Briefings in bioinformatics最新文献_第7页

Elucidating the interactions between Kinesin-5/BimC and the microtubule: insights from TIRF microscopy and molecular dynamics simulations.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf144

Wenhan Guo, Yuan Gao, Dan Du, Jason E Sanchez, Yupeng Li, Weihong Qiu, Lin Li

{"title":"Elucidating the interactions between Kinesin-5/BimC and the microtubule: insights from TIRF microscopy and molecular dynamics simulations.","authors":"Wenhan Guo, Yuan Gao, Dan Du, Jason E Sanchez, Yupeng Li, Weihong Qiu, Lin Li","doi":"10.1093/bib/bbaf144","DOIUrl":"10.1093/bib/bbaf144","url":null,"abstract":"Kinesin-5 s are bipolar motor proteins that contribute to cell division by crosslinking and sliding apart antiparallel microtubules inside the mitotic spindle. However, the mechanism underlying the interactions between kinesin-5 and the microtubule remains poorly understood. In this study, we investigated the binding of BimC, a kinesin-5 motor from Aspergillus nidulans, to the microtubule using a combination of total internal reflection fluorescence (TIRF) microscopy and molecular dynamics (MD) simulations. TIRF microscopy experiments revealed that increasing the concentration of KCl in the motility buffer from 0 mM to 150 mM completely abolishes the ability of BimC to bind to the microtubule. Consistent with this experimental finding, MD simulations demonstrated a significant reduction in the strength of electrostatic interactions between BimC and microtubules at 150 mM KCl compared to 0 mM KCl. Furthermore, we identified several salt bridges at the BimC-microtubule interface, with positively charged residues on BimC interacting with negatively charged residues on the tubulin heterodimer. These results provide mechanistic insights into the role of electrostatic interactions in kinesin-5-microtubule binding, advancing our understanding of the molecular underpinnings of kinesin-5 motility.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11962974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoPPIs algorithm: a tool to unravel protein cooperative strategies in pathophysiological conditions.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf146

Andrea Lomagno, Ishak Yusuf, Gabriele Tosadori, Dario Bonanomi, Pietro Luigi Mauri, Dario Di Silvestre

{"title":"CoPPIs algorithm: a tool to unravel protein cooperative strategies in pathophysiological conditions.","authors":"Andrea Lomagno, Ishak Yusuf, Gabriele Tosadori, Dario Bonanomi, Pietro Luigi Mauri, Dario Di Silvestre","doi":"10.1093/bib/bbaf146","DOIUrl":"https://doi.org/10.1093/bib/bbaf146","url":null,"abstract":"We present here the co-expressed protein-protein interactions algorithm. In addition to minimizing correlation-causality imbalance and contextualizing protein-protein interactions to the investigated systems, it combines protein-protein interactions and protein co-expression networks to identify differentially correlated functional modules. To test the algorithm, we processed a set of proteomic profiles from different brain regions of controls and subjects affected by idiopathic Parkinson's disease or carrying a GBA1 mutation. Its robustness was supported by the extraction of functional modules, related to translation and mitochondria, whose involvement in Parkinson's disease pathogenesis is well documented. Furthermore, the selection of hubs and bottlenecks from the weightedprotein-protein interactions networks provided molecular clues consistent with the Parkinson pathophysiology. Of note, like quantification, the algorithm revealed less variations when comparing disease groups than when comparing diseased and controls. However, correlation and quantification results showed low overlap, suggesting the complementarity of these measures. An observation that opens the way to a new investigation strategy that takes into account not only protein expression, but also the level of coordination among proteins that cooperate to perform a given function.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring diverse approaches for predicting interferon-gamma release: utilizing MHC class II and peptide sequences.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf101

