Current protocols in bioinformatics最新文献

MetaBridge is a web-based tool designed to facilitate the integration of metabolomics with other “omics” data types such as transcriptomics and proteomics. It uses data from the MetaCyc metabolic pathway database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to map metabolite compounds to directly interacting upstream or downstream enzymes in enzymatic reactions and metabolic pathways. The resulting list of enzymes can then be integrated with transcriptomics or proteomics data via protein-protein interaction networks to perform integrative multi-omics analyses. MetaBridge was developed to be intuitive and easy to use, requiring little to no prior computational experience. The protocols described here detail all steps involved in the use of MetaBridge, from preparing input data and performing metabolite mapping to utilizing the results to build a protein-protein interaction network. © 2020 by John Wiley & Sons, Inc.

Basic Protocol 1: Mapping metabolite data using MetaCyc identifiers

Basic Protocol 2: Mapping metabolite data using KEGG identifiers

Support Protocol 1: Converting compound names to HMDB IDs

Support Protocol 2: Submitting mapped genes produced by MetaBridge for protein-protein interaction (PPI) network construction

Visualizing protein data remains a challenging and stimulating task. Useful and intuitive visualization tools may help advance biomolecular and medical research; unintuitive tools may bar important breakthroughs. This protocol describes two use cases for the CellMap (http://cellmap.protein.properties) web tool. The tool allows researchers to visualize human protein-protein interaction data constrained by protein subcellular localizations. In the simplest form, proteins are visualized on cell images that also show protein-protein interactions (PPIs) through lines (edges) connecting the proteins across the compartments. At a glance, this simultaneously highlights spatial constraints that proteins are subject to in their physical environment and visualizes PPIs against these localizations. Visualizing two realities helps in decluttering the protein interaction visualization from “hairball” phenomena that arise when single proteins or groups thereof interact with hundreds of partners. © 2019 The Authors.

Basic Protocol 1: Visualizing proteins and their interactions on cell images

Basic Protocol 2: Displaying all interaction partners for a protein

Ggtree is an R/Bioconductor package for visualizing tree-like structures and associated data. After 5 years of continual development, ggtree has been evolved as a package suite that contains treeio for tree data input and output, tidytree for tree data manipulation, and ggtree for tree data visualization. Ggtree was originally designed to work with phylogenetic trees, and has been expanded to support other tree-like structures, which extends the application of ggtree to present tree data in other disciplines. This article contains five basic protocols describing how to visualize trees using the grammar of graphics syntax, how to visualize hierarchical clustering results with associated data, how to estimate bootstrap values and visualize the values on the tree, how to estimate continuous and discrete ancestral traits and visualize ancestral states on the tree, and how to visualize a multiple sequence alignment with a phylogenetic tree. The ggtree package is freely available at https://www.bioconductor.org/packages/ggtree. © 2020 by John Wiley & Sons, Inc.

Basic Protocol 1: Using grammar of graphics for visualizing trees

Basic Protocol 2: Visualizing hierarchical clustering using ggtree

Basic Protocol 3: Visualizing bootstrap values as symbolic points

Basic Protocol 4: Visualizing ancestral status

Basic Protocol 5: Visualizing a multiple sequence alignment with a phylogenetic tree

We present Mothulity—a novel interface for Mothur, a well-established tool for 16S/ITS biodiversity analysis. Although Mothur is a well-documented and virtually complete software suite, its proper execution might be challenging for first-time users, and editing the Mothur batch scripts is time consuming even for experienced users. Mothur produces little to no graphical output, leaving the generation of plots to the user. Mothulity minimizes the chance of human error through a minimalistic yet powerful interface, with most of the analysis parameters predefined or adjusted automatically. Time spent on running the analysis is drastically reduced, since Mothulity produces an HTML report with publication-quality figures. Finally, Mothulity can be conveniently used with the SLURM workload manager, and is thereby suitable for a range of computing facilities. © 2020 by John Wiley & Sons, Inc.

Basic Protocol 1: Standard operational procedure (SOP)

Basic Protocol 2: Generating report on pre-processed data

BioModelAnalyzer (BMA) is an open-source graphical tool for the development of executable models of protein and gene networks within cells. Based upon the Qualitative Networks formalism, the user can rapidly construct large networks, either manually or by connecting motifs selected from a built-in library. After the appropriate functions for each variable are defined, the user has access to three analysis engines to test the model. In addition to standard simulation tools, BMA includes an interface to the stability-testing algorithm and to a graphical Linear Temporal Logic (LTL) editor and analysis tool. Alongside this, we have developed a novel ChatBot to aid users constructing LTL queries and to explain the interface and run through tutorials. Here we present worked examples of model construction and testing via the interface. As an initial example, we discuss fate decisions in Dictyostelium discoidum and cAMP signaling. We go on to describe the workflow leading to the construction of a published model of the germline of C. elegans. Finally, we demonstrate how to construct simple models from the built-in network motif library. © 2020 by John Wiley & Sons, Inc.

