PLoS Computational Biology最新文献

bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling. Python包定量表型从化学暴露与基准剂量建模。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 DOI: 10.1371/journal.pcbi.1013337

David J Degnan, Lisa M Bramer, Lisa Truong, Robyn L Tanguay, Sara M Gosline, Katrina M Waters

{"title":"bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling.","authors":"David J Degnan, Lisa M Bramer, Lisa Truong, Robyn L Tanguay, Sara M Gosline, Katrina M Waters","doi":"10.1371/journal.pcbi.1013337","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013337","url":null,"abstract":"Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013337"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732913","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

Algorithms to reconstruct past indels: The deletion-only parsimony problem. 重建过去索引的算法：只删除的简约问题。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 DOI: 10.1371/journal.pcbi.1012585

Jordan Moutet, Eric Rivals, Fabio Pardi

{"title":"Algorithms to reconstruct past indels: The deletion-only parsimony problem.","authors":"Jordan Moutet, Eric Rivals, Fabio Pardi","doi":"10.1371/journal.pcbi.1012585","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012585","url":null,"abstract":"Ancestral sequence reconstruction is an important task in bioinformatics, with applications ranging from protein engineering to the study of genome evolution. When sequences can only undergo substitutions, optimal reconstructions can be efficiently computed using well-known algorithms. However, accounting for indels in ancestral reconstructions is much harder. First, for biologically-relevant problem formulations, no polynomial-time exact algorithms are available. Second, multiple reconstructions are often equally parsimonious or likely, making it crucial to correctly display uncertainty in the results. Here, we consider a parsimony approach where only deletions are allowed, while addressing the aforementioned limitations. First, we describe an exact algorithm to obtain all the optimal solutions. The algorithm runs in polynomial time if only one solution is sought. Second, we show that all possible optimal reconstructions for a fixed node can be represented using a graph computable in polynomial time. While previous studies have proposed graph-based representations of ancestral reconstructions, this result is the first to offer a solid mathematical justification for this approach. Finally we provide arguments for the relevance of the deletion-only case for the general case.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1012585"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732912","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

Recurrent neural networks as neuro-computational models of human speech recognition. 递归神经网络作为人类语音识别的神经计算模型。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 DOI: 10.1371/journal.pcbi.1013244

Christian Brodbeck, Thomas Hannagan, James S Magnuson

{"title":"Recurrent neural networks as neuro-computational models of human speech recognition.","authors":"Christian Brodbeck, Thomas Hannagan, James S Magnuson","doi":"10.1371/journal.pcbi.1013244","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013244","url":null,"abstract":"Human speech recognition transforms a continuous acoustic signal into categorical linguistic units, by aggregating information that is distributed in time. It has been suggested that this kind of information processing may be understood through the computations of a Recurrent Neural Network (RNN) that receives input frame by frame, linearly in time, but builds an incremental representation of this input through a continually evolving internal state. While RNNs can simulate several key behavioral observations about human speech and language processing, it is unknown whether RNNs also develop computational dynamics that resemble human neural speech processing. Here we show that the internal dynamics of long short-term memory (LSTM) RNNs, trained to recognize speech from auditory spectrograms, predict human neural population responses to the same stimuli, beyond predictions from auditory features. Variations in the RNN architecture motivated by cognitive principles further improved this predictive power. Specifically, modifications that allow more human-like phonetic competition also led to more human-like temporal dynamics. Overall, our results suggest that RNNs provide plausible computational models of the cortical processes supporting human speech recognition.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013244"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732931","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

Tracking causal pathways in TMS-evoked brain responses. 追踪经颅磁刺激诱发的脑反应的因果通路。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-28 DOI: 10.1371/journal.pcbi.1013316

Jinming Xiao, Qing Yin, Lei Li, Yao Meng, Xiaobo Liu, Wanrou Hu, Xinyue Huang, Yu Feng, Xiaolong Shan, Weixing Zhao, Peng Wang, Xiaotian Wang, Youyi Li, Huafu Chen, Xujun Duan

