Current protocols in bioinformatics最新文献_第9页

Using the PRIDE Database and ProteomeXchange for Submitting and Accessing Public Proteomics Datasets 使用PRIDE数据库和ProteomeXchange提交和访问公共蛋白质组学数据集

Current protocols in bioinformatics Pub Date : 2018-02-13 DOI: 10.1002/cpbi.30

Andrew F. Jarnuczak, Juan Antonio Vizcaíno

引用次数: 39

Prediction of Protein-Protein Interactions 蛋白质相互作用的预测

Current protocols in bioinformatics Pub Date : 2017-12-08 DOI: 10.1002/cpbi.38

Max Kotlyar, Andrea E.M. Rossos, Igor Jurisica

引用次数: 32

The HMMER Web Server for Protein Sequence Similarity Search 蛋白质序列相似性搜索的HMMER Web服务器

Current protocols in bioinformatics Pub Date : 2017-12-08 DOI: 10.1002/cpbi.40

Ananth Prakash, Matt Jeffryes, Alex Bateman, Robert D. Finn

引用次数: 103

Using the Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes 利用拟南芥信息资源(TAIR)查找拟南芥基因信息

Current protocols in bioinformatics Pub Date : 2017-12-08 DOI: 10.1002/cpbi.36

Leonore Reiser, Shabari Subramaniam, Donghui Li, Eva Huala

引用次数: 54

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data 使用七桥癌症基因组云访问和分析数pb的癌症数据

Current protocols in bioinformatics Pub Date : 2017-12-08 DOI: 10.1002/cpbi.39

Raunaq Malhotra, Isheeta Seth, Erik Lehnert, Jing Zhao, Gaurav Kaushik, Elizabeth H. Williams, Anurag Sethi, Brandi N. Davis-Dusenbery

{"title":"Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data","authors":"Raunaq Malhotra, Isheeta Seth, Erik Lehnert, Jing Zhao, Gaurav Kaushik, Elizabeth H. Williams, Anurag Sethi, Brandi N. Davis-Dusenbery","doi":"10.1002/cpbi.39","DOIUrl":"10.1002/cpbi.39","url":null,"abstract":"Next-generation sequencing has produced petabytes of data, but accessing and analyzing these data remain challenging. Traditionally, researchers investigating public datasets like The Cancer Genome Atlas (TCGA) would download the data to a high-performance cluster, which could take several weeks even with a highly optimized network connection. The National Cancer Institute (NCI) initiated the Cancer Genomics Cloud Pilots program to provide researchers with the resources to process data with cloud computational resources. We present protocols using one of these Cloud Pilots, the Seven Bridges Cancer Genomics Cloud (CGC), to find and query public datasets, bring your own data to the CGC, analyze data using standard or custom workflows, and benchmark tools for accuracy with interactive analysis features. These protocols demonstrate that the CGC is a data-analysis ecosystem that fully empowers researchers with a variety of areas of expertise and interests to collaborate in the analysis of petabytes of data. © 2017 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.39","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35629630","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: 4

Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER 使用3D- surfer2.0和EM-SURFER的蛋白质三维结构和电子显微镜图谱检索

Current protocols in bioinformatics Pub Date : 2017-12-08 DOI: 10.1002/cpbi.37

Xusi Han, Qing Wei, Daisuke Kihara

{"title":"Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER","authors":"Xusi Han, Qing Wei, Daisuke Kihara","doi":"10.1002/cpbi.37","DOIUrl":"10.1002/cpbi.37","url":null,"abstract":"With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITECSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.37","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35629627","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: 9

Using the NONCODE Database Resource 使用NONCODE数据库资源

Current protocols in bioinformatics Pub Date : 2017-06-27 DOI: 10.1002/cpbi.25

Li Xiyuan, Bu Dechao, Sun Liang, Wu Yang, Fang Shuangsang, Li Hui, Luo Haitao, Luo Chunlong, Fang Wenzheng, Chen Runsheng, Zhao Yi

{"title":"Using the NONCODE Database Resource","authors":"Li Xiyuan, Bu Dechao, Sun Liang, Wu Yang, Fang Shuangsang, Li Hui, Luo Haitao, Luo Chunlong, Fang Wenzheng, Chen Runsheng, Zhao Yi","doi":"10.1002/cpbi.25","DOIUrl":"10.1002/cpbi.25","url":null,"abstract":"NONCODE is a comprehensive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNA genes), and thus NONCODE is essential to modern biological and medical research. Scientists are producing a flood of new data from which new lncRNA genes and lncRNA-disease relationships are continually being identified. NONCODE assimilates such information from a wide variety of sources including published articles, RNA-seq data, micro-array data and databases on genetic variation (dbSNP) and genome-wide associations (GWAS). NONCODE organizes all this information and makes it freely available to the public via the Internet. The NONCODE protocol provides step-by-step instructions on how to browse and search lncRNA information such as sequence, expression, and disease relationships, how to use the tools for functional prediction, species conservation assays, blast analysis, identifier conversion, and, finally, how to submit sequences to identify lncRNA genes. As of Dec 2016, NONCODE has cataloged 487,851 lncRNA genes sequenced from 16 species. © 2017 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.25","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35123102","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: 13

