, Inc.\u003C\u002Fjats:p>",{"EN":121},"Predicting Genes in Single Genomes with AUGUSTUS",{"VOID":123},"30466165",{"VOID":125},"10.1002\u002Fcpbi.57","PUBLICATION","VERIFIED","Auto Verify",[130],"EN","https:\u002F\u002Fcurrentprotocols.onlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fcpbi.57",[133,155],{"id":134,"sortIndex":25,"researcher":24,"roles":135,"affiliations":136,"properties":148},"ee7ea668-4f3f-4d8a-9217-43d1aa928a73",[],[137],{"id":138,"sortIndex":25,"affiliation":139,"properties":24},"7341bd02-cd65-49c6-b14d-0602a81681dd",{"id":140,"createTime":141,"updateTime":142,"relativeEntities":143,"slug":144,"properties":145,"entityType":61,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"b141ed4f-6ca4-48f3-ae6c-7e3f976d5ade","2024-01-03T05:49:18.146+00:00","2024-09-25T23:06:35.045+00:00",[],"University-of-Greifswald-Institute-of-Mathematics-and-Computer-Science-Greifswald-Germany",{"title":146},{"VI":147},"University of Greifswald, Institute of Mathematics and Computer Science, Greifswald, Germany",{"openalex":149,"orcid":151,"title":153},{"VOID":150},"A5022398424",{"VOID":152},"https:\u002F\u002Forcid.org\u002F0000-0002-7333-8390",{"EN":154},"Katharina J. 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It also includes protocols for building a genome index and calling consensus sequences from Bowtie alignments using SAMtools. \u003Cjats:italic>Curr. Protoc. Bioinform\u003C\u002Fjats:italic>. 32:11.7.1‐11.7.14. © 2010 by John Wiley & Sons, Inc.\u003C\u002Fjats:p>",{"EN":311},"Aligning Short Sequencing Reads with Bowtie",{"VOID":313},"21154709",{"VOID":315},"10.1002\u002F0471250953.bi1107s32","2024-09-20T20:50:09.551+00:00",[130],"https:\u002F\u002Fcurrentprotocols.onlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002F0471250953.bi1107s32",[320],{"id":321,"sortIndex":25,"researcher":24,"roles":322,"affiliations":323,"properties":335},"44ae484d-73b2-4a09-b193-1c711c389e59",[],[324],{"id":325,"sortIndex":25,"affiliation":326,"properties":24},"5ceecc44-d190-4305-a440-6781d7cfca16",{"id":327,"createTime":328,"updateTime":329,"relativeEntities":330,"slug":331,"properties":332,"entityType":61,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"a4af0db1-0298-40c3-85be-dd4f2394a0c1","2024-02-14T13:05:48.709+00:00","2025-02-03T05:46:25.394+00:00",[],"Johns-Hopkins-University-Baltimore-Maryland",{"title":333},{"VI":334},"Johns Hopkins University, Baltimore, Maryland",{"openalex":336,"orcid":338,"title":340},{"VOID":337},"A5009556658",{"VOID":339},"https:\u002F\u002Forcid.org\u002F0000-0003-2437-1976",{"EN":341},"Ben Langmead",{"url":24,"publisher":343,"properties":366},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":344,"slug":10,"properties":345,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":351,"manageAffiliations":352,"indexDatabases":353,"url":86,"thumbnailPath":24,"statistic":361,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":346,"issn":347,"introduce":348,"eissn":349,"title":350},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[354],{"id":66,"indexDatabase":355,"url":79,"indexYears":80,"academicFieldIds":360,"indexDatabaseRanking":85},{"id":68,"createTime":69,"updateTime":70,"relativeEntities":356,"label":357,"description":358,"key":76,"publicationTags":359,"standard":24},[],{"EN":73,"VI":73},{"EN":73,"VI":75},[78],[82,83,84],{"impactFactor":25,"impactFactorByYear":362,"i10Index":93,"i10IndexLast5Year":94,"totalPublication":93,"totalPublicationByYear":363,"totalCitation":96,"totalCitationByYear":364,"totalCitationPerPublication":101,"totalCitationPerPublicationByYear":365,"hindexLast5Year":93,"hindex":93},{"2017":89,"2018":90,"2021":91,"2022":92},{"2003":94,"2016":94,"2020":94},{"2003":98,"2016":99,"2020":100},{"2003":98,"2016":99,"2020":100},{"volume":367,"issue":369},{"VOID":368},"32",{"VOID":201},1195,{"total":370,"publishYear":24,"statisticByYear":372},{"2012":373,"2013":374,"2014":375,"2015":376,"2016":377,"2017":378,"2018":379,"2019":380,"2020":381,"2021":382,"2022":383,"2023":384,"2024":385},29,50,44,52,72,87,119,116,146,167,136,105,57,"2010-12-01",2010,[389,392,395,398,401,404,407],{"id":24,"text":390,"url":24,"identifiers":391},"Burrows