Current protocols in bioinformatics最新文献

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 portal

Support Protocol 1: Batch downloading

Support Protocol 2: Using the cart to download files

Support Protocol 3: Visualize data

Alternate Protocol: Query building and programmatic access

INTER-Species Protein Interaction Analysis (INTERSPIA) is a web application for identifying diverse patterns of protein-protein interactions (PPIs) in different species. Given a set of proteins of interest to the user, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins as well as different or common patterns of PPIs among the proteins in multiple species through a server-side pipeline. Second, it visualizes the dynamics of PPIs in multiple species via an easy-to-use web interface. This article contains a basic protocol describing how to visualize diverse patterns of PPIs of input proteins in multiple species, and how to use them for functional analysis in the web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/. © 2019 by John Wiley & Sons, Inc.

Basic Protocol: Running INTERSPIA using a list of input proteins

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 MIGRATE

Basic Protocol 2: Population model specification

Basic Protocol 3: Prior distribution specification

Basic Protocol 4: Model selection

Support Protocol 1: Installing the program MIGRATE

Support Protocol 2: Installation of parallel MIGRATE

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 p_m) corresponding to five informative SNPs. Positions p₁ to p_n are within a hemizygously deleted genomic segment, A, whereas genomic positions p_{n + 1} to p_m 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.

One of the greatest challenges in the bioinformatic analysis of human sequencing data is identifying which variants are pathogenic. Numerous databases and tools have been generated to address this difficulty. However, these many useful data and tools are broadly dispersed, requiring users to search for their variants of interest through human genetic databases, variant function prediction tools, and model organism databases. To solve this problem, we collected data and observed workflows of human geneticists, clinicians, and model organism researchers to carefully select and display valuable information that facilitates the evaluation of whether a variant is likely to be pathogenic. This program, Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) v1.2, allows users to collect relevant data from 27 public sources for further efficient bioinformatic analysis of the pathogenicity of human variants. © 2019 by John Wiley & Sons, Inc.

MEta-Server for protein Sequence Analysis (MESSA) is a tool that facilitates widespread protein sequence analysis by gathering structural (local sequence properties and three-dimensional structure) and functional (annotations from SWISS-PROT, Gene Ontology terms, and enzyme classification) predictions for a query protein of interest. MESSA uses multiple well-established tools to offer consensus-based predictions on important aspects of protein sequence analysis. Being freely available for noncommercial users and with a user-friendly interface, MESSA serves as an umbrella platform that overcomes the absence of a comprehensive tool for predictive protein analysis. This article reveals how to access MESSA via the Web and shows how to input a protein sequence to analyze using the MESSA web server. It also includes a detailed explanation of the output from MESSA to aid in better interpretation of results. © 2019 by John Wiley & Sons, Inc.

High-throughput DNA sequencing technology provides base-level and statistically rich information about the genomic content of a sample. In the contexts of cancer research and precision oncology, thousands of genomes from paired tumor and matched normal samples are profiled and processed to determine somatic copy-number changes and single-nucleotide variations. Higher-order informative analyses, in the form of allele-specific copy-number assessments or subclonality quantification, require reliable estimates of tumor DNA ploidy and tumor cellularity. CLONETv2 provides a complete set of functions to process matched normal and tumor pairs using patient-specific genotype data, is independent of low-level tools (e.g., aligner, segmentation algorithm, mutation caller) and offers high-level functions to compute allele-specific copy number from segmented data and to identify subclonal population in the input sample. CLONETv2 is applicable to whole-genome, whole-exome and targeted sequencing data generated either from tissue or from liquid biopsy samples. © 2019 The Authors.

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.

The 16S ribosomal RNA (rRNA) gene is one of the scaffolding molecules of the prokaryotic ribosome. Because this gene is slow to evolve and has very well conserved regions, this gene is used to reconstruct phylogenies in prokaryotes. Universal primers can be used to amplify the gene in prokaryotes including bacteria and archaea. To determine the microbial composition in microbial communities using high-throughput short-read sequencing techniques, primers are designed to span two or three of the nine variable regions of the gene. Mothur, developed in 2009, is a suite of tools to study the composition and structure of bacterial communities. This package is freely available from the developers (https://www.mothur.org). This protocol will show how to (1) perform preprocessing of sequences to remove errors, (2) perform operational taxonomic unit (OTU) analysis to determine alpha and beta diversity, and (3) determine the taxonomic profile of OTUs and the environmental sample. © 2019 The Authors.