Briefings in bioinformatics最新文献

TRIAGE: an R package for regulatory gene analysis. TRIAGE：用于调控基因分析的R包。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-01-12 DOI: 10.1093/bib/bbaf004

Qiongyi Zhao, Woo Jun Shim, Yuliangzi Sun, Enakshi Sinniah, Sophie Shen, Mikael Boden, Nathan J Palpant

{"title":"TRIAGE: an R package for regulatory gene analysis.","authors":"Qiongyi Zhao, Woo Jun Shim, Yuliangzi Sun, Enakshi Sinniah, Sophie Shen, Mikael Boden, Nathan J Palpant","doi":"10.1093/bib/bbaf004","DOIUrl":"10.1093/bib/bbaf004","url":null,"abstract":"Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions. It facilitates the construction of efficient and adaptable pipelines for transcriptomic data analysis and visualization, with a focus on revealing regulatory gene networks. We demonstrate the utility of the TRIAGE R package using three independent transcriptomic datasets, showcasing its integration into standard analysis workflows for examining regulatory mechanisms across diverse biological contexts. The TRIAGE R package is available on GitHub at https://github.com/palpant-comp/TRIAGE_R_Package.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11725390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969270","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

tcrBLOSUM: an amino acid substitution matrix for sensitive alignment of distant epitope-specific TCRs. tcrBLOSUM：用于远距离表位特异性 TCR 敏感比对的氨基酸替代矩阵。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae602

Anna Postovskaya, Koen Vercauteren, Pieter Meysman, Kris Laukens

{"title":"tcrBLOSUM: an amino acid substitution matrix for sensitive alignment of distant epitope-specific TCRs.","authors":"Anna Postovskaya, Koen Vercauteren, Pieter Meysman, Kris Laukens","doi":"10.1093/bib/bbae602","DOIUrl":"10.1093/bib/bbae602","url":null,"abstract":"Deciphering the specificity of T-cell receptor (TCR) repertoires is crucial for monitoring adaptive immune responses and developing targeted immunotherapies and vaccines. To elucidate the specificity of previously unseen TCRs, many methods employ the BLOSUM62 matrix to find TCRs with similar amino acid (AA) sequences. However, while BLOSUM62 reflects the AA substitutions within conserved regions of proteins with similar functions, the remarkable diversity of TCRs means that both TCRs with similar and dissimilar sequences can bind the same epitope. Therefore, reliance on BLOSUM62 may bias detection towards epitope-specific TCRs with similar biochemical properties, overlooking those with more diverse AA compositions. In this study, we introduce tcrBLOSUMa and tcrBLOSUMb, specialized AA substitution matrices for CDR3 alpha and CDR3 beta TCR chains, respectively. The matrices reflect AA frequencies and variations occurring within TCRs that bind the same epitope, revealing that both CDR3 alpha and CDR3 beta display tolerance to a wide range of AA substitutions and differ noticeably from the standard BLOSUM62. By accurately aligning distant TCRs employing tcrBLOSUMb, we were able to improve clustering performance and capture a large number of epitope-specific TCRs with diverse AA compositions and physicochemical profiles overlooked by BLOSUM62. Utilizing both the general BLOSUM62 and specialized tcrBLOSUM matrices in existing computational tools will broaden the range of TCRs that can be associated with their cognate epitopes, thereby enhancing TCR repertoire analysis.","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/PMC11583439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686153","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

Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study. 解密抑郁症与痛经之间的基因相互作用：孟德尔随机研究。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae589

