Current protocols in bioinformatics最新文献

The ProteomeXchange (PX) Consortium is the unifying framework for world-leading mass spectrometry (MS)–based proteomics repositories. Current members include the PRIDE database (U.K.), PeptideAtlas/PASSEL, and MassIVE (U.S.A.), and jPOST (Japan). The Consortium standardizes submission and dissemination of public proteomics data worldwide. This is achieved through implementing common data submission guidelines and enforcing metadata requirements by each of the members. Furthermore, the members use a common identifier space. Each dataset receives a unique (PXD) accession number and is publicly accessible as soon as the associated scientific publications are released. The two basic protocols provide a step-by-step guide on how to submit data to the PRIDE database, and describe how to access the PX portal (called ProteomeCentral), which can be used to search datasets available in any of the PX members. © 2017 by John Wiley & Sons, Inc.

The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field—highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.

Protein sequence similarity search is one of the most commonly used bioinformatics methods for identifying evolutionarily related proteins. In general, sequences that are evolutionarily related share some degree of similarity, and sequence-search algorithms use this principle to identify homologs. The requirement for a fast and sensitive sequence search method led to the development of the HMMER software, which in the latest version (v3.1) uses a combination of sophisticated acceleration heuristics and mathematical and computational optimizations to enable the use of profile hidden Markov models (HMMs) for sequence analysis. The HMMER Web server provides a common platform by linking the HMMER algorithms to databases, thereby enabling the search for homologs, as well as providing sequence and functional annotation by linking external databases. This unit describes three basic protocols and two alternate protocols that explain how to use the HMMER Web server using various input formats and user defined parameters. © 2017 by John Wiley & Sons, Inc.

The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs, gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome. Additionally, we describe how members of the community can share data using TAIR's Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. © 2017 by John Wiley & Sons, Inc.

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.

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 LIGSITE^CSC 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.

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.

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.

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.

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.