Basic Protocol 1: Modeling the signaling network of Dictyostelium discoidum

Basic Protocol 2: Modeling the germline progression of Caenorhabditis elegans

Basic Protocol 3: Constructing a model of the cell cycle using motifs

The Molecular INTeractions Database (MINT) is a public database designed to store information about protein interactions. Protein interactions are extracted from scientific literature and annotated in the database by expert curators. Currently (October 2019), MINT contains information on more than 26,000 proteins and more than 131,600 interactions in over 30 model organisms. This article provides protocols for searching MINT over the Internet, using the new MINT Web Page. © 2020 by John Wiley & Sons, Inc.

Basic Protocol 1: Searching MINT over the internet

Alternate Protocol: MINT visualizer

Basic Protocol 2: Submitting interaction data

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc.

Basic Protocol 1: Search for a target and view details

Alternate Protocol: Search for dark target and view details

Basic Protocol 2: Filter a target list to get refined results

MathIOmica is a package for bioinformatics, written in the Wolfram language, that provides multiple utilities to facilitate the analysis of longitudinal data generated from omics experiments, including transcriptomics, proteomics, and metabolomics data, as well as any generalized time series. MathIOmica uses Mathematica's notebook interface, wherein users can import longitudinal datasets, carry out quality control and normalization, generate time series, and classify temporal trends. MathIOmica provides spectral methods based on periodograms and autocorrelations for automatically detecting classes of temporal behavior and allowing the user to visualize collective temporal behavior, and also assess biological significance through Gene Ontology and pathway enrichment analyses. MathIOmica's time-series classification methods address common issues including missing data and uneven sampling in measurements. As such, the software is ideally suited for the analysis of experimental data from individualized profiling of subjects, can facilitate analysis of data from the emerging field of individualized health monitoring, and can detect temporal trends that may be associated with adverse health events. In this article, we import a transcriptomics (RNA-sequencing) dataset collected over multiple timepoints and generate time series for each transcript represented in the data. We classify the time series to identify classes of significant temporal trends (using autocorrelations). We assess statistical significance cutoffs in the classification by generating null distributions using randomly resampled time series. We then visualize the significant trends in heatmaps and assess biological significance using enrichment analyses. Finally, we visualize pathway results for statistically significant pathways of interest. © 2019 by John Wiley & Sons, Inc.

Basic Protocol: Time series analysis of transcriptomics expression dataset

The Conserved Domain Database (CDD) is a freely available resource for the annotation of sequences with the locations of conserved protein domain footprints, as well as functional sites and motifs inferred from these footprints. It includes protein domain and protein family models curated in house by CDD staff, as well as imported from a variety of other sources. The latest CDD release (v3.17, April 2019) contains more than 57,000 domain models, of which almost 15,000 were curated by CDD staff. The CDD curation effort increases coverage and provides finer-grained classifications of common and widely distributed protein domain families, for which a wealth of functional and structural data have become available. The CDD maintains both live search capabilities and an archive of pre-computed domain annotations for a selected subset of sequences tracked by the NCBI's Entrez protein database. These can be retrieved or computed for a single sequence using CD-Search or in bulk using Batch CD-Search, or computed via standalone RPS-BLAST plus the rpsbproc software package. The CDD can be accessed via https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml. The three protocols listed here describe how to perform a CD-Search (Basic Protocol 1), a Batch CD-Search (Basic Protocol 2), and a Standalone RPS-BLAST and rpsbproc (Basic Protocol 3). © 2019 The Authors.

Basic Protocol 1: CD-search

Basic Protocol 2: Batch CD-search

Basic Protocol 3: Standalone RPS-BLAST and rpsbproc

Cover: In Chong et al. (http://doi.org/10.1002/cpbi.86), the image shows the MetaboAnalyst 4.0 flowchart. There are two main components: a data processing component to deal with different data inputs, and a data analysis component containing individual modules which can be categorized into “Exploratory Statistical Analysis,” “Functional Enrichment Analysis,” and “Data Integration and Systems Biology.” PROT X refers to the relevant protocol for that component/module.