NAR Genomics and Bioinformatics最新文献_第6页

bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data. bcRflow：从非靶向转录组数据表征 B 细胞受体谱系的 Nextflow 管道。

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NAR Genomics and Bioinformatics Pub Date : 2024-10-15 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae137

Brent T Schlegel, Michael Morikone, Fangping Mu, Wan-Yee Tang, Gary Kohanbash, Dhivyaa Rajasundaram

{"title":"bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data.","authors":"Brent T Schlegel, Michael Morikone, Fangping Mu, Wan-Yee Tang, Gary Kohanbash, Dhivyaa Rajasundaram","doi":"10.1093/nargab/lqae137","DOIUrl":"10.1093/nargab/lqae137","url":null,"abstract":"B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae137"},"PeriodicalIF":4.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142476445","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: 0

Dynamic configuration and data security for bioinformatics cloud services with the Laniakea Dashboard. 利用 Laniakea Dashboard 实现生物信息学云服务的动态配置和数据安全。

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NAR Genomics and Bioinformatics Pub Date : 2024-10-10 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae140

Marco Antonio Tangaro, Marica Antonacci, Giacinto Donvito, Nadina Foggetti, Pietro Mandreoli, Daniele Colombo, Graziano Pesole, Federico Zambelli

{"title":"Dynamic configuration and data security for bioinformatics cloud services with the Laniakea Dashboard.","authors":"Marco Antonio Tangaro, Marica Antonacci, Giacinto Donvito, Nadina Foggetti, Pietro Mandreoli, Daniele Colombo, Graziano Pesole, Federico Zambelli","doi":"10.1093/nargab/lqae140","DOIUrl":"10.1093/nargab/lqae140","url":null,"abstract":"Technological advances in high-throughput technologies improve our ability to explore the molecular mechanisms of life. Computational infrastructures for scientific applications fulfil a critical role in harnessing this potential. However, there is an ongoing need to improve accessibility and implement robust data security technologies to allow the processing of sensitive data, particularly human genetic data. Scientific clouds have emerged as a promising solution to meet these needs. We present three components of the Laniakea software stack, initially developed to support the provision of private on-demand Galaxy instances. These components can be adopted by providers of scientific cloud services built on the INDIGO PaaS layer. The Dashboard translates configuration template files into user-friendly web interfaces, enabling the easy configuration and launch of on-demand applications. The secret management and the encryption components, integrated within the Dashboard, support the secure handling of passphrases and credentials and the deployment of block-level encrypted storage volumes for managing sensitive data in the cloud environment. By adopting these software components, scientific cloud providers can develop convenient, secure and efficient on-demand services for their users.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae140"},"PeriodicalIF":4.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401507","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: 0

scATAcat: cell-type annotation for scATAC-seq data. scATAcat：用于 scATAC-seq 数据的细胞类型注释。

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NAR Genomics and Bioinformatics Pub Date : 2024-10-08 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae135

Aybuge Altay, Martin Vingron

引用次数: 0

TrajectoryGeometry suggests cell fate decisions can involve branches rather than bifurcations. 轨迹几何表明，细胞命运的决定可能涉及分支而非分叉。

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NAR Genomics and Bioinformatics Pub Date : 2024-10-08 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae139

Anna Laddach, Vassilis Pachnis, Michael Shapiro

{"title":"TrajectoryGeometry suggests cell fate decisions can involve branches rather than bifurcations.","authors":"Anna Laddach, Vassilis Pachnis, Michael Shapiro","doi":"10.1093/nargab/lqae139","DOIUrl":"10.1093/nargab/lqae139","url":null,"abstract":"Differentiation of multipotential progenitor cells is a key process in the development of any multi-cellular organism and often continues throughout its life. It is often assumed that a bi-potential progenitor develops along a (relatively) straight trajectory until it reaches a decision point where the trajectory bifurcates. At this point one of two directions is chosen, each direction representing the unfolding of a new transcriptional programme. However, we have lacked quantitative means for testing this model. Accordingly, we have developed the R package TrajectoryGeometry. Applying this to published data we find several examples where, rather than bifurcate, developmental pathways branch. That is, the bipotential progenitor develops along a relatively straight trajectory leading to one of its potential fates. A second relatively straight trajectory branches off from this towards the other potential fate. In this sense only cells that branch off to follow the second trajectory make a 'decision'. Our methods give precise descriptions of the genes and cellular pathways involved in these trajectories. We speculate that branching may be the more common behaviour and may have advantages from a control-theoretic viewpoint.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae139"},"PeriodicalIF":4.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11459380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142393890","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: 0