M., 1994, Digital Equipment Corporation",{},{"id":24,"text":393,"url":24,"identifiers":394},"10.1093\u002Fnar\u002Fgkp1137",{"doi":393},{"id":24,"text":396,"url":24,"identifiers":397},"Ferragina P.andManzini G.2000.Opportunistic data structures with applications.InProceedings of the 41st Annual Symposium on Foundations of Computer Science. IEEE Computer Society.",{},{"id":24,"text":399,"url":24,"identifiers":400},"10.1186\u002Fgb-2009-10-3-r25",{"doi":399},{"id":24,"text":402,"url":24,"identifiers":403},"10.1093\u002Fbioinformatics\u002Fbtp352",{"doi":402},{"id":24,"text":405,"url":24,"identifiers":406},"Langmeadet al. 2009. Seeabove.",{},{"id":24,"text":408,"url":24,"identifiers":409},"http:\u002F\u002Fbowtie‐bio.sf.net",{},{"id":411,"createTime":412,"updateTime":412,"relativeEntities":413,"slug":414,"properties":415,"entityType":126,"verifyStatus":127,"verifyTime":412,"verifyNote":128,"syncStatus":23,"languages":429,"translateLanguages":24,"viewCount":25,"primaryUrl":430,"fullTextUrl":24,"authors":431,"publicationType":172,"publisherRelationship":524,"citationCount":552,"citationInfo":553,"publishDate":561,"publishYear":212,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":562,"isForceReanalyzing":294},"8d44b706-0294-42ff-a001-34648573e766","2024-12-26T16:11:20.757+00:00",[],"Using-MetaboAnalyst-4-0-for-Comprehensive-and-Integrative-Metabolomics-Data-Analysis",{"mag":416,"keywords":418,"openalex":419,"abstract":421,"title":423,"pm":425,"doi":427},{"VOID":417},"2974361058",{},{"VOID":420},"W2974361058",{"EN":422},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>MetaboAnalyst (https:\u002F\u002Fwww.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc.\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 1\u003C\u002Fjats:bold>: Data uploading, processing, and normalization\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 2\u003C\u002Fjats:bold>: Identification of significant variables\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 3\u003C\u002Fjats:bold>: Multivariate exploratory data analysis\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 4\u003C\u002Fjats:bold>: Functional interpretation of metabolomic data\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 5\u003C\u002Fjats:bold>: Biomarker analysis based on receiver operating characteristic (ROC) curves\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 6\u003C\u002Fjats:bold>: Time‐series and two‐factor data analysis\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 7\u003C\u002Fjats:bold>: Sample size estimation and power analysis\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 8\u003C\u002Fjats:bold>: Joint pathway analysis\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 9\u003C\u002Fjats:bold>: MS peaks to pathway activities\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 10\u003C\u002Fjats:bold>: Biomarker meta‐analysis\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 11\u003C\u002Fjats:bold>: Knowledge‐based network exploration of multi‐omics data\u003C\u002Fjats:p>\u003Cjats:p>\u003Cjats:bold>Basic Protocol 12\u003C\u002Fjats:bold>: MetaboAnalystR introduction\u003C\u002Fjats:p>",{"EN":424},"Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data 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With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole‐genome sequencing and metagenomic datasets. 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