Current protocols in bioinformatics最新文献

The Eukaryotic Linear Motif (ELM) resource is dedicated to the characterization and prediction of short linear motifs (SLiMs). SLiMs are compact, degenerate peptide segments found in many proteins and essential to almost all cellular processes. However, despite their abundance, SLiMs remain largely uncharacterized. The ELM database is a collection of manually annotated SLiM instances curated from experimental literature. In this article we illustrate how to browse and search the database for curated SLiM data, and cover the different types of data integrated in the resource. We also cover how to use this resource in order to predict SLiMs in known as well as novel proteins, and how to interpret the results generated by the ELM prediction pipeline. The ELM database is a very rich resource, and in the following protocols we give helpful examples to demonstrate how this knowledge can be used to improve your own research. © 2017 by John Wiley & Sons, Inc.

Online Mendelian Inheritance in Man (OMIM) at OMIM.org is the primary repository of comprehensive, curated information on genes and genetic phenotypes and the relationships between them. This unit provides an overview of the types of information in OMIM and optimal strategies for searching and retrieving the information. OMIM.org has links to many related and complementary databases, providing easy access to more information on a topic. The relationship between genes and genetic disorders is highlighted in this unit. The basic protocol explains searching OMIM both from a gene perspective and a clinical features perspective. Two alternate protocols provide strategies for viewing gene-phenotype relationships: a gene map table and Quick View or Side-by-Side format for clinical features. OMIM.org is updated nightly, and the MIMmatch service, described in the support protocol, provides a convenient way to follow updates to entries, gene-phenotype relationships, and collaborate with other researchers. © 2017 by John Wiley & Sons, Inc.

This unit describes how to use 3dRNA to predict RNA 3-D structures from their sequences and secondary (2-D) structures, and how to use 3dRNAscore to evaluate the predicted structures. The predicted RNA 3-D structures can be used to predict or understand their functions and can also be used to find the interactions between the RNA and other molecules. © 2017 by John Wiley & Sons, Inc.

The identification of non-coding drivers remains a challenge and bottleneck for the use of whole-genome sequencing in the clinic. FunSeq2 is a computational tool for annotation and prioritization of somatic mutations in coding and non-coding regions. It integrates a data context made from large-scale genomic datasets and uses a high-throughput variant prioritization pipeline. This unit provides guidelines for installing and running FunSeq2 to (a) annotate and prioritize variants, (b) incorporate user-defined annotations, and (c) detect differential gene expression. © 2017 by John Wiley & Sons, Inc.

Bayesian phylogenetic inference aims to estimate the evolutionary relationships among different lineages (species, populations, gene families, viral strains, etc.) in a model-based statistical framework that uses the likelihood function for parameter estimates. In recent years, evolutionary models for Bayesian analysis have grown in number and complexity. RevBayes uses a probabilistic-graphical model framework and an interactive scripting language for model specification to accommodate and exploit model diversity and complexity within a single software package. In this unit we describe how to specify standard phylogenetic models and perform Bayesian phylogenetic analyses in RevBayes. The protocols focus on the basic analysis of inferring a phylogeny from single and multiple loci, describe a hypothesis-testing approach, and point to advanced topics. Thus, this unit is a starting point to illustrate the power and potential of Bayesian inference under complex phylogenetic models in RevBayes. © 2017 by John Wiley & Sons, Inc.

Recent advances in top-down mass spectrometry using native electrospray now enable the analysis of intact protein complexes with relatively small sample amounts in an untargeted mode. Here, we describe how to characterize both homo- and heteropolymeric complexes with high molecular specificity using input data produced by tandem mass spectrometry of whole protein assemblies. The tool described is a “search engine for multi-proteoform complexes,” (SEMPC) and is available for free online. The output is a list of candidate multi-proteoform complexes and scoring metrics, which are used to define a distinct set of one or more unique protein subunits, their overall stoichiometry in the intact complex, and their pre- and post-translational modifications. Thus, we present an approach for the identification and characterization of intact protein complexes from native mass spectrometry data. © 2016 by John Wiley & Sons, Inc.

We have developed ascatNgs to aid researchers in carrying out Allele-Specific Copy number Analysis of Tumours (ASCAT). ASCAT is capable of detecting DNA copy number changes affecting a tumor genome when comparing to a matched normal sample. Additionally, the algorithm estimates the amount of tumor DNA in the sample, known as Aberrant Cell Fraction (ACF). ASCAT itself is an R-package which requires the generation of many file types. Here, we present a suite of tools to help handle this for the user. Our code is available on our GitHub site (https://github.com/cancerit). This unit describes both ‘one-shot’ execution and approaches more suitable for large-scale compute farms. © 2016 by John Wiley & Sons, Inc.

The Mouse Genome Informatics (MGI) resource provides the research community with access to information on the genetics, genomics, and biology of the laboratory mouse. Core data in MGI include gene characterization and function, phenotype and disease model descriptions, DNA and protein sequence data, gene expression data, vertebrate homologies, SNPs, mapping data, and links to other bioinformatics databases. Semantic integration is supported through the use of standardized nomenclature, and through the use of controlled vocabularies such as the mouse Anatomical Dictionary, the Mammalian Phenotype Ontology, and the Gene Ontologies. MGI extracts and organizes data from primary literature. MGI data are shared with and widely displayed from other bioinformatics resources. The database is updated weekly with curated annotations, and regularly adds new datasets and features. This unit provides a guide to using the MGI bioinformatics resource. © 2016 by John Wiley & Sons, Inc.

FlyBase (flybase.org) is the primary online database of genetic, genomic, and functional information about Drosophila species, with a major focus on the model organism Drosophila melanogaster. The long and rich history of Drosophila research, combined with recent surges in genomic-scale and high-throughput technologies, mean that FlyBase now houses a huge quantity of data. Researchers need to be able to rapidly and intuitively query these data, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This unit describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2016 by John Wiley & Sons, Inc.

CaVEMan is an expectation maximization–based somatic substitution-detection algorithm that is written in C. The algorithm analyzes sequence data from a test sample, such as a tumor relative to a reference normal sample from the same patient and the reference genome. It performs a comparative analysis of the tumor and normal sample to derive a probabilistic estimate for putative somatic substitutions. When combined with a set of validated post-hoc filters, CaVEMan generates a set of somatic substitution calls with high recall and positive predictive value. Here we provide instructions for using a wrapper script called cgpCaVEManWrapper, which runs the CaVEMan algorithm and additional downstream post-hoc filters. We describe both a simple one-shot run of cgpCaVEManWrapper and a more in-depth implementation suited to large-scale compute farms. © 2016 by John Wiley & Sons, Inc.