Briefings in bioinformatics最新文献

{"title":"LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.","authors":"Cui-Xiang Lin, Hong-Dong Li, Jianxin Wang","doi":"10.1093/bib/bbae611","DOIUrl":"10.1093/bib/bbae611","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a complex disease with its genetic etiology not fully understood. Gene network-based methods have been proven promising in predicting AD genes. However, existing approaches are limited in their ability to model the nonlinear relationship between networks and disease genes, because (i) any data can be theoretically decomposed into the sum of a linear part and a nonlinear part, (ii) the linear part can be best modeled by a linear model since a nonlinear model is biased and can be easily overfit, and (iii) existing methods do not separate the linear part from the nonlinear part when building the disease gene prediction model. To address the limitation, we propose linear model-integrated graph convolutional network (LIMO-GCN), a generic disease gene prediction method that models the data linearity and nonlinearity by integrating a linear model with GCN. The reason to use GCN is that it is by design naturally suitable to dealing with network data, and the reason to integrate a linear model is that the linearity in the data can be best modeled by a linear model. The weighted sum of the prediction of the two components is used as the final prediction of LIMO-GCN. Then, we apply LIMO-GCN to the prediction of AD genes. LIMO-GCN outperforms the state-of-the-art approaches including GCN, network-wide association studies, and random walk. Furthermore, we show that the top-ranked genes are significantly associated with AD based on molecular evidence from heterogeneous genomic data. Our results indicate that LIMO-GCN provides a novel method for prioritizing AD genes.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11596108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726293","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}

{"title":"ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.","authors":"Jiachen Chen, Joanne M Murabito, Kathryn L Lunetta","doi":"10.1093/bib/bbae610","DOIUrl":"10.1093/bib/bbae610","url":null,"abstract":"<p><p>The Gaussian graphical model (GGM) is a statistical network approach that represents conditional dependencies among components, enabling a comprehensive exploration of disease mechanisms using high-throughput multi-omics data. Analyzing differential and similar structures in biological networks across multiple clinical conditions can reveal significant biological pathways and interactions associated with disease onset and progression. However, most existing methods for estimating group differences in sparse GGMs only apply to comparisons between two groups, and the challenging problem of multiple testing across multiple GGMs persists. This limitation hinders the ability to uncover complex biological insights that arise from comparing multiple conditions simultaneously. To address these challenges, we propose the Omics Networks Differential and Similarity Analysis (ONDSA) framework, specifically designed for continuous omics data. ONDSA tests for structural differences and similarities across multiple groups, effectively controlling the false discovery rate (FDR) at a desired level. Our approach focuses on entry-wise comparisons of precision matrices across groups, introducing two test statistics to sequentially estimate structural differences and similarities while adjusting for correlated effects in FDR control procedures. We show via comprehensive simulations that ONDSA outperforms existing methods under a range of graph structures and is a valuable tool for joint comparisons of multiple GGMs. We also illustrate our method through the detection of neuroinflammatory pathways in a multi-omics dataset from the Framingham Heart Study Offspring cohort, involving three apolipoprotein E genotype groups. It highlights ONDSA's ability to provide a more holistic view of biological interactions and disease mechanisms through multi-omics data integration.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11586129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142709265","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}

{"title":"CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis.","authors":"Tianyu Liu, Wenxin Long, Zhiyuan Cao, Yuge Wang, Chuan Hua He, Le Zhang, Stephen M Strittmatter, Hongyu Zhao","doi":"10.1093/bib/bbae626","DOIUrl":"10.1093/bib/bbae626","url":null,"abstract":"<p><strong>Motivation: </strong>Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types.</p><p><strong>Results: </strong>Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11596696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726287","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}

{"title":"Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.","authors":"Peng Liu, Yuchen Pan, Hung-Ching Chang, Wenjia Wang, Yusi Fang, Xiangning Xue, Jian Zou, Jessica M Toothaker, Oluwabunmi Olaloye, Eduardo Gonzalez Santiago, Black McCourt, Vanessa Mitsialis, Pietro Presicce, Suhas G Kallapur, Scott B Snapper, Jia-Jun Liu, George C Tseng, Liza Konnikova, Silvia Liu","doi":"10.1093/bib/bbae633","DOIUrl":"10.1093/bib/bbae633","url":null,"abstract":"<p><p>Cytometry is an advanced technique for simultaneously identifying and quantifying many cell surface and intracellular proteins at a single-cell resolution. Analyzing high-dimensional cytometry data involves identifying and quantifying cell populations based on their marker expressions. This study provided a quantitative review and comparison of various ways to phenotype cellular populations within the cytometry data, including manual gating, unsupervised clustering, and supervised auto-gating. Six datasets from diverse species and sample types were included in the study, and manual gating with two hierarchical layers was used as the truth for evaluation. For manual gating, results from five researchers were compared to illustrate the gating consistency among different raters. For unsupervised clustering, 23 tools were quantitatively compared in terms of accuracy with the truth and computing cost. While no method outperformed all others, several tools, including PAC-MAN, CCAST, FlowSOM, flowClust, and DEPECHE, generally demonstrated strong performance. For supervised auto-gating methods, four algorithms were evaluated, where DeepCyTOF and CyTOF Linear Classifier performed the best. We further provided practical recommendations on prioritizing gating methods based on different application scenarios. This study offers comprehensive insights for biologists to understand diverse gating methods and choose the best-suited ones for their applications.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11630031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827382","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}