Abir Omran, Alexander Amberg, Gerhard F Ecker

{"title":"Exploring diverse approaches for predicting interferon-gamma release: utilizing MHC class II and peptide sequences.","authors":"Abir Omran, Alexander Amberg, Gerhard F Ecker","doi":"10.1093/bib/bbaf101","DOIUrl":"10.1093/bib/bbaf101","url":null,"abstract":"Therapeutic proteins are in high demand due to their significant potential, driving continuous market growth. However, a critical concern for therapeutic proteins is their ability to trigger an immune response, while some treatments rely on this response for their therapeutic effect. Therefore, to assess the efficacy and safety of the drug, it is pivotal to determine its immunogenicity potential. Various experimental methods, such as cytokine release or T-cell proliferation assays, are used for this purpose. However, these assays can be costly, time-consuming, and often limited in their ability to screen large peptide sets across diverse major histocompatibility complex (MHC) alleles. Hence, this study aimed to develop a computational classification model for predicting the release of interferon-gamma based on the peptide sequence and the MHC class II (MHC-II) allele pseudo-sequence, which represents the binding environment of the MHC-II molecule. The dataset used in this study was obtained from the Immune Epitope Database and labeled as active or inactive. Among the approaches explored, the random forest algorithm combined with letter-based encoding resulted in the overall best-performing model. Consequently, this model's generalizability to other T-cell activities was further evaluated using a T-cell proliferation dataset. Furthermore, feature importance analysis and virtual single-point mutations were conducted to gain insights into the model's decision-making and to improve the interpretability of the model.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disrupted microbial cross-feeding and altered L-phenylalanine consumption in people living with HIV.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf111

Hai Duc Nguyen, Woong-Ki Kim

{"title":"Disrupted microbial cross-feeding and altered L-phenylalanine consumption in people living with HIV.","authors":"Hai Duc Nguyen, Woong-Ki Kim","doi":"10.1093/bib/bbaf111","DOIUrl":"10.1093/bib/bbaf111","url":null,"abstract":"This work aims to (1) identify microbial and metabolic alterations and (2) reveal a shift in phenylalanine production-consumption equilibrium in individuals with HIV. We conducted extensive searches in multiple databases [MEDLINE, Web of Science (including Cell Press, Oxford, HighWire, Science Direct, IOS Press, Springer Nature, PNAS, and Wiley), Google Scholar, and Embase] and selected two case-control 16S data sets (GenBank IDs: SRP039076 and EBI ID: ERP003611) for analysis. We assessed alpha and beta diversity, performed univariate tests on genus-level relative abundances, and identified significant microbiome features using random forest. We also utilized the MICOM model to simulate growth and metabolic exchanges within the microbiome, focusing on the Metabolite Exchange Score (MES) to determine key metabolic interactions. We found that L-phenylalanine had a higher MES in HIV-uninfected individuals compared with their infected counterparts. The flux of L-phenylalanine consumption was significantly lower in HIV-infected individuals compared with healthy controls, correlating with a decreased number of consuming species in the chronic HIV stage. Prevotella, Roseburia, and Catenibacterium were demonstrated as the most important microbial species involving an increase in L-phenylalanine production in HIV patients, whereas Bacteroides, Faecalibacterium, and Blautia contributed to a decrease in L-phenylalanine consumption. We also found significant alterations in both microbial diversity and metabolic exchanges in people living with HIV. Our findings shed light on why HIV-1 patients have elevated levels of phenylalanine. The impact on essential amino acids like L-phenylalanine underscores the effect of HIV on gut microbiome dynamics. Targeting the restoration of these interactions presents a potential therapeutic avenue for managing HIV-related dysbiosis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11899578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143613397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GcDUO: an open-source software for GC × GC-MS data analysis.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf080

Maria Llambrich, Frans M van der Kloet, Lluc Sementé, Anaïs Rodrigues, Saer Samanipour, Pierre-Hugues Stefanuto, Johan A Westerhuis, Raquel Cumeras, Jesús Brezmes

{"title":"GcDUO: an open-source software for GC × GC-MS data analysis.","authors":"Maria Llambrich, Frans M van der Kloet, Lluc Sementé, Anaïs Rodrigues, Saer Samanipour, Pierre-Hugues Stefanuto, Johan A Westerhuis, Raquel Cumeras, Jesús Brezmes","doi":"10.1093/bib/bbaf080","DOIUrl":"10.1093/bib/bbaf080","url":null,"abstract":"Comprehensive 2D gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful analytical technique. However, the complexity and volume of data generated pose significant challenges for data processing and interpretation, limiting a broader adoption. Chemometric approaches, particularly multiway models like Parallel Factor Analysis (PARAFAC), have proven effective in addressing these challenges by enabling the extraction of meaningful chemical information from multi-dimensional datasets. However, traditional PARAFAC is constrained by its assumption of data tri-linearity, which may not be valid in all cases, leading to potential inaccuracies. To overcome these limitations, we present GcDUO, an open-source software implemented in R, designed specifically for the processing and analysis of GC × GC-MS data. GcDUO integrates advanced chemometric methods, including both PARAFAC and PARAFAC2, for a more accurate and comprehensive analysis. PARAFAC is particularly useful for deconvoluting overlapping peaks and extracting pure chemical signals, while PARAFAC2 relaxes de tri-linearity constraint, allowing the alignment between samples. The software is structured into six modules-data import, region of interest (ROI) selection, deconvolution, peak annotation, data integration, and visualization-facilitating comprehensive and flexible data processing. GcDUO was validated against the gold-standard software for comprehensive GC, demonstrating a high correlation (R2 = 0.9) in peak area measurements, confirming its effectiveness and reliability. GcDUO provides a valuable, open-source platform for researchers in metabolomics and related fields, enabling more accessible and customizable GC × GC-MS data analysis.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf089