Genome wide clustering on integrated chromatin states and Micro-C contacts reveals chromatin interaction signatures. 整合染色质状态和 Micro-C 接触的全基因组聚类揭示了染色质相互作用特征。

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NAR Genomics and Bioinformatics Pub Date : 2024-10-03 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae136

Corinne E Sexton, Sylvia Victor Paul, Dylan Barth, Mira V Han

引用次数: 0

Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package. 利用 Signifinder Bioconductor 软件包探索大量、单细胞和空间转录组学数据中的公共癌症基因表达特征。

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NAR Genomics and Bioinformatics Pub Date : 2024-10-03 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae138

Stefania Pirrotta, Laura Masatti, Anna Bortolato, Anna Corrà, Fabiola Pedrini, Martina Aere, Giovanni Esposito, Paolo Martini, Davide Risso, Chiara Romualdi, Enrica Calura

{"title":"Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package.","authors":"Stefania Pirrotta, Laura Masatti, Anna Bortolato, Anna Corrà, Fabiola Pedrini, Martina Aere, Giovanni Esposito, Paolo Martini, Davide Risso, Chiara Romualdi, Enrica Calura","doi":"10.1093/nargab/lqae138","DOIUrl":"10.1093/nargab/lqae138","url":null,"abstract":"Understanding cancer mechanisms, defining subtypes, predicting prognosis and assessing therapy efficacy are crucial aspects of cancer research. Gene-expression signatures derived from bulk gene expression data have played a significant role in these endeavors over the past decade. However, recent advancements in high-resolution transcriptomic technologies, such as single-cell RNA sequencing and spatial transcriptomics, have revealed the complex cellular heterogeneity within tumors, necessitating the development of computational tools to characterize tumor mass heterogeneity accurately. Thus we implemented signifinder, a novel R Bioconductor package designed to streamline the collection and use of cancer transcriptional signatures across bulk, single-cell, and spatial transcriptomics data. Leveraging publicly available signatures curated by signifinder, users can assess a wide range of tumor characteristics, including hallmark processes, therapy responses, and tumor microenvironment peculiarities. Through three case studies, we demonstrate the utility of transcriptional signatures in bulk, single-cell, and spatial transcriptomic data analyses, providing insights into cell-resolution transcriptional signatures in oncology. Signifinder represents a significant advancement in cancer transcriptomic data analysis, offering a comprehensive framework for interpreting high-resolution data and addressing tumor complexity.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae138"},"PeriodicalIF":4.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373105","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: 0

StableMate: a statistical method to select stable predictors in omics data. StableMate：一种在 omics 数据中选择稳定预测因子的统计方法。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-28 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae130

Yidi Deng, Jiadong Mao, Jarny Choi, Kim-Anh Lê Cao

{"title":"StableMate: a statistical method to select stable predictors in omics data.","authors":"Yidi Deng, Jiadong Mao, Jarny Choi, Kim-Anh Lê Cao","doi":"10.1093/nargab/lqae130","DOIUrl":"https://doi.org/10.1093/nargab/lqae130","url":null,"abstract":"Identifying statistical associations between biological variables is crucial to understanding molecular mechanisms. Most association studies are based on correlation or linear regression analyses, but the identified associations often lack reproducibility and interpretability due to the complexity and variability of omics datasets, making it difficult to translate associations into meaningful biological hypotheses. We developed StableMate, a regression framework, to address these challenges through a process of variable selection across heterogeneous datasets. Given datasets from different environments, such as experimental batches, StableMate selects environment-agnostic (stable) and environment-specific predictors in predicting the response of interest. Stable predictors represent robust functional dependencies with the response, and can be used to build regression models that make generalizable predictions in unseen environments. We applied StableMate to (i) RNA sequencing data of breast cancer to discover genes that consistently predict estrogen receptor expression across disease status; (ii) metagenomics data to identify microbial signatures that show persistent association with colon cancer across study cohorts; and (iii) single-cell RNA sequencing data of glioblastoma to discern signature genes associated with the development of pro-tumour microglia regardless of cell location. Our case studies demonstrate that StableMate is adaptable to regression and classification analyses and achieves comprehensive characterization of biological systems for different omics data types.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae130"},"PeriodicalIF":4.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11437361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355524","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: 0

