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Network-Based Approaches for Pathway Level Analysis 基于网络的路径级分析方法
Current protocols in bioinformatics Pub Date : 2018-04-09 DOI: 10.1002/cpbi.42
Tin Nguyen, Cristina Mitrea, Sorin Draghici
{"title":"Network-Based Approaches for Pathway Level Analysis","authors":"Tin Nguyen,&nbsp;Cristina Mitrea,&nbsp;Sorin Draghici","doi":"10.1002/cpbi.42","DOIUrl":"10.1002/cpbi.42","url":null,"abstract":"<p>Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.42","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36339898","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: 28
Searching ECOD for Homologous Domains by Sequence and Structure 利用序列和结构搜索ECOD的同源结构域
Current protocols in bioinformatics Pub Date : 2018-04-09 DOI: 10.1002/cpbi.45
R. Dustin Schaeffer, Yuxing Liao, Nick V. Grishin
{"title":"Searching ECOD for Homologous Domains by Sequence and Structure","authors":"R. Dustin Schaeffer,&nbsp;Yuxing Liao,&nbsp;Nick V. Grishin","doi":"10.1002/cpbi.45","DOIUrl":"10.1002/cpbi.45","url":null,"abstract":"<p>ECOD is a database of evolutionary domains from structures deposited in the PDB. Domains in ECOD are classified by a mixed manual/automatic method wherein the bulk of newly deposited structures are classified automatically by protein-protein BLAST. Those structures that cannot be classified automatically are referred to manual curators who use a combination of alignment results, functional analysis, and close reading of the literature to generate novel assignments. ECOD differs from other structural domain resources in that it is continually updated, classifying thousands of proteins per week. ECOD recognizes homology as its key organizing concept, rather than structural or sequence similarity alone. Such a classification scheme provides functional information about proteins of interest by placing them in the correct evolutionary context among all proteins of known structure. This unit demonstrates how to access ECOD via the Web and how to search the database by sequence or structure. It also details the distributable data files available for large-scale bioinformatics users. © 2018 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.45","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36340323","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
Leveraging Experimental Details for an Improved Understanding of Host-Pathogen Interactome 利用实验细节来提高对宿主-病原体相互作用组的理解
Current protocols in bioinformatics Pub Date : 2018-04-09 DOI: 10.1002/cpbi.44
Mais Ammari, Fiona McCarthy, Bindu Nanduri
{"title":"Leveraging Experimental Details for an Improved Understanding of Host-Pathogen Interactome","authors":"Mais Ammari,&nbsp;Fiona McCarthy,&nbsp;Bindu Nanduri","doi":"10.1002/cpbi.44","DOIUrl":"10.1002/cpbi.44","url":null,"abstract":"<p>An increasing proportion of curated host-pathogen interaction (HPI) information is becoming available in interaction databases. These data represent detailed, experimentally-verified, molecular interaction data, which may be used to better understand infectious diseases. By their very nature, HPIs are context dependent, where the outcome of two proteins as interacting or not depends on the precise biological conditions studied and approaches used for identifying these interactions. The associated biology and the technical details of the experiments identifying interacting protein molecules are increasing being curated using defined curation standards but are overlooked in current HPI network modeling. Given the increase in data size and complexity, awareness of the process and variables included in HPI identification and curation, and their effect on data analysis and interpretation is crucial in understanding pathogenesis. We describe the use of HPI data for network modeling, aspects of curation that can help researchers to more accurately model specific infection conditions, and provide examples to illustrate these principles. © 2018 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.44","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36339291","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: 2
Using RegulonDB, the Escherichia coli K-12 Gene Regulatory Transcriptional Network Database 使用RegulonDB，大肠杆菌K-12基因调控转录网络数据库
Current protocols in bioinformatics Pub Date : 2018-04-09 DOI: 10.1002/cpbi.43
Heladia Salgado, Irma Martínez-Flores, Víctor H. Bustamante, Kevin Alquicira-Hernández, Jair S. García-Sotelo, Delfino García-Alonso, Julio Collado-Vides
{"title":"Using RegulonDB, the Escherichia coli K-12 Gene Regulatory Transcriptional Network Database","authors":"Heladia Salgado,&nbsp;Irma Martínez-Flores,&nbsp;Víctor H. Bustamante,&nbsp;Kevin Alquicira-Hernández,&nbsp;Jair S. García-Sotelo,&nbsp;Delfino García-Alonso,&nbsp;Julio Collado-Vides","doi":"10.1002/cpbi.43","DOIUrl":"10.1002/cpbi.43","url":null,"abstract":"<p>In RegulonDB, for over 25 years, we have been gathering knowledge by manual curation from original scientific literature on the regulation of transcription initiation and genome organization in transcription units of the <i>Escherichia coli</i> K-12 genome. This unit describes six basic protocols that can serve as a guiding introduction to the main content of the current version (v9.4) of this electronic resource. These protocols include general navigation as well as searching for specific objects such as