Liang Chen, Madison Dautle, Ruoying Gao, Shaoqiang Zhang, Yong Chen

{"title":"Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.","authors":"Liang Chen, Madison Dautle, Ruoying Gao, Shaoqiang Zhang, Yong Chen","doi":"10.1093/bib/bbaf089","DOIUrl":"10.1093/bib/bbaf089","url":null,"abstract":"The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, and negative binomial (NB)-based loss functions, to infer GRNs. GRANGER was evaluated using multiple popular benchmarking datasets, where it demonstrated superior performance compared to eight well-known GRN inference methods. The integration of a NB-based loss function and sparsity-inducing penalties in GRANGER significantly enhanced its capacity to address dropout noise and sparsity in scRNA-seq data. Additionally, GRANGER exhibited robustness against high levels of dropout noise. We applied GRANGER to scRNA-seq data from the whole mouse brain obtained through the BRAIN Initiative project and identified GRNs for five transcription regulators: E2f7, Gbx1, Sox10, Prox1, and Onecut2, which play crucial roles in diverse brain cell types. The inferred GRNs not only recalled many known regulatory relationships but also revealed sets of novel regulatory interactions with functional potential. These findings demonstrate that GRANGER is a highly effective tool for real-world applications in discovering novel gene regulatory relationships.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11891664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-stream cross-modal fusion alignment network for survival analysis.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf103

Jinmiao Song, Yatong Hao, Shuang Zhao, Peng Zhang, Qilin Feng, Qiguo Dai, Xiaodong Duan

{"title":"Dual-stream cross-modal fusion alignment network for survival analysis.","authors":"Jinmiao Song, Yatong Hao, Shuang Zhao, Peng Zhang, Qilin Feng, Qiguo Dai, Xiaodong Duan","doi":"10.1093/bib/bbaf103","DOIUrl":"10.1093/bib/bbaf103","url":null,"abstract":"Survival prediction serves as a pivotal component in precision oncology, enabling the optimization of treatment strategies through mortality risk assessment. While the integration of histopathological images and genomic profiles offers enhanced potential for patient stratification, existing methodologies are constrained by two fundamental limitations: (i) insufficient attention to fine-grained local features in favor of global representations, and (ii) suboptimal cross-modal fusion strategies that either neglect intrinsic correlations or discard modality-specific information. To address these challenges, we propose DSCASurv, a novel cross-modal fusion alignment framework designed to explore and integrate intrinsic correlations across multimodal data, thereby improving the accuracy of survival prediction. Specifically, DSCASurv leverages the local feature extraction capabilities of convolutional layers and the long-range dependency modeling of scanning state space models to extract intra-modal representations, while generating cross-modal representations through dual parallel mixer architectures. A cross-modal attention module functions as a bridge for inter-modal information exchange and complementary information transfer. The framework ultimately integrates all intra-modal representations to generate survival predictions by enhancing and recalibrating complementary information. Extensive experiments on five benchmark cancer datasets demonstrate the superior performance of our approach compared to existing methods.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143673311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MolEM: a unified generative framework for molecular graphs and sequential orders.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf094

