Pub Date : 2023-12-18DOI: 10.1093/bioinformatics/btad759
Anisha Haldar, Vishal H Oza, Nathaniel S DeVoss, Amanda D Clark, Brittany N Lasseigne
Summary High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics. Availability and Implementation https://github.com/lasseignelab/CoSIA Supplementary information See Supplementary Material
{"title":"CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis","authors":"Anisha Haldar, Vishal H Oza, Nathaniel S DeVoss, Amanda D Clark, Brittany N Lasseigne","doi":"10.1093/bioinformatics/btad759","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad759","url":null,"abstract":"Summary High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics. Availability and Implementation https://github.com/lasseignelab/CoSIA Supplementary information See Supplementary Material","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138743822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1093/bioinformatics/btad752
Min Zeng, Yifan Wu, Yiming Li, Rui Yin, Chengqian Lu, Junwen Duan, Min Li
Motivation There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. Results In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes 8 Transformer blocks to model long-range dependencies within the lncRNA sequence and share information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. Availability The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer. Supplementary information Supplementary data are available at Bioinformatics online.
{"title":"LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism","authors":"Min Zeng, Yifan Wu, Yiming Li, Rui Yin, Chengqian Lu, Junwen Duan, Min Li","doi":"10.1093/bioinformatics/btad752","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad752","url":null,"abstract":"Motivation There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. Results In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes 8 Transformer blocks to model long-range dependencies within the lncRNA sequence and share information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. Availability The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer. Supplementary information Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138743953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1093/bioinformatics/btad751
Xiran Liu, Naama M Kopelman, Noah A Rosenberg
Motivation In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates, so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time. Results We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong, and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K. Availability Clumppling is available at https://github.com/PopGenClustering/Clumppling. Supplementary information Supplementary data are available online.
{"title":"Clumppling: cluster matching and permutation program with integer linear programming","authors":"Xiran Liu, Naama M Kopelman, Noah A Rosenberg","doi":"10.1093/bioinformatics/btad751","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad751","url":null,"abstract":"Motivation In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates, so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time. Results We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong, and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K. Availability Clumppling is available at https://github.com/PopGenClustering/Clumppling. Supplementary information Supplementary data are available online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138692738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1093/bioinformatics/btad756
Marcos Díaz-Gay, Raviteja Vangara, Mark Barnes, Xi Wang, S M Ashiqul Islam, Ian Vermes, Stephen Duke, Nithish Bharadhwaj Narasimman, Ting Yang, Zichen Jiang, Sarah Moody, Sergey Senkin, Paul Brennan, Michael R Stratton, Ludmil B Alexandrov
Motivation Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature. Results Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. Availability SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/. Supplementary information Supplementary data are available at Bioinformatics online.
{"title":"Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment","authors":"Marcos Díaz-Gay, Raviteja Vangara, Mark Barnes, Xi Wang, S M Ashiqul Islam, Ian Vermes, Stephen Duke, Nithish Bharadhwaj Narasimman, Ting Yang, Zichen Jiang, Sarah Moody, Sergey Senkin, Paul Brennan, Michael R Stratton, Ludmil B Alexandrov","doi":"10.1093/bioinformatics/btad756","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad756","url":null,"abstract":"Motivation Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. To evaluate the mutational signatures operative in a cancer genome, one first needs to quantify their activities by estimating the number of mutations imprinted by each signature. Results Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. Availability SigProfilerAssignment is available under the BSD 2-clause license at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/. Supplementary information Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138693242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1093/bioinformatics/btad722
G Vozza, E Bonetti, G Tini, V Favalli, G Frige’, G Bucci, S De Summa, M Zanfardino, F Zapelloni, L Mazzarella
Motivation The steady increment of Whole Genome/Exome sequencing and the development of novel NGS-based gene panels requires continuous testing and validation of variant calling pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize variant calling parameters remains unmet. The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. Availability Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/ To use RecallME, users must obtain a license for ANNOVAR by themselves. Supplementary information Supplementary data are available at Bioinformatics online.
{"title":"Benchmarking and improving the performance of variant-calling pipelines with RecallME","authors":"G Vozza, E Bonetti, G Tini, V Favalli, G Frige’, G Bucci, S De Summa, M Zanfardino, F Zapelloni, L Mazzarella","doi":"10.1093/bioinformatics/btad722","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad722","url":null,"abstract":"Motivation The steady increment of Whole Genome/Exome sequencing and the development of novel NGS-based gene panels requires continuous testing and validation of variant calling pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize variant calling parameters remains unmet. The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. Availability Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/ To use RecallME, users must obtain a license for ANNOVAR by themselves. Supplementary information Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138684216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1093/bioinformatics/btad753
Steve Ma, Longxuan Fan, Sai Anish Konanki, Eva Liu, John H Gennari, Lucian P Smith, Joseph L Hellerstein, Herbert M Sauro
Motivation Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface (GUI). However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not ”model aware”, they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. Results We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of SBML models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g., to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g., between Antimony and SBML). Availability VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony. Supplementary information Documentation and downloads are available at the visual studio marketplace.