Shuhe Liu, Zhen Wei, Daniel F Carr, John Moraros

{"title":"Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study.","authors":"Shuhe Liu, Zhen Wei, Daniel F Carr, John Moraros","doi":"10.1093/bib/bbae589","DOIUrl":"10.1093/bib/bbae589","url":null,"abstract":"Background: This study aims to explore the link between depression and dysmenorrhea by using an integrated and innovative approach that combines genomic, transcriptomic, and protein interaction data/information from various resources.Methods: A two-sample, bidirectional, and multivariate Mendelian randomization (MR) approach was applied to determine causality between dysmenorrhea and depression. Genome-wide association study (GWAS) data were used to identify genetic variants associated with both dysmenorrhea and depression, followed by colocalization analysis of shared genetic influences. Expression quantitative trait locus (eQTL) data were analyzed from public databases to pinpoint target genes in relevant tissues. Additionally, a protein-protein interaction (PPI) network was constructed using the STRING database to analyze interactions among identified proteins.Results: MR analysis confirmed a significant causal effect of depression on dysmenorrhea ['odds ratio' (95% confidence interval) = 1.51 (1.19, 1.91), P = 7.26 × 10-4]. Conversely, no evidence was found to support a causal effect of dysmenorrhea on depression (P = .74). Genetic analysis, using GWAS and eQTL data, identified single-nucleotide polymorphisms in several genes, including GRK4, TRAIP, and RNF123, indicating that depression may impact reproductive function through these genetic pathways, with a detailed picture presented by way of analysis in the PPI network. Colocalization analysis highlighted rs34341246(RBMS3) as a potential shared causal variant.Conclusions: This study suggests that depression significantly affects dysmenorrhea and identifies key genes and proteins involved in this interaction. The findings underline the need for integrated clinical and public health approaches that screen for depression among women presenting with dysmenorrhea and suggest new targeted preventive strategies.","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/PMC11596086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726289","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

microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites. micro - cnn：一个前卫的深度卷积神经网络揭示了规范位点之外的功能miRNA目标。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae678

Elissavet Zacharopoulou, Maria D Paraskevopoulou, Spyros Tastsoglou, Athanasios Alexiou, Anna Karavangeli, Vasilis Pierros, Stefanos Digenis, Galatea Mavromati, Artemis G Hatzigeorgiou, Dimitra Karagkouni

{"title":"microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.","authors":"Elissavet Zacharopoulou, Maria D Paraskevopoulou, Spyros Tastsoglou, Athanasios Alexiou, Anna Karavangeli, Vasilis Pierros, Stefanos Digenis, Galatea Mavromati, Artemis G Hatzigeorgiou, Dimitra Karagkouni","doi":"10.1093/bib/bbae678","DOIUrl":"10.1093/bib/bbae678","url":null,"abstract":"microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidated miRNA functions. Here, we introduce microT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integrating hundreds of tissue-matched (in-)direct experiments from 26 distinct cell types, corresponding to a unique training and evaluation set of >60 000 miRNA binding events and ~30 000 unique miRNA-gene target pairs. The multilayer sequence-based design enables the prediction of both host and virus-encoded miRNA interactions, providing for the first time up to 67% of direct genuine Epstein-Barr virus- and Kaposi's sarcoma-associated herpesvirus-derived miRNA-target pairs corresponding to one out of four binding events of virus-encoded miRNAs. microT-CNN fills the existing gap of the miRNA-target prediction by providing functional targets beyond the canonical sites, including 3' compensatory miRNA pairings, prompting 1.4-fold more validated miRNA binding events compared to other implementations and shedding light on previously unexplored facets of the miRNA interactome.","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/PMC11685103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906215","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

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata. 使用非结构化元数据的可解释建模对公开可用的样本和研究进行注释。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae652

Hao Yuan, Parker Hicks, Mansooreh Ahmadian, Kayla A Johnson, Lydia Valtadoros, Arjun Krishnan

