Current protocols in bioinformatics最新文献_第3页

The ENCODE Portal as an Epigenomics Resource ENCODE门户作为表观基因组学资源

Current protocols in bioinformatics Pub Date : 2019-11-21 DOI: 10.1002/cpbi.89

Jennifer Jou, Idan Gabdank, Yunhai Luo, Khine Lin, Paul Sud, Zachary Myers, Jason A. Hilton, Meenakshi S. Kagda, Bonita Lam, Emma O'Neill, Philip Adenekan, Keenan Graham, Ulugbek K. Baymuradov, Stuart R. Miyasato, J. Seth Strattan, Otto Jolanki, Jin-Wook Lee, Casey Litton, Forrest Y. Tanaka, Benjamin C. Hitz, J. Michael Cherry

{"title":"The ENCODE Portal as an Epigenomics Resource","authors":"Jennifer Jou, Idan Gabdank, Yunhai Luo, Khine Lin, Paul Sud, Zachary Myers, Jason A. Hilton, Meenakshi S. Kagda, Bonita Lam, Emma O'Neill, Philip Adenekan, Keenan Graham, Ulugbek K. Baymuradov, Stuart R. Miyasato, J. Seth Strattan, Otto Jolanki, Jin-Wook Lee, Casey Litton, Forrest Y. Tanaka, Benjamin C. Hitz, J. Michael Cherry","doi":"10.1002/cpbi.89","DOIUrl":"10.1002/cpbi.89","url":null,"abstract":"The Encyclopedia of DNA Elements (ENCODE) web portal hosts genomic data generated by the ENCODE Consortium, Genomics of Gene Regulation, The NIH Roadmap Epigenomics Consortium, and the modENCODE and modERN projects. The goal of the ENCODE project is to build a comprehensive map of the functional elements of the human and mouse genomes. Currently, the portal database stores over 500 TB of raw and processed data from over 15,000 experiments spanning assays that measure gene expression, DNA accessibility, DNA and RNA binding, DNA methylation, and 3D chromatin structure across numerous cell lines, tissue types, and differentiation states with selected genetic and molecular perturbations. The ENCODE portal provides unrestricted access to the aforementioned data and relevant metadata as a service to the scientific community. The metadata model captures the details of the experiments, raw and processed data files, and processing pipelines in human and machine-readable form and enables the user to search for specific data either using a web browser or programmatically via REST API. Furthermore, ENCODE data can be freely visualized or downloaded for additional analyses. © 2019 The Authors.Basic Protocol: Query the portalSupport Protocol 1: Batch downloadingSupport Protocol 2: Using the cart to download filesSupport Protocol 3: Visualize dataAlternate Protocol: Query building and programmatic access","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.89","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42016937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Using INTERSPIA to Explore the Dynamics of Protein-Protein Interactions Among Multiple Species 利用INTERSPIA探索多物种之间蛋白质-蛋白质相互作用的动力学

Current protocols in bioinformatics Pub Date : 2019-11-21 DOI: 10.1002/cpbi.88

Daehong Kwon, Daehwan Lee, Juyeon Kim, Jongin Lee, Mikang Sim, Jaebum Kim

引用次数: 0

Population Genetic Inference With MIGRATE 与迁移的群体遗传推断

Current protocols in bioinformatics Pub Date : 2019-10-24 DOI: 10.1002/cpbi.87

Peter Beerli, Somayeh Mashayekhi, Marjan Sadeghi, Marzieh Khodaei, Kyle Shaw

{"title":"Population Genetic Inference With MIGRATE","authors":"Peter Beerli, Somayeh Mashayekhi, Marjan Sadeghi, Marzieh Khodaei, Kyle Shaw","doi":"10.1002/cpbi.87","DOIUrl":"10.1002/cpbi.87","url":null,"abstract":"Many evolutionary biologists collect genetic data from natural populations and then need to investigate the relationship among these populations to compare different biogeographic hypotheses. MIGRATE, a useful tool for exploring relationships between populations and comparing hypotheses, has existed since 1998. Throughout the years, it has steadily improved in both the quality of algorithms used and in the efficiency of carrying out those calculations, thus allowing for a larger number of loci to be evaluated. This efficiency has been enhanced, as MIGRATE has been developed to perform many of its calculations concurrently when running on a computer cluster. The program is based on the coalescence theory and uses Bayesian inference to estimate posterior probability densities of all the parameters of a user-specified population model. Complex models, which include migration and colonization parameters, can be specified. These models can be evaluated using marginal likelihoods, thus allowing a user to compare the merits of different hypotheses. The three presented protocols will help novice users to develop sophisticated analysis techniques useful for their research projects. © 2019 The Authors.Basic Protocol 1: First steps with MIGRATEBasic Protocol 2: Population model specificationBasic Protocol 3: Prior distribution specificationBasic Protocol 4: Model selectionSupport Protocol 1: Installing the program MIGRATESupport Protocol 2: Installation of parallel MIGRATE","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.87","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47483598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis 使用MetaboAnalyst 4.0进行综合代谢组学数据分析

Current protocols in bioinformatics Pub Date : 2019-09-20 DOI: 10.1002/cpbi.86

Jasmine Chong, David S. Wishart, Jianguo Xia

{"title":"Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis","authors":"Jasmine Chong, David S. Wishart, Jianguo Xia","doi":"10.1002/cpbi.86","DOIUrl":"10.1002/cpbi.86","url":null,"abstract":"MetaboAnalyst (https://www.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.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.86","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43320523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1489

