Current protocols in bioinformatics最新文献_第2页

MetaBridge: An Integrative Multi-Omics Tool for Metabolite-Enzyme Mapping metbridge:代谢酶图谱的综合多组学工具

Current protocols in bioinformatics Pub Date : 2020-03-21 DOI: 10.1002/cpbi.98

Travis Blimkie, Amy Huei-Yi Lee, Robert E.W. Hancock

{"title":"MetaBridge: An Integrative Multi-Omics Tool for Metabolite-Enzyme Mapping","authors":"Travis Blimkie, Amy Huei-Yi Lee, Robert E.W. Hancock","doi":"10.1002/cpbi.98","DOIUrl":"10.1002/cpbi.98","url":null,"abstract":"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 identifiersBasic Protocol 2: Mapping metabolite data using KEGG identifiersSupport Protocol 1: Converting compound names to HMDB IDsSupport Protocol 2: Submitting mapped genes produced by MetaBridge for protein-protein interaction (PPI) network construction","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.98","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37760312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Visualizing Human Protein-Protein Interactions and Subcellular Localizations on Cell Images Through CellMap 可视化人类蛋白质-蛋白质相互作用和亚细胞定位在细胞图像上通过细胞地图

Current protocols in bioinformatics Pub Date : 2020-03-09 DOI: 10.1002/cpbi.97

Christian Dallago, Tatyana Goldberg, Miguel Angel Andrade-Navarro, Gregorio Alanis-Lobato, Burkhard Rost

{"title":"Visualizing Human Protein-Protein Interactions and Subcellular Localizations on Cell Images Through CellMap","authors":"Christian Dallago, Tatyana Goldberg, Miguel Angel Andrade-Navarro, Gregorio Alanis-Lobato, Burkhard Rost","doi":"10.1002/cpbi.97","DOIUrl":"10.1002/cpbi.97","url":null,"abstract":"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 imagesBasic Protocol 2: Displaying all interaction partners for a protein","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.97","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37718929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Using ggtree to Visualize Data on Tree-Like Structures 使用ggtree在树状结构上可视化数据

Current protocols in bioinformatics Pub Date : 2020-03-05 DOI: 10.1002/cpbi.96

Guangchuang Yu

{"title":"Using ggtree to Visualize Data on Tree-Like Structures","authors":"Guangchuang Yu","doi":"10.1002/cpbi.96","DOIUrl":"10.1002/cpbi.96","url":null,"abstract":"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 treesBasic Protocol 2: Visualizing hierarchical clustering using ggtreeBasic Protocol 3: Visualizing bootstrap values as symbolic pointsBasic Protocol 4: Visualizing ancestral statusBasic Protocol 5: Visualizing a multiple sequence alignment with a phylogenetic tree","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.96","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37730487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 713

Mothulity Facilitates 16S/ITS Amplicon Diversity Analysis Mothulity便于16S/ITS扩增子多样性分析

Current protocols in bioinformatics Pub Date : 2020-02-24 DOI: 10.1002/cpbi.94

D. Izak, A. Gromadka, S. Kaczanowski

引用次数: 0

Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer 用BioModelAnalyzer构建和分析细胞信号传导的计算模型

Current protocols in bioinformatics Pub Date : 2020-02-20 DOI: 10.1002/cpbi.95

Benjamin A. Hall, Jasmin Fisher

{"title":"Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer","authors":"Benjamin A. Hall, Jasmin Fisher","doi":"10.1002/cpbi.95","DOIUrl":"10.1002/cpbi.95","url":null,"abstract":"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 discoidumBasic Protocol 2: Modeling the germline progression of Caenorhabditis elegansBasic Protocol 3: Constructing a model of the cell cycle using motifs","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.95","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37661228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Using the MINT Database to Search Protein Interactions 使用MINT数据库搜索蛋白质相互作用

Current protocols in bioinformatics Pub Date : 2020-01-16 DOI: 10.1002/cpbi.93

Alberto Calderone, Marta Iannuccelli, Daniele Peluso, Luana Licata

引用次数: 12

How to Illuminate the Druggable Genome Using Pharos 如何利用Pharos照亮可用药基因组

Current protocols in bioinformatics Pub Date : 2020-01-03 DOI: 10.1002/cpbi.92

Timothy Sheils, Stephen L. Mathias, Vishal B. Siramshetty, Giovanni Bocci, Cristian G. Bologa, Jeremy J. Yang, Anna Waller, Noel Southall, Dac-Trung Nguyen, Tudor I. Oprea

{"title":"How to Illuminate the Druggable Genome Using Pharos","authors":"Timothy Sheils, Stephen L. Mathias, Vishal B. Siramshetty, Giovanni Bocci, Cristian G. Bologa, Jeremy J. Yang, Anna Waller, Noel Southall, Dac-Trung Nguyen, Tudor I. Oprea","doi":"10.1002/cpbi.92","DOIUrl":"10.1002/cpbi.92","url":null,"abstract":"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 detailsAlternate Protocol: Search for dark target and view detailsBasic Protocol 2: Filter a target list to get refined results","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.92","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37509395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

The MathIOmica Toolbox: General Analysis Utilities for Dynamic Omics Datasets MathIOmica工具箱:动态组学数据集的通用分析工具

Current protocols in bioinformatics Pub Date : 2019-12-18 DOI: 10.1002/cpbi.91

George I. Mias, Minzhang Zheng

{"title":"The MathIOmica Toolbox: General Analysis Utilities for Dynamic Omics Datasets","authors":"George I. Mias, Minzhang Zheng","doi":"10.1002/cpbi.91","DOIUrl":"10.1002/cpbi.91","url":null,"abstract":"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","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.91","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37469965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

NCBI's Conserved Domain Database and Tools for Protein Domain Analysis NCBI的保守结构域数据库和蛋白质结构域分析工具

Current protocols in bioinformatics Pub Date : 2019-12-18 DOI: 10.1002/cpbi.90

Mingzhang Yang, Myra K. Derbyshire, Roxanne A. Yamashita, Aron Marchler-Bauer

{"title":"NCBI's Conserved Domain Database and Tools for Protein Domain Analysis","authors":"Mingzhang Yang, Myra K. Derbyshire, Roxanne A. Yamashita, Aron Marchler-Bauer","doi":"10.1002/cpbi.90","DOIUrl":"10.1002/cpbi.90","url":null,"abstract":"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-searchBasic Protocol 2: Batch CD-searchBasic Protocol 3: Standalone RPS-BLAST and rpsbproc","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.90","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37469483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 107

Issue Information TOC 发布信息TOC

Current protocols in bioinformatics Pub Date : 2019-12-17 DOI: 10.1002/cpbi.65

引用次数: 0