Current protocols in bioinformatics最新文献

{"title":"Protein Sequence Analysis Using the MPI Bioinformatics Toolkit","authors":"Felix Gabler,&nbsp;Seung-Zin Nam,&nbsp;Sebastian Till,&nbsp;Milot Mirdita,&nbsp;Martin Steinegger,&nbsp;Johannes Söding,&nbsp;Andrei N. Lupas,&nbsp;Vikram Alva","doi":"10.1002/cpbi.108","DOIUrl":"10.1002/cpbi.108","url":null,"abstract":"<p>The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best-performing bioinformatics tools and databases, including the state-of-the-art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in-house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. © 2020 The Authors.</p><p><b>Basic Protocol 1</b>: Sequence similarity searching using HHpred</p><p><b>Alternate Protocol</b>: Pairwise sequence comparison using HHpred</p><p><b>Support Protocol</b>: Building a custom multiple sequence alignment using PSI-BLAST and forwarding it as input to HHpred</p><p><b>Basic Protocol 2</b>: Calculation of homology models using HHpred and MODELLER</p><p><b>Basic Protocol 3</b>: Cluster analysis using CLANS</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38719394","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}

{"title":"Exploring Manually Curated Annotations of Intrinsically Disordered Proteins with DisProt","authors":"Federica Quaglia,&nbsp;András Hatos,&nbsp;Damiano Piovesan,&nbsp;Silvio C. E. Tosatto","doi":"10.1002/cpbi.107","DOIUrl":"10.1002/cpbi.107","url":null,"abstract":"<p>DisProt is the major repository of manually curated data for intrinsically disordered proteins collected from the literature. Although lacking a stable tertiary structure under physiological conditions, intrinsically disordered proteins carry out a plethora of biological functions, some of them directly arising from their flexible nature. A growing number of scientific studies have been published during the last few decades in an effort to shed light on their unstructured state, their binding modes, and their functions. DisProt makes use of a team of expert biocurators to provide up-to-date annotations of intrinsically disordered proteins from the literature, making them available to the scientific community. Here we present a comprehensive description on how to use DisProt in different contexts and provide a detailed explanation of how to explore and interpret manually curated annotations of intrinsically disordered proteins. We describe how to search DisProt annotations, using both the web interface and the API for programmatic access. Finally, we explain how to visualize and interpret a DisProt entry, p53, a widely studied protein characterized by the presence of unstructured N-terminal and C-terminal regions. © 2020 Wiley Periodicals LLC.</p><p><b>Basic Protocol 1</b>: Performing a search in DisProt</p><p><b>Support Protocol 1</b>: Downloading options</p><p><b>Support Protocol 2</b>: Programmatic access with DisProt REST API</p><p><b>Basic Protocol 2</b>: Visualizing and interpreting DisProt entries: the p53 use case</p><p><b>Basic Protocol 3</b>: Providing feedback and submitting new intrinsic disorder−related data</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38551117","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}

{"title":"Network Building with the Cytoscape BioGateway App Explained in Five Use Cases","authors":"Rafael Riudavets Puig,&nbsp;Stian Holmås,&nbsp;Vladimir Mironov,&nbsp;Martin Kuiper","doi":"10.1002/cpbi.106","DOIUrl":"10.1002/cpbi.106","url":null,"abstract":"<p>The BioGateway App is a plugin for the Cytoscape network editor, allowing users to interactively build biological networks by querying the Biogateway Resource Description Framework (RDF) triple store. BioGateway contains information from several curated resources including UniProtKB, IntAct, Gene Ontology Annotations, various datasets containing transcription-factor regulatory relations to specific target genes, and more. The BioGateway App facilitates the step-by-step creation of complex SPARQL queries through an intuitive Graphical User Interface, allowing users to build and explore biological interaction networks to assess, among other things, gene regulatory relationships, gene ontology annotations, and protein-protein interactions. As the BioGateway information content is most abundant for human proteins and genes, this article describes the utility of the tool through a series of use cases on these human data, starting from the most basic levels and then detailing applications that address some of the rich complexity of the integrated data. Network refinement and display can be further optimized via the selection and filtering possibilities that the Cytoscape framework provides. The use cases also provide examples to explore network information in other species, as they become supported by BioGateway. © 2020 The Authors.</p><p><b>Basic Protocol 1</b>: Introducing a node from the canvas</p><p><b>Basic Protocol 2</b>: Introducing a node from the query builder</p><p><b>Basic Protocol 3</b>: Exploring molecular relationships between diseases</p><p><b>Basic Protocol 4</b>: Find proteins with protein kinase activity involved in a disease and explore the context around them</p><p><b>Basic Protocol 5</b>: Exploring the potential downstream effects after targeted inhibition of proteins</p><p><b>Support Protocol</b>: Installation of the BioGateway plugin through the Cytoscape App Manager and from source</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38431054","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}