{"title":"Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.","authors":"Raúl Galindo-Hernández, Katya Rodríguez-Vázquez, Edgardo Galán-Vásquez, Carlos Ignacio Hernández Castellanos","doi":"10.1093/bib/bbae681","DOIUrl":"10.1093/bib/bbae681","url":null,"abstract":"<p><p>Analyzing gene expression data helps the identification of significant biological relationships in genes. With a growing number of open biological datasets available, it is paramount to use reliable and innovative methods to perform in-depth analyses of biological data and ensure that informed decisions are made based on accurate information. Evolutionary algorithms have been successful in the analysis of biological datasets. However, there is still room for improvement, and further analysis should be conducted. In this work, we propose Online-Adjusted EVOlutionary Biclustering algorithm (OAEVOB), a novel evolutionary-based biclustering algorithm that efficiently handles vast gene expression data. OAEVOB incorporates an online-adjustment feature that efficiently identifies significant groups by updating the mutation probability and crossover parameters. We utilize measurements such as Pearson correlation, distance correlation, biweight midcorrelation, and mutual information to assess the similarity of genes in the biclusters. Algorithms in the specialized literature do not address generalization to diverse gene expression sources. Therefore, to evaluate OAEVOB's performance, we analyzed six gene expression datasets obtained from diverse sequencing data sources, specifically Deoxyribonucleic Acid microarray, Ribonucleic Acid (RNA) sequencing, and single-cell RNA sequencing, which are subject to a thorough examination. OAEVOB identified significant broad gene expression biclusters with correlations greater than $0.5$ across all similarity measurements employed. Additionally, when biclusters are evaluated by functional enrichment analysis, they exhibit biological functions, suggesting that OAEVOB effectively identifies biclusters with specific cancer and tissue-related genes in the analyzed datasets. We compared the OAEVOB's performance with state-of-the-art methods and outperformed them showing robustness to noise, overlapping, sequencing data sources, and gene coverage.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920882","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}

{"title":"Understanding relationships between epigenetic marks and their application to robust assignment of chromatin states.","authors":"Leandro Murgas, Gianluca Pollastri, Erick Riquelme, Mauricio Sáez, Alberto J M Martin","doi":"10.1093/bib/bbae638","DOIUrl":"10.1093/bib/bbae638","url":null,"abstract":"<p><p>Structural changes of chromatin modulate access to DNA for the molecular machinery involved in the control of transcription. These changes are linked to variations in epigenetic marks that allow to classify chromatin in different functional states depending on the pattern of these histone marks. Importantly, alterations in chromatin states are known to be linked with various diseases, and their changes are known to explain processes such as cellular proliferation. For most of the available samples, there are not enough epigenomic data available to accurately determine chromatin states for the cells affected in each of them. This is mainly due to high costs of performing this type of experiments but also because of lack of a sufficient amount of sample or its degradation. In this work, we describe a cascade method based on a random forest algorithm to infer epigenetic marks, and by doing so, to identify relationships between different histone marks. Importantly, our approach also reduces the number of experimentally determined marks required to assign chromatin states. Moreover, in this work we have identified several relationships between patterns of different histone marks, which strengthens the evidence in favor of a redundant epigenetic code.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142806106","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}

{"title":"CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data.","authors":"Yi Zhang, Yin Wang, Xinyuan Liu, Xi Feng","doi":"10.1093/bib/bbae668","DOIUrl":"10.1093/bib/bbae668","url":null,"abstract":"<p><p>A key challenge in analyzing single-cell RNA sequencing data is the large number of false zeros, known as \"dropout zeros\", which are caused by technical limitations such as shallow sequencing depth or inefficient mRNA capture. To address this challenge, we propose a novel imputation model called CPARI, which combines cell partitioning with our designed absolute and relative imputation methods. Initially, CPARI employs a new approach to select highly variable genes and constructs an average consensus matrix using C-mean fuzzy clustering-based blockchain technology to obtain results at different resolutions. Hierarchical clustering is then applied to further refine these blocks, resulting in well-defined cellular partitions. Subsequently, CPARI identifies dropout events and determines the imputation positions of these identified zeros. An autoencoder is trained within each cellular block to learn gene features and reconstruct data. Our uniquely defined absolute imputation technique is first applied to the identified positions, followed by our relative imputation technique to address remaining dropout zeros, ensuring that both global consistency and local variation are maintained. Through comprehensive analyses conducted on simulated and real scRNA-seq datasets, including quantitative assessment, differential expression analysis, cell clustering, cell trajectory inference, robustness evaluation, and large-scale data imputation, CPARI demonstrates superior performance compared to 12 other art-of-state imputation models. Additionally, ablation experiments further confirm the significance and necessity of both the cell partitioning and relative imputation components of CPARI. Notably, CPARI as a new denoising approach could distinguish between real biological zeros and dropout zeros and minimize false positives, and maximize the accuracy of imputation.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880666","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}