genes, gene products, transcription units, promoters, transcription factors, coexpression, and genetic sensory response units or GENSOR Units. In these protocols, the user will find an initial introduction to the concepts pertinent to the protocol, the content obtained when performing the given navigation, and the necessary resources for carrying out the protocol. This easy-to-follow presentation should help anyone interested in quickly seeing all that is currently offered in RegulonDB, including position weight matrices of transcription factors, coexpression values based on published microarrays, and the GENSOR Units unique to RegulonDB that offer regulatory mechanisms in the context of their signals and metabolic consequences. © 2018 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36339903","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: 11
Exploring Biological Networks in 3D, Stereoscopic 3D, and Immersive 3D with iCAVE 探索生物网络在3D，立体3D，沉浸式3D与iCAVE
Current protocols in bioinformatics Pub Date : 2018-04-09 DOI: 10.1002/cpbi.47
Selim Kalayci, Zeynep H. Gümüş
{"title":"Exploring Biological Networks in 3D, Stereoscopic 3D, and Immersive 3D with iCAVE","authors":"Selim Kalayci,&nbsp;Zeynep H. Gümüş","doi":"10.1002/cpbi.47","DOIUrl":"10.1002/cpbi.47","url":null,"abstract":"<p>Biological networks are becoming increasingly large and complex, pushing the limits of existing 2D tools. iCAVE is an open-source software tool for interactive visual explorations of large and complex networks in 3D, stereoscopic 3D, or immersive 3D. It introduces new 3D network layout algorithms and 3D extensions of popular 2D network layout, clustering, and edge bundling algorithms to assist researchers in understanding the underlying patterns in large, multi-layered, clustered, or complex networks. This protocol aims to guide new users on the basic functions of iCAVE for loading data, laying out networks (single or multi-layered), bundling edges, clustering networks, visualizing clusters, visualizing data attributes, and saving output images or videos. It also provides examples on visualizing networks constrained in physical 3D space (e.g., proteins; neurons; brain). It is accompanied by a new version of iCAVE with an enhanced user interface and highlights new features useful for existing users. © 2018 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36340322","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: 5
From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline 从快速数据到高可信度的变体调用:基因组分析工具包最佳实践管道
Current protocols in bioinformatics Pub Date : 2018-03-15 DOI: 10.1002/0471250953.bi1110s43
Geraldine A. Van der Auwera, Mauricio O. Carneiro, Christopher Hartl, Ryan Poplin, Guillermo del Angel, Ami Levy-Moonshine, Tadeusz Jordan, Khalid Shakir, David Roazen, Joel Thibault, Eric Banks, Kiran V. Garimella, David Altshuler, Stacey Gabriel, Mark A. DePristo
{"title":"From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline","authors":"Geraldine A. Van der Auwera,&nbsp;Mauricio O. Carneiro,&nbsp;Christopher Hartl,&nbsp;Ryan Poplin,&nbsp;Guillermo del Angel,&nbsp;Ami Levy-Moonshine,&nbsp;Tadeusz Jordan,&nbsp;Khalid Shakir,&nbsp;David Roazen,&nbsp;Joel Thibault,&nbsp;Eric Banks,&nbsp;Kiran V. Garimella,&nbsp;David Altshuler,&nbsp;Stacey Gabriel,&nbsp;Mark A. DePristo","doi":"10.1002/0471250953.bi1110s43","DOIUrl":"10.1002/0471250953.bi1110s43","url":null,"abstract":"<p>This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high-quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data-processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK. <i>Curr. Protoc. Bioinform</i>. 43:11.10.1-11.10.33. © 2013 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/0471250953.bi1110s43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32843642","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: 5158
Selecting the Right Similarity-Scoring Matrix 选择正确的相似度评分矩阵
Current protocols in bioinformatics Pub Date : 2018-02-16 DOI: 10.1002/0471250953.bi0305s43
William R. Pearson
{"title":"Selecting the Right Similarity-Scoring Matrix","authors":"William R. Pearson","doi":"10.1002/0471250953.bi0305s43","DOIUrl":"10.1002/0471250953.bi0305s43","url":null,"abstract":"<p>Protein sequence similarity searching programs like BLASTP, SSEARCH, and FASTA use scoring matrices that are designed to identify distant evolutionary relationships (BLOSUM62 for BLAST, BLOSUM50 for SSEARCH and FASTA). Different similarity scoring matrices are most effective at different evolutionary distances. “Deep” scoring matrices like BLOSUM62 and BLOSUM50 target alignments with 20% to 30% identity, while “shallow” scoring matrices (e.g., VTML10 to VTML80) target alignments that share 90% to 50% identity, reflecting much less evolutionary change. While “deep” matrices provide very sensitive similarity searches, they also require longer sequence alignments and can sometimes produce alignment overextension into nonhomologous regions. Shallower scoring matrices are more effective when searching for short protein domains, or when the goal is to limit the scope of the search to sequences that are likely to be orthologous between recently diverged organisms. Likewise, in DNA searches, the match and mismatch parameters set evolutionary look-back times and domain boundaries. In this unit, we will discuss the theoretical foundations that drive practical choices of protein and DNA similarity scoring matrices and gap penalties. Deep scoring matrices (BLOSUM62 and BLOSUM50) should be used for sensitive searches with full-length protein sequences, but short domains or restricted evolutionary look-back require shallower scoring matrices. <i>Curr. Protoc. Bioinform</i>. 43:3.5.1-3.5.9. © 2013 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/0471250953.bi0305s43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32100405","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: 113