{"title":"Expanding the Perseus Software for Omics Data Analysis With Custom Plugins","authors":"Sung-Huan Yu,&nbsp;Daniela Ferretti,&nbsp;Julia P. Schessner,&nbsp;Jan Daniel Rudolph,&nbsp;Georg H. H. Borner,&nbsp;Jürgen Cox","doi":"10.1002/cpbi.105","DOIUrl":"10.1002/cpbi.105","url":null,"abstract":"<p>The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors.</p><p><b>Basic Protocol 1</b>: Basic steps for R plugins</p><p><b>Support Protocol 1</b>: R plugins with additional arguments</p><p><b>Basic Protocol 2</b>: Basic steps for python plugins</p><p><b>Support Protocol 2</b>: Python plugins with additional arguments</p><p><b>Basic Protocol 3</b>: Basic steps and construction of C# plugins</p><p><b>Basic Protocol 4</b>: Basic steps of construction and connection for R plugins with C# interface</p><p><b>Support Protocol 4</b>: Advanced example of R Plugin with C# interface: UMAP</p><p><b>Basic Protocol 5</b>: Basic steps of construction and connection for python plugins with C# interface</p><p><b>Support Protocol 5</b>: Advanced example of python plugin with C# interface: UMAP</p><p><b>Support Protocol 6</b>: A basic workflow for the analysis of label-free quantification proteomics data using perseus</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38383053","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}

{"title":"Exploring Non-Coding RNAs in RNAcentral","authors":"Blake A. Sweeney,&nbsp;Arina A. Tagmazian,&nbsp;Carlos E. Ribas,&nbsp;Robert D. Finn,&nbsp;Alex Bateman,&nbsp;Anton I. Petrov","doi":"10.1002/cpbi.104","DOIUrl":"10.1002/cpbi.104","url":null,"abstract":"<p>Non-coding RNAs are essential for all life and carry out a wide range of functions. Information about these molecules is distributed across dozens of specialized resources. RNAcentral is a database of non-coding RNA sequences that provides a unified access point to non-coding RNA annotations from &gt;40 member databases and helps provide insight into the function of these RNAs. This article describes different ways of accessing the data, including searching the website and retrieving the data programmatically over web APIs and a public database. We also demonstrate an example Galaxy workflow for using RNAcentral for RNA-seq differential expression analysis. RNAcentral is available at https://rnacentral.org. © 2020 The Authors.</p><p><b>Basic Protocol 1</b>: Viewing RNAcentral sequence reports</p><p><b>Basic Protocol 2</b>: Using RNAcentral text search to explore ncRNA sequences</p><p><b>Basic Protocol 3</b>: Using RNAcentral sequence search</p><p><b>Basic Protocol 4</b>: Using RNAcentral FTP archive</p><p><b>Support Protocol 1</b>: Using web APIs for programmatic data access</p><p><b>Support Protocol 2</b>: Using public Postgres database to export large datasets</p><p><b>Support Protocol 3</b>: Analyze non-coding RNA in RNA-seq datasets using RNAcentral and Galaxy</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38405986","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}

{"title":"How to Illuminate the Dark Proteome Using the Multi-omic OpenProt Resource","authors":"Marie A. Brunet,&nbsp;Amina M. Lekehal,&nbsp;Xavier Roucou","doi":"10.1002/cpbi.103","DOIUrl":"10.1002/cpbi.103","url":null,"abstract":"<p>Ten of thousands of open reading frames (ORFs) are hidden within genomes. These alternative ORFs, or small ORFs, have eluded annotations because they are either small or within unsuspected locations. They are found in untranslated regions or overlap a known coding sequence in messenger RNA and anywhere in a “non-coding” RNA. Serendipitous discoveries have highlighted these ORFs’ importance in biological functions and pathways. With their discovery came the need for deeper ORF annotation and large-scale mining of public repositories to gather supporting experimental evidence. OpenProt, accessible at https://openprot.org/, is the first proteogenomic resource enforcing a polycistronic model of annotation across an exhaustive transcriptome for 10 species. Moreover, OpenProt reports experimental evidence cumulated across a re-analysis of 114 mass spectrometry and 87 ribosome profiling datasets. The multi-omics OpenProt resource also includes the identification of predicted functional domains and evaluation of conservation for all predicted ORFs. The OpenProt web server provides two query interfaces and one genome browser. The query interfaces allow for exploration of the coding potential of genes or transcripts of interest as well as custom downloads of all information contained in OpenProt. © 2020 The Authors.</p><p><b>Basic Protocol 1</b>: Using the Search interface</p><p><b>Basic Protocol 2</b>: Using the Downloads interface</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38260181","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}

