Genetic Studies Through the Lens of Gene Networks

Annual Review of Biomedical Data Science, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Understanding the genetic basis of complex traits is a longstanding challenge in field genomics. Genome-wide association studies have identified thousands variant-trait associations, but most these variants are located noncoding regions, making link to biological function elusive. While traditional approaches, such as transcriptome-wide (TWAS), advanced our understanding by linking gene expression, they often overlook gene-gene interactions. Here, we review current approaches integrate different molecular data, leveraging machine learning methods identify modules based on coexpression and functional relationships. These integrative PhenoPLIER, combine TWAS drug-induced transcriptional profiles effectively capture biologically meaningful networks. This integration provides context-specific disease processes while highlighting both core peripheral genes. insights pave way for novel therapeutic targets enhance interpretability personalized medicine.

Язык: Английский

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Interpretable AI for inference of causal molecular relationships from omics data

Science Advances, Год журнала: 2025, Номер 11(7)

Опубликована: Фев. 14, 2025

The discovery of molecular relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise this context but lack causal interpretation. Here, we show that popular model, under certain assumptions, estimates an average quantity reflecting the direct influence one variable on another. We leverage insight to propose precise definition gene regulatory relationship implement new tool, CIMLA (Counterfactual Inference by Learning Attribution Models), identify differences networks between biological conditions, has received attention recent years. Using extensive benchmarking simulated data, more robust confounding variables accurate than leading methods. Last, use analyze previously published single-cell RNA sequencing dataset subjects with without Alzheimer’s disease (AD), discovering several potential regulators AD.

Язык: Английский

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The transcription factor Traffic jam orchestrates the somatic piRNA pathway in Drosophila ovaries

Cell Reports, Год журнала: 2025, Номер unknown, С. 115453 - 115453

Опубликована: Апрель 1, 2025

Язык: Английский

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Interpretable deep learning in single-cell omics

Bioinformatics, Год журнала: 2024, Номер 40(6)

Опубликована: Июнь 1, 2024

Abstract Motivation Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field machine has instilled significant interest single-cell research due to its remarkable success analysing heterogeneous high-dimensional data. Nevertheless, inherent multi-layer nonlinear architecture deep learning models often makes them ‘black boxes’ as reasoning behind predictions is unknown and not transparent user. This stimulated increasing body for addressing lack interpretability models, especially data analyses, where identification understanding regulators are crucial interpreting model directing downstream experimental validations. Results In this work, we introduce basics concept interpretable learning. followed by review recent applied various research. Lastly, highlight current limitations discuss potential future directions.

Язык: Английский

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Single-cell technology for plant systems biology

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 133 - 156

Опубликована: Янв. 1, 2025

Язык: Английский

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Analysis of gene regulatory networks from gene expression using graph neural networks

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 249 - 270

Опубликована: Янв. 1, 2025

Язык: Английский

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

Briefings in Bioinformatics, Год журнала: 2025, Номер 26(2)

Опубликована: Март 1, 2025

Abstract The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series due to high noise levels sparsity. temporal dimension introduces additional complexity by requiring models capture dynamic changes, increasing sensitivity noise, exacerbating sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, negative binomial (NB)-based loss functions, infer GRNs. was evaluated using popular benchmarking datasets, where it demonstrated superior performance compared eight well-known GRN inference methods. integration NB-based function penalties significantly enhanced its capacity address dropout data. Additionally, exhibited robustness against noise. We applied the whole mouse brain obtained through BRAIN Initiative project identified five transcription regulators: E2f7, Gbx1, Sox10, Prox1, Onecut2, which play crucial roles diverse cell types. inferred not only recalled known relationships but also revealed sets novel interactions with functional potential. These findings demonstrate is highly effective tool real-world applications discovering relationships.

Язык: Английский

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Single-cell epigenetics and multiomics analysis in kidney research

Clinical and Experimental Nephrology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

Язык: Английский

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Recent advances in exploring transcriptional regulatory landscape of crops

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Июнь 5, 2024

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of phenotype by altering the expression particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend transcriptional mechanisms that underpin these In multi-omics era, a large amount omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, single-cell omics. The abundant resources emergence advanced computational tools offer unprecedented opportunities obtaining holistic view profound understanding processes linked desirable This review focuses on integrated network approaches utilize investigate gene regulation. Various types networks their inference methods are discussed, focusing recent advancements in plants. integration proven be crucial construction high-confidence networks. With refinement methodologies, they will significantly enhance efforts contribute global food security.

Язык: Английский

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A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(5)

Опубликована: Июль 24, 2024

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays vital role in development disease can be modeled at systems level regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples even cells has lifted field GRN inference to next stage. Combinations (single-cell) transcriptomics chromatin accessibility allow prediction fine-grained programs go beyond mere correlation transcription factor target expression, with enhancer GRNs (eGRNs) modeling molecular interactions between factors, elements, genes. In this review, we highlight key components for successful (e)GRN from (sc)RNA-seq (sc)ATAC-seq exemplified by state-of-the-art methods as well open challenges future developments. Moreover, address preprocessing strategies, metacell generation computational omics pairing, binding site detection, linear three-dimensional approaches identify dynamic causal eGRN inference. We believe integration together epigenomics single-cell new standard mechanistic network inference, it further advanced integrating additional layers spatiotemporal data, shifting focus towards more strategies.

Язык: Английский

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