Issue Information TOC 发布信息TOC

Current protocols in bioinformatics Pub Date : 2019-09-16 DOI: 10.1002/cpbi.64

{"title":"Issue Information TOC","authors":"","doi":"10.1002/cpbi.64","DOIUrl":"10.1002/cpbi.64","url":null,"abstract":"Cover: In Prandi and Demichelis (https://doi.org/10.1002/cpbi.81), the image shows a cartoon of the computation of beta and allelic fraction of informative SNPs. (A) Example of the allelic fraction (AF) and beta (β) values computed for five genomic positions (p1 to pm) corresponding to five informative SNPs. Positions p1 to pn are within a hemizygously deleted genomic segment, A, whereas genomic positions pn + 1 to pm lie within a wild-type genomic segment, B. (B to D) Examples of a normal cell and two different tumor cells. Tumor cells 1 and 2 differ in the status of genomic segment B. Histograms below the cell cartoons report the expected distribution of the AF of SNPs in genomic segments A and B together with the associated beta values. (E and F) Examples of two different tumor samples. Tumor sample 1 includes one normal cell and nine tumor cells with deleted genomic segment A and wild-type genomic segment B. Tumor sample 2 differs from tumor sample 1 in the presence of six tumor cells with a hemizygous deletion of genomic segment B. Expected distribution of the AF of informative SNPs together with estimated beta are depicted below each tumor sample cartoon.\t\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture>\u0000 </div>\u0000 </figure>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.64","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47235711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using MARRVEL v1.2 for Bioinformatics Analysis of Human Genes and Variant Pathogenicity 利用marvel v1.2进行人类基因和变异致病性的生物信息学分析

Current protocols in bioinformatics Pub Date : 2019-07-19 DOI: 10.1002/cpbi.85

Julia Wang, Dongxue Mao, Fatima Fazal, Seon-Young Kim, Shinya Yamamoto, Hugo Bellen, Zhandong Liu

引用次数: 10

Predicting Sequence Features, Function, and Structure of Proteins Using MESSA 利用MESSA预测蛋白质的序列特征、功能和结构

Current protocols in bioinformatics Pub Date : 2019-07-15 DOI: 10.1002/cpbi.84

Archana S. Bhat, Nick V. Grishin

引用次数: 1

Ploidy- and Purity-Adjusted Allele-Specific DNA Analysis Using CLONETv2 使用CLONETv2进行倍性和纯度调整的等位基因特异性DNA分析

Current protocols in bioinformatics Pub Date : 2019-06-21 DOI: 10.1002/cpbi.81

Davide Prandi, Francesca Demichelis

引用次数: 8

Community Curation and Expert Curation of Human Long Noncoding RNAs with LncRNAWiki and LncBook 基于LncRNAWiki和LncBook的人类长链非编码rna的社区管理和专家管理

Current protocols in bioinformatics Pub Date : 2019-06-20 DOI: 10.1002/cpbi.82

Lina Ma, Jiabao Cao, Lin Liu, Zhao Li, Huma Shireen, Nashaiman Pervaiz, Fatima Batool, Rabail Z. Raza, Dong Zou, Yiming Bao, Amir A. Abbasi, Zhang Zhang

{"title":"Community Curation and Expert Curation of Human Long Noncoding RNAs with LncRNAWiki and LncBook","authors":"Lina Ma, Jiabao Cao, Lin Liu, Zhao Li, Huma Shireen, Nashaiman Pervaiz, Fatima Batool, Rabail Z. Raza, Dong Zou, Yiming Bao, Amir A. Abbasi, Zhang Zhang","doi":"10.1002/cpbi.82","DOIUrl":"10.1002/cpbi.82","url":null,"abstract":"In recent years, the number of human long noncoding RNAs (lncRNAs) that have been identified has increased exponentially. However, these lncRNAs are poorly annotated compared to protein-coding genes, posing great challenges for a better understanding of their functional significance and elucidating their complex functioning molecular mechanisms. Here we employ both community and expert curation to yield a comprehensive collection of human lncRNAs and their annotations. Specifically, LncRNAWiki (http://lncrna.big.ac.cn/index.php/Main_Page) uses a wiki-based community curation model, thus showing great promise in dealing with the flood of biological knowledge, while LncBook (http://bigd.big.ac.cn/lncbook) is an expert curation–based database that provides a complement to LncRNAWiki. LncBook features a comprehensive collection of human lncRNAs and a systematic curation of lncRNAs by multi-omics data integration, functional annotation, and disease association. These protocols provide step-by-step instructions on how to browse and search a specific lncRNA and how to obtain a range of related information including expression, methylation, variation, function, and disease association. © 2019 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.82","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47832300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Using Mothur to Determine Bacterial Community Composition and Structure in 16S Ribosomal RNA Datasets 利用母亲测定16S核糖体RNA数据集中的细菌群落组成和结构

Current protocols in bioinformatics Pub Date : 2019-06-20 DOI: 10.1002/cpbi.83

Sruthi Chappidi, Erika C. Villa, Brandi L. Cantarel

引用次数: 25