Bioinformatics最新文献_第6页

Correction to: "Retraction of: DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency". 更正:“撤回:deepcrisstl:深度迁移学习预测CRISPR/Cas9功能和内源性靶向编辑效率”。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad562

引用次数: 0

RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints. 基于小角度x射线散射约束的RNA片段组装三维结构建模。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad527

Grzegorz Chojnowski, Rafał Zaborowski, Marcin Magnus, Sunandan Mukherjee, Janusz M Bujnicki

{"title":"RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints.","authors":"Grzegorz Chojnowski, Rafał Zaborowski, Marcin Magnus, Sunandan Mukherjee, Janusz M Bujnicki","doi":"10.1093/bioinformatics/btad527","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad527","url":null,"abstract":"Summary: Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the small-angle X-ray scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts, and small-angle X-ray scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints.Availability and implementation: The program web server is available at http://iimcb.genesilico.pl/rnamasonry. The source code is available at https://gitlab.com/gchojnowski/rnamasonry.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10285624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test. 全基因组多介质分析使用广义伯克-琼斯统计与复合检验。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad544

En-Yu Lai, Yen-Tsung Huang

{"title":"Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test.","authors":"En-Yu Lai, Yen-Tsung Huang","doi":"10.1093/bioinformatics/btad544","DOIUrl":"https://doi.org/10.1093/bioinformatics/btad544","url":null,"abstract":"Motivation: Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that marks the progression from an exposure, through mediators, to an outcome. In the age of high-throughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen. In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment cannot be attained. The power loss hence decreases the true mechanisms identified after multiple testing corrections. To fairly delineate a uniform distribution under the composite null, Huang (Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat 2019a;13:60-84; AoAS) proposed the composite test to provide adjusted P-values for single-mediator analyses.Results: Our contribution is to extend the method to multimediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests. Using the generalized Berk-Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations.Availability and implementation: An R package MACtest is available on https://github.com/roqe/MACtest.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10286120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction to: Online bias-aware disease module mining with ROBUST-Web. 更正:使用ROBUST-Web进行在线偏见感知疾病模块挖掘。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad566

引用次数: 0

GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs. GraphCpG：基于位点感知相邻子图的单细胞甲基组插补。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad533

Yuzhong Deng, Jianxiong Tang, Jiyang Zhang, Jianxiao Zou, Que Zhu, Shicai Fan

{"title":"GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.","authors":"Yuzhong Deng, Jianxiong Tang, Jiyang Zhang, Jianxiao Zou, Que Zhu, Shicai Fan","doi":"10.1093/bioinformatics/btad533","DOIUrl":"10.1093/bioinformatics/btad533","url":null,"abstract":"Motivation: Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses.Results: We proposed a novel graph-based deep learning approach to impute methylation matrices based on locus-aware neighboring subgraphs with locus-aware encoding orienting on one cell type. Merely using the CpGs methylation matrix, the obtained GraphCpG outperforms previous methods on datasets containing more than hundreds of cells and achieves competitive performance on smaller datasets, with subgraphs of predicted sites visualized by retrievable bipartite graphs. Besides better imputation performance with increasing cell number, it significantly reduces computation time and demonstrates improvement in downstream analysis.Availability and implementation: The source code is freely available at https://github.com/yuzhong-deng/graphcpg.git.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10121589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Automated machine learning for genome wide association studies. 用于全基因组关联研究的自动化机器学习。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad545

Kleanthi Lakiotaki, Zaharias Papadovasilakis, Vincenzo Lagani, Stefanos Fafalios, Paulos Charonyktakis, Michail Tsagris, Ioannis Tsamardinos

{"title":"Automated machine learning for genome wide association studies.","authors":"Kleanthi Lakiotaki, Zaharias Papadovasilakis, Vincenzo Lagani, Stefanos Fafalios, Paulos Charonyktakis, Michail Tsagris, Ioannis Tsamardinos","doi":"10.1093/bioinformatics/btad545","DOIUrl":"10.1093/bioinformatics/btad545","url":null,"abstract":"Motivation: Genome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice.Results: We develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures.Availability and implementation: Code for this study is available at: https://github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at: https://jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https://ega-archive.org/). PRS data are found at: https://www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at: https://easygwas.ethz.ch/data/public/dataset/view/1/.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10562960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10161763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Accessibility of covariance information creates vulnerability in Federated Learning frameworks. 协方差信息的可访问性在联合学习框架中造成漏洞。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad531