{"title":"Tracking causal pathways in TMS-evoked brain responses.","authors":"Jinming Xiao, Qing Yin, Lei Li, Yao Meng, Xiaobo Liu, Wanrou Hu, Xinyue Huang, Yu Feng, Xiaolong Shan, Weixing Zhao, Peng Wang, Xiaotian Wang, Youyi Li, Huafu Chen, Xujun Duan","doi":"10.1371/journal.pcbi.1013316","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013316","url":null,"abstract":"Exploring how local perturbations of cortical activity propagate across the brain network not only helps us understanding causal mechanisms of brain networks, but also offers a network insight into neurobiological mechanisms for transcranial magnetic stimulation (TMS) treatment response. The concurrent combination of TMS and electroencephalography (EEG) enables researchers to track the TMS-evoked activity, defined here as scalp-recorded electrical signals reflecting the brain's response to TMS, with millisecond-level temporal resolution. Based on this technique, we proposed a quantitative framework which combined sparse non-negative matrix factorization and stage-dependent effective connectivity methods to infer the causal pathways in TMS-evoked brain responses. We found that single-pulse TMS firstly induces local activity in the directly stimulated regions (left primary motor cortex, M1), and then propagates to the contralateral hemisphere and other brain regions. Finally, it propagates back from the contralateral region (right M1) to the stimulation region (left M1). This study provides preliminary evidence demonstrating how local perturbations propagate through brain networks to influence various cortical regions, and offers insights into the neural mechanism of TMS-evoked brain responses from a network perspective.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013316"},"PeriodicalIF":3.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732932","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

A variational deep-learning approach to modeling memory T cell dynamics. 记忆T细胞动力学建模的变分深度学习方法。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-24 DOI: 10.1371/journal.pcbi.1013242

Christiaan H van Dorp, Joshua I Gray, Daniel H Paik, Donna L Farber, Andrew J Yates

{"title":"A variational deep-learning approach to modeling memory T cell dynamics.","authors":"Christiaan H van Dorp, Joshua I Gray, Daniel H Paik, Donna L Farber, Andrew J Yates","doi":"10.1371/journal.pcbi.1013242","DOIUrl":"10.1371/journal.pcbi.1013242","url":null,"abstract":"Mechanistic models of dynamic, interacting cell populations have yielded many insights into the growth and resolution of immune responses. Historically these models have described the behavior of pre-defined cell types based on small numbers of phenotypic markers. The ubiquity of deep phenotyping therefore presents a new challenge; how do we confront tractable and interpretable mathematical models with high-dimensional data? To tackle this problem, we studied the development and persistence of lung-resident memory CD4 and CD8 T cells ([Formula: see text]) in mice infected with influenza virus. We developed an approach in which dynamical model parameters and the population structure are inferred simultaneously. This method uses deep learning and stochastic variational inference and is trained on the single-cell flow-cytometry data directly, rather than on the kinetics of pre-identified clusters. We show that during the resolution phase of the immune response, memory CD4 and CD8 T cells within the lung are phenotypically diverse, with subsets exhibiting highly distinct and time-dependent dynamics. [Formula: see text] heterogeneity is maintained long-term by ongoing differentiation of relatively persistent Bcl-2hi CD4 and CD8 [Formula: see text] subsets which resolve into distinct functional populations. Our approach yields new insights into the dynamics of tissue-localized immune memory, and is a novel basis for interpreting time series of high-dimensional data, broadly applicable to diverse biological systems.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013242"},"PeriodicalIF":3.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144708547","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

Competition and cooperation: The plasticity of bacterial interactions across environments. 竞争与合作：细菌跨环境相互作用的可塑性。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-07-24 eCollection Date: 2025-07-01 DOI: 10.1371/journal.pcbi.1013213

Josephine Solowiej-Wedderburn, Jennifer T Pentz, Ludvig Lizana, Bjoern O Schroeder, Peter A Lind, Eric Libby