Hanwen Zhang, Deng Xiong, Xianggen Liu, Jiancheng Lv

{"title":"MolEM: a unified generative framework for molecular graphs and sequential orders.","authors":"Hanwen Zhang, Deng Xiong, Xianggen Liu, Jiancheng Lv","doi":"10.1093/bib/bbaf094","DOIUrl":"10.1093/bib/bbaf094","url":null,"abstract":"Structure-based drug design aims to generate molecules that fill the cavity of the protein pocket with a high binding affinity. Many contemporary studies employ sequential generative models. Their standard training method is to sequentialize molecular graphs into ordered sequences and then maximize the likelihood of the resulting sequences. However, the exact likelihood is computationally intractable, which involves a sum over all possible sequential orders. Molecular graphs lack an inherent order and the number of orders is factorial in the graph size. To avoid the intractable full space of factorially-many orders, existing works pre-define a fixed node ordering scheme such as depth-first search to sequentialize the 3D molecular graphs. In these cases, the training objectives are loose lower bounds of the exact likelihoods which are suboptimal for generation. To address the challenges, we propose a unified generative framework named MolEM to learn the 3D molecular graphs and corresponding sequential orders jointly. We derive a tight lower bound of the likelihood and maximize it via variational expectation-maximization algorithm, opening a new line of research in learning-based ordering schemes for 3D molecular graph generation. Besides, we first incorporate the molecular docking method QuickVina 2 to manipulate the binding poses, leading to accurate and flexible ligand conformations. Experimental results demonstrate that MolEM significantly outperforms baseline models in generating molecules with high binding affinities and realistic structures. Our approach efficiently approximates the true marginal graph likelihood and identifies reasonable orderings for 3D molecular graphs, aligning well with relevant chemical priors.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kolmogorov-Arnold networks for genomic tasks.

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-03-04 DOI: 10.1093/bib/bbaf129

Oleksandr Cherednichenko, Maria Poptsova

{"title":"Kolmogorov-Arnold networks for genomic tasks.","authors":"Oleksandr Cherednichenko, Maria Poptsova","doi":"10.1093/bib/bbaf129","DOIUrl":"10.1093/bib/bbaf129","url":null,"abstract":"Kolmogorov-Arnold networks (KANs) emerged as a promising alternative for multilayer perceptrons (MLPs) in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures in the domains of computer vision and natural language processing. Integrating KANs into deep learning models for genomic tasks has not been explored. Here, we tested linear KANs (LKANs) and convolutional KANs (CKANs) as a replacement for MLP in baseline deep learning architectures for classification and generation of genomic sequences. We used three genomic benchmark datasets: Genomic Benchmarks, Genome Understanding Evaluation, and Flipon Benchmark. We demonstrated that LKANs outperformed both baseline and CKANs on almost all datasets. CKANs can achieve comparable results but struggle with scaling over large number of parameters. Ablation analysis demonstrated that the number of KAN layers correlates with the model performance. Overall, linear KANs show promising results in improving the performance of deep learning models with relatively small number of parameters. Unleashing KAN potential in different state-of-the-art deep learning architectures currently used in genomics requires further research.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of the functionality and usability of open-source rare variant analysis pipelines. 评估开源罕见变体分析管道的功能和可用性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-02-05 DOI: 10.1093/bib/bbaf044

Cristian Riccio, Max L Jansen, Felix Thalén, Georgios Koliopanos, Vivian Link, Andreas Ziegler

{"title":"Assessment of the functionality and usability of open-source rare variant analysis pipelines.","authors":"Cristian Riccio, Max L Jansen, Felix Thalén, Georgios Koliopanos, Vivian Link, Andreas Ziegler","doi":"10.1093/bib/bbaf044","DOIUrl":"10.1093/bib/bbaf044","url":null,"abstract":"Sequencing of increasingly larger cohorts has revealed many rare variants, presenting an opportunity to further unravel the genetic basis of complex traits. Compared with common variants, rare variants are more complex to analyze. Specialized computational tools for these analyses should be both flexible and user-friendly. However, an overview of the available rare variant analysis pipelines and their functionalities is currently lacking. Here, we provide a systematic review of the currently available rare variant analysis pipelines. We searched MEDLINE and Google Scholar until 27 November 2023, and included open-source rare variant pipelines that accepted genotype data from cohort and case-control studies and group variants into testing units. Eligible pipelines were assessed based on functionality and usability criteria. We identified 17 rare variant pipelines that collectively support various trait types, association tests, testing units, and variant weighting schemes. Currently, no single pipeline can handle all data types in a scalable and flexible manner. We recommend different tools to meet diverse analysis needs. STAARpipeline is suitable for newcomers and common applications owing to its built-in definitions for the testing units. REGENIE is highly scalable, actively maintained, regularly updated, and well documented. Ravages is suitable for analyzing multinomial variables, and OrdinalGWAS is tailored for analyzing ordinal variables. Opportunities remain for developing a user-friendly pipeline that provides high degrees of flexibility and scalability. Such a pipeline would enable researchers to exploit the potential of rare variant analyses to uncover the genetic basis of complex traits.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11795309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143188335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0