Finding Similar Nucleotide Sequences Using Network BLAST Searches 使用网络BLAST搜索寻找相似的核苷酸序列

Current protocols in bioinformatics Pub Date : 2017-06-27 DOI: 10.1002/cpbi.29

Istvan Ladunga

{"title":"Finding Similar Nucleotide Sequences Using Network BLAST Searches","authors":"Istvan Ladunga","doi":"10.1002/cpbi.29","DOIUrl":"10.1002/cpbi.29","url":null,"abstract":"The Basic Local Alignment Search Tool (BLAST) is the first tool in the annotation of nucleotide or amino acid sequences. BLAST is a flagship of bioinformatics due to its performance and user-friendliness. Beginners and intermediate users will learn how to design and submit blastn and Megablast searches on the Web pages at the National Center for Biotechnology Information. We map nucleic acid sequences to genomes, find identical or similar mRNAs, expressed sequence tag, and noncoding RNA sequences, and run Megablast searches, which are much faster than blastn. Understanding results is assisted by taxonomy reports, genomic views, and multiple alignments. We interpret expected frequency thresholds, biological significance, and statistical significance. Weak hits provide no evidence, but indicate hints for further analyses. We find genes that may code for homologous proteins by translated BLAST. We reduce false positives by filtering out low-complexity regions. Parsed BLAST results can be integrated into analysis pipelines. Links in the output connect to Entrez and PubMed, as well as structural, sequence, interaction, and expression databases. This facilitates integration with a wide spectrum of biological knowledge. © 2017 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.29","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35123103","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: 20

SIGNOR: A Database of Causal Relationships Between Biological Entities—A Short Guide to Searching and Browsing SIGNOR:一个生物实体之间因果关系的数据库——一个搜索和浏览的简短指南

Current protocols in bioinformatics Pub Date : 2017-06-27 DOI: 10.1002/cpbi.28

Prisca Lo Surdo, Alberto Calderone, Gianni Cesareni, Livia Perfetto

{"title":"SIGNOR: A Database of Causal Relationships Between Biological Entities—A Short Guide to Searching and Browsing","authors":"Prisca Lo Surdo, Alberto Calderone, Gianni Cesareni, Livia Perfetto","doi":"10.1002/cpbi.28","DOIUrl":"10.1002/cpbi.28","url":null,"abstract":"SIGNOR (http://signor.uniroma2.it), the SIGnaling Network Open Resource, is a database designed to store experimentally validated causal interactions, i.e., interactions where a source entity has a regulatory effect (up-regulation, down-regulation, etc.) on a second target entity. SIGNOR acts both as a source of signaling information and a support for data analysis, modeling, and prediction. A user-friendly interface features the ability to search entries for any given protein or group of proteins and to display their interactions graphically in a network view. At the time of writing, SIGNOR stores approximately 16,000 manually curated interactions connecting more than 4,000 biological entities (proteins, chemicals, protein complexes, etc.) that play a role in signal transduction. SIGNOR also offers a collection of 37 signaling pathways. SIGNOR can be queried by three search tools: “single-entity” search, “multiple-entity” search, and “pathway” search. This manuscript describes two basic protocols detailing how to navigate and search the SIGNOR database and how to download the annotated dataset for local use. Finally, the support protocol reviews the utilities of the graphic visualizer. © 2017 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.28","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35123104","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: 26

Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide 使用iPathwayGuide识别显著受影响的通路和推测的机制

Current protocols in bioinformatics Pub Date : 2017-06-27 DOI: 10.1002/cpbi.24

Sidra Ahsan, Sorin Drăghici

{"title":"Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide","authors":"Sidra Ahsan, Sorin Drăghici","doi":"10.1002/cpbi.24","DOIUrl":"10.1002/cpbi.24","url":null,"abstract":"iPathwayGuide is a gene expression analysis tool that provides biological context and inferences from data generated by high-throughput sequencing. iPathwayGuide utilizes a systems biology approach to identify significantly impacted signaling pathways, Gene Ontology terms, disease processes, predicted microRNAs, and putative mechanisms based on the given differential expression signature. By using a novel analytical approach called Impact Analysis, iPathwayGuide considers the role, position, and relationships of each gene within a pathway, which results in a significant reduction in false positives, as well as a better ability to identify the truly impacted pathways and putative mechanisms that can explain all measured gene expression changes. It is a Web-based, user-friendly, interactive tool that does not require prior training in bioinformatics. The protocols in this unit describe how to use iPathwayGuide to analyze a single contrast between two phenotypes (any number of samples), and provide guidance on how to interpret the results obtained from iPathwayGuide. Even though iPathwayGuide has powerful meta-analysis capabilities, these are not covered in this unit. © 2017 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.24","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35124380","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: 61