{"title":"Competition and cooperation: The plasticity of bacterial interactions across environments.","authors":"Josephine Solowiej-Wedderburn, Jennifer T Pentz, Ludvig Lizana, Bjoern O Schroeder, Peter A Lind, Eric Libby","doi":"10.1371/journal.pcbi.1013213","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013213","url":null,"abstract":"Bacteria live in diverse communities, forming complex networks of interacting species. A central question in bacterial ecology is whether species engage in cooperative or competitive interactions. But this question often neglects the role of the environment. Here, we use genome-scale metabolic networks from two different open-access collections (AGORA and CarveMe) to assess pairwise interactions of different microbes in varying environmental conditions (provision of different environmental compounds). By computationally simulating thousands of environments for 10,000 pairs of bacteria from each collection, we found that most pairs were able to both compete and cooperate depending on the availability of environmental resources. This modeling approach allowed us to determine commonalities between environments that could facilitate the potential for cooperation or competition between a pair of species. Namely, cooperative interactions, especially obligate, were most common in less diverse environments. Further, as compounds were removed from the environment, we found interactions tended to degrade towards obligacy. However, we also found that on average at least one compound could be removed from an environment to switch the interaction from competition to facultative cooperation or vice versa. Together our approach indicates a high degree of plasticity in microbial interactions in response to the availability of environmental resources.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013213"},"PeriodicalIF":3.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144708548","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

Towards a comprehensive view of the pocketome universe-biological implications and algorithmic challenges. 对口袋世界的全面看法——生物学的影响和算法的挑战。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-07-24 DOI: 10.1371/journal.pcbi.1013298

Hanne Zillmer, Dirk Walther

{"title":"Towards a comprehensive view of the pocketome universe-biological implications and algorithmic challenges.","authors":"Hanne Zillmer, Dirk Walther","doi":"10.1371/journal.pcbi.1013298","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013298","url":null,"abstract":"With the availability of reliably predicted 3D-structures for essentially all known proteins, characterizing the entirety of compound-binding sites (binding pockets on proteins) has become a possibility. The aim of this study was to identify and analyze all compound-binding sites, i.e., the pocketomes, of eleven species from different kingdoms of life to discern evolutionary trends as well as to arrive at a global cross-species view of the pocketome universe. Computational binding site prediction was performed on all protein structures in each species as available from the AlphaFold database. The resulting set of potential binding sites was inspected for overlaps with known pockets and annotated with regard to the protein domains in which they are located. 2D-projection plots of all pockets embedded in a 128-dimensional feature space, and characterizing them with regard to selected physicochemical properties, provide informative, global pocketome maps that unveil differentiating features between pockets. Our study revealed a sub-linear scaling law of the number of unique binding sites relative to the number of unique protein structures per species. Thus, as proteome size increased during evolution and therefore potentially diversified, the number of distinct binding sites, reflecting potentially diversifying functions, grew less than proportionally. We discuss the biological significance of this finding as well as identify critical and unmet algorithmic challenges.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013298"},"PeriodicalIF":3.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144708549","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

Using homologous network to identify reassortment risk in H5Nx avian influenza viruses. 利用同源网络识别H5Nx禽流感病毒重配风险。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-07-22 eCollection Date: 2025-07-01 DOI: 10.1371/journal.pcbi.1013301

Ruihao Gong, Zijian Feng, Yanyun Zhang

{"title":"Using homologous network to identify reassortment risk in H5Nx avian influenza viruses.","authors":"Ruihao Gong, Zijian Feng, Yanyun Zhang","doi":"10.1371/journal.pcbi.1013301","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013301","url":null,"abstract":"The resurgence of H5Nx reassortment has caused multiple epidemics resulting in severe disease even death in wild birds and poultry. Assessing H5Nx reassortment risk is crucial for designing targeted interventions and enhancing preparedness efforts to manage H5Nx outbreaks effectively. However, the complexity in H5Nx reassortment, driven by the diversity of influenza A viruses (IAVs) and wide range of hosts, has hindered the effective quantification of reassortment risk. In this study, we utilized a network approach to explore the reassortment history using a large-scale dataset. By inferring genomic homogeneity among IAVs, we constructed an IAVs homologous network with reassortment history embedded within it. We estimated the communities within the IAVs homologous network to represent the reassortment risk of various viruses, revealing diverse reassortment risks across different H5Nx viruses. Our analysis also identified the primary hosts contributing to reassortment: domestic poultry in China, and wild birds in North America and Europe. These primary hosts are critical targets for future H5Nx reassortment interventions. Our study provides a framework for quantifying and ranking H5Nx reassortment risk, contributing to enhanced preparedness and prevention efforts.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013301"},"PeriodicalIF":3.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144691323","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