{"title":"VSCode-Antimony: A Source Editor for Building, Analyzing, and Translating Antimony Models","authors":"Steve Ma, Longxuan Fan, Sai Anish Konanki, Eva Liu, John H Gennari, Lucian P Smith, Joseph L Hellerstein, Herbert M Sauro","doi":"10.1093/bioinformatics/btad753","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad753","url":null,"abstract":"Motivation Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface (GUI). However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not ”model aware”, they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. Results We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of SBML models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g., to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g., between Antimony and SBML). Availability VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony. Supplementary information Documentation and downloads are available at the visual studio marketplace.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-13DOI: 10.1093/bioinformatics/btad755
William DeGroat, Dinesh Mendhe, Atharva Bhusari, Habiba Abdelhalim, Saman Zeeshan, Zeeshan Ahmed
In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric i.e., Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. Availability The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1). Supplementary information Supplementary data are available at Bioinformatics online.
在这篇文章中，我们介绍了 IntelliGenes，这是一种用于多基因组学探索的新型机器学习（ML）管道，可高精度地发现对疾病预测有重要意义的生物标记物。IntelliGenes 基于一种新颖的方法，它将传统统计技术和前沿的 ML 算法结合在一起，并使用多基因组、临床和人口统计学数据。IntelliGenes 引入了一种新指标，即智能基因（I-Gene）得分，用于衡量单个生物标记对复杂性状预测的重要性。I-基因分数可用于生成个人的I-基因图谱，以了解用于疾病预测的ML的复杂性。IntelliGenes 用户界面友好，便于携带，是一款跨平台应用程序，兼容 Microsoft Windows、macOS 和 UNIX 操作系统。IntelliGenes 不仅有可能实现对常见和罕见疾病的个性化早期检测，还能利用新型 ML 方法为更广泛的研究开辟道路，最终实现个性化干预和新型治疗目标。可用性 IntelliGenes 的源代码可在 GitHub (https://github.com/drzeeshanahmed/intelligenes) 和 Code Ocean (https://codeocean.com/capsule/8638596/tree/v1) 上获取。补充信息 补充数据可在 Bioinformatics online 上获取。
{"title":"IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles","authors":"William DeGroat, Dinesh Mendhe, Atharva Bhusari, Habiba Abdelhalim, Saman Zeeshan, Zeeshan Ahmed","doi":"10.1093/bioinformatics/btad755","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad755","url":null,"abstract":"In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric i.e., Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. Availability The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1). Supplementary information Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1093/bioinformatics/btad744
Adam Midlik, Sreenath Nair, Stephen Anyango, Mandar Deshpande, David Sehnal, Mihaly Varadi, Sameer Velankar
Summary PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. Availability and Implementation PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.
{"title":"PDBImages: A Command Line Tool for Automated Macromolecular Structure Visualization","authors":"Adam Midlik, Sreenath Nair, Stephen Anyango, Mandar Deshpande, David Sehnal, Mihaly Varadi, Sameer Velankar","doi":"10.1093/bioinformatics/btad744","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad744","url":null,"abstract":"Summary PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. Availability and Implementation PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1093/bioinformatics/btad750
Kaspar Märtens, Michele Bortolomeazzi, Lucia Montorsi, Jo Spencer, Francesca Ciccarelli, Christopher Yau
Motivation Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori. While unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that are magnified when they are defined by differentially expressing a small number of genes. Results Typical unsupervised approaches fail to identify such rare sub-populations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC data sets. Availability Implementation of Rarity together with examples are available from the Github repository (https://github.com/kasparmartens/rarity). Supplementary information Supplementary data are available at Bioinformatics online.
{"title":"Rarity: Discovering rare cell populations from single-cell imaging data","authors":"Kaspar Märtens, Michele Bortolomeazzi, Lucia Montorsi, Jo Spencer, Francesca Ciccarelli, Christopher Yau","doi":"10.1093/bioinformatics/btad750","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad750","url":null,"abstract":"Motivation Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori. While unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that are magnified when they are defined by differentially expressing a small number of genes. Results Typical unsupervised approaches fail to identify such rare sub-populations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC data sets. Availability Implementation of Rarity together with examples are available from the Github repository (https://github.com/kasparmartens/rarity). Supplementary information Supplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1093/bioinformatics/btad747
Herui Liao, Jiayu Shang, Yanni Sun
Motivation With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model. Results To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host’s disease status. Availability and implementation https://github.com/liaoherui/GDmicro Supplementary information Supplementary data are available at Bioinformatics online
{"title":"GDmicro: classifying host disease status with GCN and Deep adaptation network based on the human gut microbiome data","authors":"Herui Liao, Jiayu Shang, Yanni Sun","doi":"10.1093/bioinformatics/btad747","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad747","url":null,"abstract":"Motivation With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model. Results To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host’s disease status. Availability and implementation https://github.com/liaoherui/GDmicro Supplementary information Supplementary data are available at Bioinformatics online","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138579537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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