{"title":"SatXplor-a comprehensive pipeline for satellite DNA analyses in complex genome assemblies.","authors":"Marin Volarić, Nevenka Meštrović, Evelin Despot-Slade","doi":"10.1093/bib/bbae660","DOIUrl":"10.1093/bib/bbae660","url":null,"abstract":"<p><p>Satellite DNAs (satDNAs) are tandemly repeated sequences that make up a significant portion of almost all eukaryotic genomes. Although satDNAs have been shown to play an important role in genome organization and evolution, they are relatively poorly analyzed, even in model organisms. One of the main reasons for the current lack of in-depth studies on satDNAs is their underrepresentation in genome assemblies. Due to complexity, abundance, and highly repetitive nature of satDNAs, their analysis is challenging, requiring efficient tools that ensure accurate annotation and comprehensive genome-wide analysis. We present a novel pipeline, named satellite DNA Exploration (SatXplor), designed to robustly characterize satDNA elements and analyze their arrays and flanking regions. SatXplor is benchmarked against other tools and curated satDNA datasets from diverse species, including mice and humans, showcase its versatility across genomes with varying complexities and satDNA profiles. Component algorithms excel in the identification of tandemly repeated sequences and, for the first time, enable evaluation of satDNA variation and array annotation with the addition of information about surrounding genomic landscape. SatXplor is an innovative pipeline for satDNA analysis that can be paired with any tool used for satDNA detection, offering insights into the structural characteristics, array determination, and genomic context of satDNA elements. By integrating various computational techniques, from sequence analysis and homology investigation to advanced clustering and graph-based methods, it provides a versatile and comprehensive approach to explore the complexity of satDNA organization and understand the underlying mechanisms and evolutionary aspects. It is open-source and freely accessible at https://github.com/mvolar/SatXplor.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11663013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871374","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}

{"title":"BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.","authors":"Yuejun Tan, Linhai Xie, Hong Yang, Qingyuan Zhang, Jinyuan Luo, Yanchun Zhang","doi":"10.1093/bib/bbae617","DOIUrl":"10.1093/bib/bbae617","url":null,"abstract":"<p><p>Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full combination space of genetic perturbations and cell types. Consequently, a bunch of computational models have been proposed to predict unseen combinations based on existing data. Among them, generative models, e.g. variational autoencoder and diffusion models, have the superiority in capturing the perturbed data distribution, but lack a biologically understandable foundation for generalization. On the other side of the spectrum, Gene Regulation Networks or gene pathway knowledge have been exploited for more reasonable generalization enhancement. Unfortunately, they do not reach a balanced processing of the two data modalities, leading to a degraded fitting ability. Hence, we propose a dual-stream architecture. Before the information from two modalities are merged, the sequencing data are learned with a generative model while three types of knowledge data are comprehensively processed with graph networks and a masked transformer, enforcing a deep understanding of single-modality data, respectively. The benchmark results show an approximate 20% reduction in terms of mean squared error, proving the effectiveness of the model.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11586784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142709171","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}

{"title":"Predictive modelling of acute Promyelocytic leukaemia resistance to retinoic acid therapy.","authors":"José A Sánchez-Villanueva, Lia N'Guyen, Mathilde Poplineau, Estelle Duprez, Élisabeth Remy, Denis Thieffry","doi":"10.1093/bib/bbaf002","DOIUrl":"10.1093/bib/bbaf002","url":null,"abstract":"<p><p>Acute Promyelocytic Leukaemia (APL) arises from an aberrant chromosomal translocation involving the Retinoic Acid Receptor Alpha (RARA) gene, predominantly with the Promyelocytic Leukaemia (PML) or Promyelocytic Leukaemia Zinc Finger (PLZF) genes. The resulting oncoproteins block the haematopoietic differentiation program promoting aberrant proliferative promyelocytes. Retinoic Acid (RA) therapy is successful in most of the PML::RARA patients, while PLZF::RARA patients frequently become resistant and relapse. Recent studies pointed to various underlying molecular components, but their precise contributions remain to be deciphered. We developed a logical network model integrating signalling, transcriptional, and epigenetic regulatory mechanisms, which captures key features of the APL cell responses to RA depending on the genetic background. The explicit inclusion of the histone methyltransferase EZH2 allowed the assessment of its role in the resistance mechanism, distinguishing between its canonical and non-canonical activities. The model dynamics was thoroughly analysed using tools integrated in the public software suite maintained by the CoLoMoTo consortium (https://colomoto.github.io/). The model serves as a solid basis to assess the roles of novel regulatory mechanisms, as well as to explore novel therapeutical approaches in silico.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142977611","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}