A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations. 一个整合代谢、生长和细胞周期的模块化模型预测，发酵需要调节酵母种群的细胞大小。

IF 3.8 2区生物学

PLoS Computational Biology Pub Date : 2025-07-21 DOI: 10.1371/journal.pcbi.1013296

Marco Vanoni, Pasquale Palumbo, Federico Papa, Stefano Busti, Laura Gotti, Meike Wortel, Bas Teusink, Ivan Orlandi, Alex Pessina, Cristina Airoldi, Luca Brambilla, Marina Vai, Lilia Alberghina

{"title":"A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations.","authors":"Marco Vanoni, Pasquale Palumbo, Federico Papa, Stefano Busti, Laura Gotti, Meike Wortel, Bas Teusink, Ivan Orlandi, Alex Pessina, Cristina Airoldi, Luca Brambilla, Marina Vai, Lilia Alberghina","doi":"10.1371/journal.pcbi.1013296","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1013296","url":null,"abstract":"For unicellular organisms, the reproduction rate and growth are crucial fitness determinants and functional manifestations of the organism genotype. Using the budding yeast Saccharomyces cerevisiae as a model organism, we integrated metabolism, which provides energy and building blocks for growth, with cell mass growth and cell cycle progression into a low-granularity, multiscale (from cell to population) computational model. This model predicted that cells with constitutive respiration do not modulate cell size according to the growth conditions. We experimentally validated the model predictions using mutants with defects in the upper part of glycolysis or glucose transport. Plugging in molecular details of cellular subsystems allowed us to refine predictions from the cellular to the molecular level. Our hybrid multiscale modeling approach provides a framework for structuring molecular knowledge and predicting cell phenotypes under various genetic and environmental conditions.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013296"},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144682983","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

Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits. 张量分解跨多个组织的多维剪接事件，以确定剪接介导的与复杂性状相关的风险基因。

IF 3.6 2区生物学

PLoS Computational Biology Pub Date : 2025-07-21 eCollection Date: 2025-07-01 DOI: 10.1371/journal.pcbi.1013303

Yan Yan, Rui Chen, Hakmook Kang, Yuting Tan, Anshul Tiwari, Siyuan Ma, Zhexing Wen, Xue Zhong, Bingshan Li

{"title":"Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.","authors":"Yan Yan, Rui Chen, Hakmook Kang, Yuting Tan, Anshul Tiwari, Siyuan Ma, Zhexing Wen, Xue Zhong, Bingshan Li","doi":"10.1371/journal.pcbi.1013303","DOIUrl":"10.1371/journal.pcbi.1013303","url":null,"abstract":"Identifying risk genes associated with complex traits remains challenging. Integrating gene expression data with Genome-Wide Association Study (GWAS) through Transcriptome-Wide Association Study (TWAS) methods has discovered candidate risk genes for various complex traits. Splicing, which explains a comparable heritability of complex traits as gene expression, is under-explored due to its multidimensionality. To leverage multiple splicing events in a gene and shared splicing across tissues, we develop Multi-tissue Splicing Gene (MTSG), which employs tensor decomposition and sparse Canonical Correlation Analysis (sCCA) to extract meaningful information from high-dimensional multiple splicing events across multiple tissues. We build MTSG models using GTEx data and apply them to GWAS summary statistics of Alzheimer's disease (AD) (111,326 cases and 677,663 controls) and schizophrenia (SCZ) (36,989 cases and 113,075 controls). We identify 174 and 497 significant splicing-mediated risk genes for AD and SCZ, respectively, at Bonferroni correction. For AD, our results demonstrate significant enrichment of AD related pathways and identify additional AD risk genes not detected in the single-tissue analysis, while preserving most top genes identified in the brain frontal cortex. Consistently, for SCZ, genes identified by our brain-wide MTSG model, built from a cluster of 13 brain tissues, exhibit stronger enrichment in SCZ-relevant genes and MTSG identifies unique SCZ risk genes compared to single-tissue models. These results showcase that our MTSG models capture distinctive splicing events across tissues, which might be overlooked when using single tissue alone. Our MTSG models can be applied to other complex traits to help identify splicing-mediated disease risk genes.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 7","pages":"e1013303"},"PeriodicalIF":3.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144682955","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