Current protocols in bioinformatics最新文献

pLink is a search engine for high-throughput identification of cross-linked peptides from their tandem mass spectra, which is the data-analysis step in chemical cross-linking of proteins coupled with mass spectrometry analysis. pLink has accumulated more than 200 registered users from all over the world since its first release in 2012. After 2 years of continual development, a new version of pLink has been released, which is at least 40 times faster, more versatile, and more user-friendly. Also, the function of the new pLink has been expanded to identifying endogenous protein cross-linking sites such as disulfide bonds and SUMO (Small Ubiquitin-like MOdifier) modification sites. Integrated into the new version are two accessory tools: pLabel, to annotate spectra of cross-linked peptides for visual inspection and publication, and pConfig, to assist users in setting up search parameters. Here, we provide detailed guidance on running a database search for identification of protein cross-links using the 2014 version of pLink. © 2015 by John Wiley & Sons, Inc.

SCOP2 is a successor to the Structural Classification of Proteins (SCOP) database that organizes proteins of known structure according to their structural and evolutionary relationships. It was designed to provide a more advanced framework for the classification of proteins. The SCOP2 classification is described in terms of a directed acyclic graph in which each node defines a relationship of particular type that is represented by a region of protein structure and sequence. The SCOP2 data are accessible via SCOP2-Browser and SCOP2-Graph. This protocol unit describes different ways to explore and investigate the SCOP2 evolutionary and structural groupings. © 2015 by John Wiley & Sons, Inc.

The Reactome database of curated biological pathways provides a tool for visualizing user-supplied expression data as an overlay on pathway diagrams, thereby affording an effective means to examine expression of the constituents of the pathway and determine whether all that are necessary are present. Several experiments can be visualized in succession, to determine whether expression changes with experimental conditions, a useful feature for examining a time-course, dose-response, or disease progression. © 2015 by John Wiley & Sons, Inc.

RNA editing is a post-transcriptional/co-transcriptional molecular phenomenon whereby a genetic message is modified from the corresponding DNA template by means of substitutions, insertions, and/or deletions. It occurs in a variety of organisms and different cellular locations through evolutionally and biochemically unrelated proteins. RNA editing has a plethora of biological effects including the modulation of alternative splicing and fine-tuning of gene expression. RNA editing events by base substitutions can be detected on a genomic scale by NGS technologies through the REDItools package, an ad hoc suite of Python scripts to study RNA editing using RNA-Seq and DNA-Seq data or RNA-Seq data alone. REDItools implement effective filters to minimize biases due to sequencing errors, mapping errors, and SNPs. The package is freely available at Google Code repository (http://code.google.com/p/reditools/) and released under the MIT license. In the present unit we show three basic protocols corresponding to three main REDItools scripts. © 2015 by John Wiley & Sons, Inc.

High-throughput Affinity Purification Mass Spectrometry (AP-MS) experiments can identify a large number of protein interactions, but only a fraction of these interactions are biologically relevant. Here, we describe a comprehensive computational strategy to process raw AP-MS data, perform quality controls, and prioritize biologically relevant bait-prey pairs in a set of replicated AP-MS experiments with Mass spectrometry interaction STatistics (MiST). The MiST score is a linear combination of prey quantity (abundance), abundance invariability across repeated experiments (reproducibility), and prey uniqueness relative to other baits (specificity). We describe how to run the full MiST analysis pipeline in an R environment and discuss a number of configurable options that allow the lay user to convert any large-scale AP-MS data into an interpretable, biologically relevant protein-protein interaction network. © 2015 by John Wiley & Sons, Inc.

RNA sequencing (RNA-seq) is a high-throughput technology that provides unique insights into the transcriptome. It has a wide variety of applications in quantifying genes/isoforms and in detecting non-coding RNA, alternative splicing, and splice junctions. It is extremely important to comprehend the entire transcriptome for a thorough understanding of the cellular system. Several RNA-seq analysis pipelines have been proposed to date. However, no single analysis pipeline can capture dynamics of the entire transcriptome. Here, we compile and present a robust and commonly used analytical pipeline covering the entire spectrum of transcriptome analysis, including quality checks, alignment of reads, differential gene/transcript expression analysis, discovery of cryptic splicing events, and visualization. Challenges, critical parameters, and possible downstream functional analysis pipelines associated with each step are highlighted and discussed. This unit provides a comprehensive understanding of state-of-the-art RNA-seq analysis pipeline and a greater understanding of the transcriptome. © 2017 by John Wiley & Sons, Inc.

Highly connected nodes in biological networks are called network hubs. Hubs are topologically important to the structure of the network and have been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we provide a step-by-step protocol for using the Contextual Hub Analysis Tool (CHAT), an application within Cytoscape 3, which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene or protein expression data, and identify hub nodes that are more highly connected to contextual nodes than expected by chance. © 2017 by John Wiley & Sons, Inc.

Relational databases can integrate diverse types of information and manage large sets of similarity search results, greatly simplifying genome-scale analyses. By focusing on taxonomic subsets of sequences, relational databases can reduce the size and redundancy of sequence libraries and improve the statistical significance of homologs. In addition, by loading similarity search results into a relational database, it becomes possible to explore and summarize the relationships between all of the proteins in an organism and those in other biological kingdoms. This unit describes how to use relational databases to improve the efficiency of sequence similarity searching and demonstrates various large-scale genomic analyses of homology-related data. It also describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. The unit also introduces search_demo, a database that stores sequence similarity search results. The search_demo database is then used to explore the evolutionary relationships between E. coli proteins and proteins in other organisms in a large-scale comparative genomic analysis. © 2017 by John Wiley & Sons, Inc.

BLAST, the Basic Local Alignment Search Tool, is used more frequently than any other biosequence database search program. We show how to run searches on the Web, and demonstrate how to increase performance by fine-tuning arguments for a specific research project. We offer guidance for interpreting results, statistical significance and biological relevance issues, and suggest complementary analyses. This unit covers both protein-to-protein (blastp) searches and translated searches (blastx, tblastn, tfastx). blastx conceptually translates the query sequence and tblastn translates all nucleotide sequences in a database, while tblastx translates both the query and the database sequences into amino acid sequences. © 2017 by John Wiley & Sons, Inc.

Post-translational modifications (PTMs) of protein amino acids are ubiquitous and important to protein function, localization, degradation, and more. In recent years, there has been an explosion in the discovery of PTMs as a result of improvements in PTM measurement techniques, including quantitative measurements of PTMs across multiple conditions. ProteomeScout is a repository for such discovery and quantitative experiments and provides tools for visualizing PTMs within proteins, including where they are relative to other PTMS, domains, mutations, and structure. ProteomeScout additionally provides analysis tools for identifying statistically significant relationships in experimental datasets. This unit describes four basic protocols for working with the ProteomeScout Web interface or programmatically with the database download. © 2017 by John Wiley & Sons, Inc.