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison. scDAPP：为跨组比较而优化的综合性单细胞转录组学分析管道。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-28 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae134

Alexander Ferrena, Xiang Yu Zheng, Kevyn Jackson, Bang Hoang, Bernice E Morrow, Deyou Zheng

{"title":"scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison.","authors":"Alexander Ferrena, Xiang Yu Zheng, Kevyn Jackson, Bang Hoang, Bernice E Morrow, Deyou Zheng","doi":"10.1093/nargab/lqae134","DOIUrl":"10.1093/nargab/lqae134","url":null,"abstract":"Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 4","pages":"lqae134"},"PeriodicalIF":4.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11437360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142336666","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: 0

RNAMotifProfile: a graph-based approach to build RNA structural motif profiles. RNAMotifProfile：一种基于图谱的 RNA 结构主题图谱构建方法。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-26 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae128

Md Mahfuzur Rahaman, Shaojie Zhang

{"title":"RNAMotifProfile: a graph-based approach to build RNA structural motif profiles.","authors":"Md Mahfuzur Rahaman, Shaojie Zhang","doi":"10.1093/nargab/lqae128","DOIUrl":"https://doi.org/10.1093/nargab/lqae128","url":null,"abstract":"RNA structural motifs are the recurrent segments in RNA three-dimensional structures that play a crucial role in the functional diversity of RNAs. Understanding the similarities and variations within these recurrent motif groups is essential for gaining insights into RNA structure and function. While recurrent structural motifs are generally assumed to be composed of the same isosteric base interactions, this consistent pattern is not observed across all examples of these motifs. Existing methods for analyzing and comparing RNA structural motifs may overlook variations in base interactions and associated nucleotides. RNAMotifProfile is a novel profile-to-profile alignment algorithm that generates a comprehensive profile from a group of structural motifs, incorporating all base interactions and associated nucleotides at each position. By structurally aligning input motif instances using a guide-tree-based approach, RNAMotifProfile captures the similarities and variations within recurrent motif groups. Additionally, RNAMotifProfile can function as a motif search tool, enabling the identification of instances of a specific motif family by searching with the corresponding profile. The ability to generate accurate and comprehensive profiles for RNA structural motif families, and to search for these motifs, facilitates a deeper understanding of RNA structure-function relationships and potential applications in RNA engineering and therapeutic design.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae128"},"PeriodicalIF":4.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11426329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355523","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: 0

Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer. 剪接触发的癌症蛋白质复合界面畸变的重要预后意义

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NAR Genomics and Bioinformatics Pub Date : 2024-09-26 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae133

Khalique Newaz, Christoph Schaefers, Katja Weisel, Jan Baumbach, Dmitrij Frishman

{"title":"Prognostic importance of splicing-triggered aberrations of protein complex interfaces in cancer.","authors":"Khalique Newaz, Christoph Schaefers, Katja Weisel, Jan Baumbach, Dmitrij Frishman","doi":"10.1093/nargab/lqae133","DOIUrl":"https://doi.org/10.1093/nargab/lqae133","url":null,"abstract":"Aberrant alternative splicing (AS) is a prominent hallmark of cancer. AS can perturb protein-protein interactions (PPIs) by adding or removing interface regions encoded by individual exons. Identifying prognostic exon-exon interactions (EEIs) from PPI interfaces can help discover AS-affected cancer-driving PPIs that can serve as potential drug targets. Here, we assessed the prognostic significance of EEIs across 15 cancer types by integrating RNA-seq data with three-dimensional (3D) structures of protein complexes. By analyzing the resulting EEI network we identified patient-specific perturbed EEIs (i.e., EEIs present in healthy samples but absent from the paired cancer samples or vice versa) that were significantly associated with survival. We provide the first evidence that EEIs can be used as prognostic biomarkers for cancer patient survival. Our findings provide mechanistic insights into AS-affected PPI interfaces. Given the ongoing expansion of available RNA-seq data and the number of 3D structurally-resolved (or confidently predicted) protein complexes, our computational framework will help accelerate the discovery of clinically important cancer-promoting AS events.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae133"},"PeriodicalIF":4.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11426328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355522","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: 0