Cloud Computing with iPlant Atmosphere 云计算与iPlant大气
Current protocols in bioinformatics Pub Date : 2018-02-16 DOI: 10.1002/0471250953.bi0915s43
Sheldon J. McKay, Edwin J. Skidmore, Christopher J. LaRose, Andre W. Mercer, Christos Noutsos
{"title":"Cloud Computing with iPlant Atmosphere","authors":"Sheldon J. McKay,&nbsp;Edwin J. Skidmore,&nbsp;Christopher J. LaRose,&nbsp;Andre W. Mercer,&nbsp;Christos Noutsos","doi":"10.1002/0471250953.bi0915s43","DOIUrl":"10.1002/0471250953.bi0915s43","url":null,"abstract":"<p>Cloud Computing refers to distributed computing platforms that use virtualization software to provide easy access to physical computing infrastructure and data storage, typically administered through a Web interface. Cloud-based computing provides access to powerful servers, with specific software and virtual hardware configurations, while eliminating the initial capital cost of expensive computers and reducing the ongoing operating costs of system administration, maintenance contracts, power consumption, and cooling. This eliminates a significant barrier to entry into bioinformatics and high-performance computing for many researchers. This is especially true of free or modestly priced cloud computing services. The iPlant Collaborative offers a free cloud computing service, Atmosphere, which allows users to easily create and use instances on virtual servers preconfigured for their analytical needs. Atmosphere is a self-service, on-demand platform for scientific computing. This unit demonstrates how to set up, access and use cloud computing in Atmosphere. <i>Curr. Protoc. Bioinform</i>. 43:9.15.1-9.15.20. © 2013 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/0471250953.bi0915s43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34089477","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: 2
An Overview of RNA Sequence Analyses: Structure Prediction, ncRNA Gene Identification, and RNAi Design RNA序列分析综述:结构预测、ncRNA基因鉴定和RNAi设计
Current protocols in bioinformatics Pub Date : 2018-02-16 DOI: 10.1002/0471250953.bi1201s43
Gary D. Stormo
{"title":"An Overview of RNA Sequence Analyses: Structure Prediction, ncRNA Gene Identification, and RNAi Design","authors":"Gary D. Stormo","doi":"10.1002/0471250953.bi1201s43","DOIUrl":"10.1002/0471250953.bi1201s43","url":null,"abstract":"This unit briefly describes the two fundamentally different methods for predicting RNA structures. The first is to find that structure with the minimum free energy of folding, as predicted by various thermodynamic parameters related to base‐pair stacking, loop lengths, and other features. If one has only a single sequence, this thermodynamic approach is the best available method. The second fundamental approach to RNA structure prediction is to use multiple, homologous sequences for which one can infer a common structure, and then try and predict a structure common to all of the sequences. Such an approach is referred to as a comparative method or phylogenetic method of RNA structure prediction. Curr. Protoc. Bioinform. 43:12.1.1‐12.1.3. © 2013 by John Wiley & Sons, Inc.","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/0471250953.bi1201s43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34089475","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: 24
LipidXplorer: Software for Quantitative Shotgun Lipidomics Compatible with Multiple Mass Spectrometry Platforms LipidXplorer:与多个质谱平台兼容的定量霰弹枪脂组学软件
Current protocols in bioinformatics Pub Date : 2018-02-16 DOI: 10.1002/0471250953.bi1412s43
Ronny Herzog, Dominik Schwudke, Andrej Shevchenko
{"title":"LipidXplorer: Software for Quantitative Shotgun Lipidomics Compatible with Multiple Mass Spectrometry Platforms","authors":"Ronny Herzog,&nbsp;Dominik Schwudke,&nbsp;Andrej Shevchenko","doi":"10.1002/0471250953.bi1412s43","DOIUrl":"10.1002/0471250953.bi1412s43","url":null,"abstract":"<p>LipidXplorer is an open-source software kit that supports the identification and quantification of molecular species of any lipid class detected by shotgun experiments performed on any mass spectrometry platform. LipidXplorer does not rely on a database of reference spectra: instead, lipid identification routines are user defined in the declarative molecular fragmentation query language (MFQL). The software supports batch processing of multiple shotgun acquisitions by high-resolution mass mapping, precursor and neutral-loss scanning, and data-dependent MS/MS lending itself to a variety of lipidomics applications in cell biology and molecular medicine. <i>Curr. Protoc. Bioinform</i>. 43:14.12.1-14.12.30. © 2013 by John Wiley &amp; Sons, Inc.</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/0471250953.bi1412s43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34089476","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: 49
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