{"title":"Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata.","authors":"Hao Yuan, Parker Hicks, Mansooreh Ahmadian, Kayla A Johnson, Lydia Valtadoros, Arjun Krishnan","doi":"10.1093/bib/bbae652","DOIUrl":"10.1093/bib/bbae652","url":null,"abstract":"Reusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we propose txt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available. Txt2onto 2.0 uses embeddings from a large language model during prediction to deal with unseen-yet-relevant words related to each disease and tissue term being predicted from the input text, thereby explaining the basis of every annotation. We demonstrate the generalizability of txt2onto 2.0 by accurately predicting disease annotations for studies from independent datasets, using proteomics and clinical trials as examples. Overall, our approach can annotate biomedical text regardless of experimental types or sources. Code, data, and trained models are available at https://github.com/krishnanlab/txt2onto2.0.","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/PMC11663484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876038","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

Filtering out the noise: metagenomic classifiers optimize ancient DNA mapping. 过滤噪音：元基因组分类器优化古 DNA 图谱。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae646

Shyamsundar Ravishankar, Vilma Perez, Roberta Davidson, Xavier Roca-Rada, Divon Lan, Yassine Souilmi, Bastien Llamas

{"title":"Filtering out the noise: metagenomic classifiers optimize ancient DNA mapping.","authors":"Shyamsundar Ravishankar, Vilma Perez, Roberta Davidson, Xavier Roca-Rada, Divon Lan, Yassine Souilmi, Bastien Llamas","doi":"10.1093/bib/bbae646","DOIUrl":"10.1093/bib/bbae646","url":null,"abstract":"Contamination with exogenous DNA presents a significant challenge in ancient DNA (aDNA) studies of single organisms. Failure to address contamination from microbes, reagents, and present-day sources can impact the interpretation of results. Although field and laboratory protocols exist to limit contamination, there is still a need to accurately distinguish between endogenous and exogenous data computationally. Here, we propose a workflow to reduce exogenous contamination based on a metagenomic classifier. Unlike previous methods that relied exclusively on DNA sequencing reads mapping specificity to a single reference genome to remove contaminating reads, our approach uses Kraken2-based filtering before mapping to the reference genome. Using both simulated and empirical shotgun aDNA data, we show that this workflow presents a simple and efficient method that can be used in a wide range of computational environments-including personal machines. We propose strategies to build specific databases used to profile sequencing data that take into consideration available computational resources and prior knowledge about the target taxa and likely contaminants. Our workflow significantly reduces the overall computational resources required during the mapping process and reduces the total runtime by up to ~94%. The most significant impacts are observed in low endogenous samples. Importantly, contaminants that would map to the reference are filtered out using our strategy, reducing false positive alignments. We also show that our method results in a negligible loss of endogenous data with no measurable impact on downstream population genetics analyses.","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/PMC11646131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823681","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 single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN. 利用 GCN 将基于点的空间转录组学、位置和组织学融合在一起，推断单细胞分辨率的空间基因表达。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae630

Shuailin Xue, Fangfang Zhu, Jinyu Chen, Wenwen Min

{"title":"Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN.","authors":"Shuailin Xue, Fangfang Zhu, Jinyu Chen, Wenwen Min","doi":"10.1093/bib/bbae630","DOIUrl":"10.1093/bib/bbae630","url":null,"abstract":"Spatial transcriptomics (ST technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization, and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer and Graph Convolutional Network that integrates histological images, spot-based ST data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately and aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations. Our source code and all used datasets are available at https://github.com/wenwenmin/scstGCN and https://zenodo.org/records/12800375.","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/PMC11645551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827387","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

R3Design: deep tertiary structure-based RNA sequence design and beyond. R3Design：基于深层三级结构的RNA序列设计及超越。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae682

Cheng Tan, Yijie Zhang, Zhangyang Gao, Hanqun Cao, Siyuan Li, Siqi Ma, Mathieu Blanchette, Stan Z Li