{"title":"Using SPAdes De Novo Assembler","authors":"Andrey Prjibelski,&nbsp;Dmitry Antipov,&nbsp;Dmitry Meleshko,&nbsp;Alla Lapidus,&nbsp;Anton Korobeynikov","doi":"10.1002/cpbi.102","DOIUrl":"10.1002/cpbi.102","url":null,"abstract":"<p>SPAdes—St. Petersburg genome Assembler—was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single-cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole-genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC.</p><p><b>Basic Protocol 1</b>: Assembling isolate bacterial datasets</p><p><b>Basic Protocol 2</b>: Assembling metagenomic datasets</p><p><b>Basic Protocol 3</b>: Assembling sets of putative plasmids</p><p><b>Basic Protocol 4</b>: Assembling transcriptomes</p><p><b>Basic Protocol 5</b>: Assembling putative biosynthetic gene clusters</p><p><b>Support Protocol 1</b>: Installing SPAdes</p><p><b>Support Protocol 2</b>: Providing input via command line</p><p><b>Support Protocol 3</b>: Providing input data via YAML format</p><p><b>Support Protocol 4</b>: Restarting previous run</p><p><b>Support Protocol 5</b>: Determining strand-specificity of RNA-seq data</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38062359","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}

{"title":"iSwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis","authors":"Zainab Noor,&nbsp;Abidali Mohamedali,&nbsp;Shoba Ranganathan","doi":"10.1002/cpbi.101","DOIUrl":"10.1002/cpbi.101","url":null,"abstract":"<p>The iSwathX web application processes and normalizes mass spectrometry−based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC.</p><p><b>Basic Protocol 1</b>: Processing, combining, and visualizing two DDA libraries</p><p><b>Basic Protocol 2</b>: Parallel processing and combination of multiple DDA libraries</p><p><b>Basic Protocol 3</b>: Downstream processing, comparison, and visualization of DIA report files</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37993966","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}

{"title":"QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data","authors":"Mehrbod Estaki,&nbsp;Lingjing Jiang,&nbsp;Nicholas A. Bokulich,&nbsp;Daniel McDonald,&nbsp;Antonio González,&nbsp;Tomasz Kosciolek,&nbsp;Cameron Martino,&nbsp;Qiyun Zhu,&nbsp;Amanda Birmingham,&nbsp;Yoshiki Vázquez-Baeza,&nbsp;Matthew R. Dillon,&nbsp;Evan Bolyen,&nbsp;J. Gregory Caporaso,&nbsp;Rob Knight","doi":"10.1002/cpbi.100","DOIUrl":"10.1002/cpbi.100","url":null,"abstract":"<p>QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org. © 2020 The Authors.</p><p><b>Basic Protocol</b>: Using QIIME 2 with microbiome data</p><p><b>Support Protocol</b>: Further microbiome analyses</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37879861","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}

{"title":"Analyzing Protein Disorder with IUPred2A","authors":"Gábor Erdős,&nbsp;Zsuzsanna Dosztányi","doi":"10.1002/cpbi.99","DOIUrl":"10.1002/cpbi.99","url":null,"abstract":"<p>IUPred2A is a combined prediction tool designed to discover intrinsically disordered or conditionally disordered proteins and protein regions. Intrinsically disordered regions exist without a well-defined three-dimensional structure in isolation but carry out important biological functions. Over the years, various prediction methods have been developed to characterize disordered regions. The existence of disordered segments can also be dependent on different factors such as binding partners or environmental traits like pH or redox potential, and recognizing such regions represents additional computational challenges. In this article, we present detailed instructions on how to use IUPred2A, one of the most widely used tools for the prediction of disordered regions/proteins or conditionally disordered segments, and provide examples of how the predictions can be interpreted in different contexts. © 2020 The Authors.</p><p><b>Basic Protocol 1</b>: Analyzing disorder propensity with IUPred2A online</p><p><b>Basic Protocol 2</b>: Analyzing disordered binding regions using ANCHOR2</p><p><b>Support Protocol 1</b>: Interpretation of the results</p><p><b>Basic Protocol 3</b>: Analyzing redox-sensitive disordered regions</p><p><b>Support Protocol 2</b>: Download options</p><p><b>Support Protocol 3</b>: REST API for programmatic purposes</p><p><b>Basic Protocol 4</b>: Using IUPred2A locally</p>","PeriodicalId":10958,"journal":{"name":"Current protocols in bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cpbi.99","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37792090","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}