Manuel Huth, Jonas Arruda, Roy Gusinow, Lorenzo Contento, Evelina Tacconelli, Jan Hasenauer

{"title":"Accessibility of covariance information creates vulnerability in Federated Learning frameworks.","authors":"Manuel Huth, Jonas Arruda, Roy Gusinow, Lorenzo Contento, Evelina Tacconelli, Jan Hasenauer","doi":"10.1093/bioinformatics/btad531","DOIUrl":"10.1093/bioinformatics/btad531","url":null,"abstract":"Motivation: Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side.Results: We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks.Availability and implementation: The code examples are provided at GitHub (https://github.com/manuhuth/Data-Leakage-From-Covariances.git). The CNSIM1 dataset, which we used in the manuscript, is available within the DSData R package (https://github.com/datashield/DSData/tree/main/data).","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10176920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Correction to: Optimal adjustment sets for causal query estimation in partially observed biomolecular networks. 修正:部分观察到的生物分子网络中因果查询估计的最优调整集。

IF 5.8 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad559

引用次数: 0

DrForna: visualization of cotranscriptional folding. DrForna：同转录折叠的可视化。

IF 4.4 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad555

Anda Ramona Tănasie, Peter Kerpedjiev, Stefan Hammer, Stefan Badelt

{"title":"DrForna: visualization of cotranscriptional folding.","authors":"Anda Ramona Tănasie, Peter Kerpedjiev, Stefan Hammer, Stefan Badelt","doi":"10.1093/bioinformatics/btad555","DOIUrl":"10.1093/bioinformatics/btad555","url":null,"abstract":"Motivation: Understanding RNA folding at the level of secondary structures can give important insights concerning the function of a molecule. We are interested to learn how secondary structures change dynamically during transcription, as well as whether particular secondary structures form already during or only after transcription. While different approaches exist to simulate cotranscriptional folding, the current strategies for visualization are lagging behind. New, more suitable approaches are necessary to help with exploring the generated data from cotranscriptional folding simulations.Results: We present DrForna, an interactive visualization app for viewing the time course of a cotranscriptional RNA folding simulation. Specifically, users can scroll along the time axis and see the population of structures that are present at any particular time point.Availability and implementation: DrForna is a JavaScript project available on Github at https://github.com/ViennaRNA/drforna and deployed at https://viennarna.github.io/drforna.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10357864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Phylogenetic inference using generative adversarial networks. 利用生成对抗网络进行系统发育推断。

IF 4.4 3区生物学

Bioinformatics Pub Date : 2023-09-02 DOI: 10.1093/bioinformatics/btad543

Megan L Smith, Matthew W Hahn

{"title":"Phylogenetic inference using generative adversarial networks.","authors":"Megan L Smith, Matthew W Hahn","doi":"10.1093/bioinformatics/btad543","DOIUrl":"10.1093/bioinformatics/btad543","url":null,"abstract":"Motivation: The application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted quartets of taxa, where there are only three possible topologies. Here, we explore the potential of generative adversarial networks (GANs) to address this limitation. GANs consist of a generator and a discriminator: at each step, the generator aims to create data that is similar to real data, while the discriminator attempts to distinguish generated and real data. By using an evolutionary model as the generator, we use GANs to make evolutionary inferences. Since a new model can be considered at each iteration, heuristic searches of complex model spaces are possible. Thus, GANs offer a potential solution to the challenges of applying machine learning in phylogenetics.Results: We developed phyloGAN, a GAN that infers phylogenetic relationships among species. phyloGAN takes as input a concatenated alignment, or a set of gene alignments, and infers a phylogenetic tree either considering or ignoring gene tree heterogeneity. We explored the performance of phyloGAN for up to 15 taxa in the concatenation case and 6 taxa when considering gene tree heterogeneity. Error rates are relatively low in these simple cases. However, run times are slow and performance metrics suggest issues during training. Future work should explore novel architectures that may result in more stable and efficient GANs for phylogenetics.Availability and implementation: phyloGAN is available on github: https://github.com/meganlsmith/phyloGAN/.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10631514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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