{"title":"R3Design: deep tertiary structure-based RNA sequence design and beyond.","authors":"Cheng Tan, Yijie Zhang, Zhangyang Gao, Hanqun Cao, Siyuan Li, Siqi Ma, Mathieu Blanchette, Stan Z Li","doi":"10.1093/bib/bbae682","DOIUrl":"10.1093/bib/bbae682","url":null,"abstract":"The rational design of Ribonucleic acid (RNA) molecules is crucial for advancing therapeutic applications, synthetic biology, and understanding the fundamental principles of life. Traditional RNA design methods have predominantly focused on secondary structure-based sequence design, often neglecting the intricate and essential tertiary interactions. We introduce R3Design, a tertiary structure-based RNA sequence design method that shifts the paradigm to prioritize tertiary structure in the RNA sequence design. R3Design significantly enhances sequence design on native RNA backbones, achieving high sequence recovery and Macro-F1 score, and outperforming traditional secondary structure-based approaches by substantial margins. We demonstrate that R3Design can design RNA sequences that fold into the desired tertiary structures by validating these predictions using advanced structure prediction models. This method, which is available through standalone software, provides a comprehensive toolkit for designing, folding, and evaluating RNA at the tertiary level. Our findings demonstrate R3Design's superior capability in designing RNA sequences, which achieves around $44%$ in terms of both recovery score and Macro-F1 score in multiple datasets. This not only denotes the accuracy and fairness of the model but also underscores its potential to drive forward the development of innovative RNA-based therapeutics and to deepen our understanding of RNA biology.","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/PMC11685104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906282","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

Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks. 将scRNA-seq和scATAC-seq与类型间注意异构图神经网络相结合。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae711

Lingsheng Cai, Xiuli Ma, Jianzhu Ma

{"title":"Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.","authors":"Lingsheng Cai, Xiuli Ma, Jianzhu Ma","doi":"10.1093/bib/bbae711","DOIUrl":"10.1093/bib/bbae711","url":null,"abstract":"Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data into a shared latent space by learning cross-modality relationships. By modeling cells and modality features as distinct node types, we design an inter-type attention mechanism to effectively capture long-range cross-modality interactions between genes and peaks. Benchmark results demonstrate that embeddings learned by scMI preserve more biological information and achieve comparable or superior performance in downstream tasks including modality prediction, cell clustering, and gene regulatory network inference compared to methods that rely on databases. Furthermore, scMI significantly improves the alignment and integration of unmatched multi-omics data, enabling more accurate embedding and improved outcomes in downstream tasks.","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/PMC11725394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969346","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

Higher order interaction analysis quantifies coordination in the epigenome revealing novel biological relationships in Kabuki syndrome. 高阶相互作用分析量化了表观基因组中的协调，揭示了歌舞伎综合征中的新型生物学关系。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-11-22 DOI: 10.1093/bib/bbae667

Sara Cuvertino, Terence Garner, Evgenii Martirosian, Bridgious Walusimbi, Susan J Kimber, Siddharth Banka, Adam Stevens

{"title":"Higher order interaction analysis quantifies coordination in the epigenome revealing novel biological relationships in Kabuki syndrome.","authors":"Sara Cuvertino, Terence Garner, Evgenii Martirosian, Bridgious Walusimbi, Susan J Kimber, Siddharth Banka, Adam Stevens","doi":"10.1093/bib/bbae667","DOIUrl":"10.1093/bib/bbae667","url":null,"abstract":"Complex direct and indirect relationships between multiple variables, termed higher order interactions (HOIs), are characteristics of all natural systems. Traditional differential and network analyses fail to account for the omic datasets richness and miss HOIs. We investigated peripheral blood DNA methylation data from Kabuki syndrome type 1 (KS1) and control individuals, identified 2,002 differentially methylated points (DMPs), and inferred 17 differentially methylated regions, which represent only 189 DMPs. We applied hypergraph models to measure HOIs on all the CpGs and revealed differences in the coordination of DMPs with lower entropy and higher coordination of the peripheral epigenome in KS1 implying reduced network complexity. Hypergraphs also capture epigenomic trans-relationships, and identify biologically relevant pathways that escape the standard analyses. These findings construct the basis of a suitable model for the analysis of organization in the epigenome in rare diseases, which can be applied to investigate mechanism in big data.","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/PMC11658816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863356","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