Article
Biochemical Research Methods
Zhen Gao, Jin Tang, Junfeng Xia, Chun-Hou Zheng, Pi-Jing Wei
Summary: This study proposes a supervised model called CNNGRN, which uses a convolutional neural network to reconstruct gene regulatory networks from gene expression data. The model integrates gene expression data and network structure information, and uses the extracted complex features to infer regulatory relationships. Experimental results show that CNNGRN achieves competitive performance on benchmark datasets and confirms key genes involved in biological processes.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Irina Gilyazova, Elizaveta Ivanova, Himanshu Gupta, Artur Mustafin, Ruslan Ishemgulov, Adel Izmailov, Gulshat Gilyazova, Elena Pudova, Valentin Pavlov, Elza Khusnutdinova, Bo-Ying Bao, Yung-Hsiang Chen
Summary: This study aimed to investigate the miRNA profile in tissue samples obtained from patients with prostate cancer (PCa) and find potential markers for diagnosis and prognosis. The study found that certain miRNAs were downregulated in early-stage cancer patients, which may be useful for diagnosing and predicting PCa.
Article
Oncology
Yanglan Gan, Xin Hu, Guobing Zou, Cairong Yan, Guangwei Xu
Summary: The article introduces a new computational algorithm BiRGRN for inferring gene regulatory networks (GRNs) from time-series single-cell RNA-seq data. The algorithm utilizes a bidirectional recurrent neural network to transform the inference of GRNs into a regression problem, and adopts bidirectional structure and prior knowledge filtering strategy to improve accuracy and stability.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biochemical Research Methods
Neel Patel, William S. Bush
Summary: This study developed a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features. The models performed significantly better compared to models containing only cis-regulatory features, and the inclusion of long distance chromatin interactions further improved accuracy. The refined effect estimates generated by the models allow for characterization of individual transcription factors' roles across the genome, providing a framework for integrating multiple data types into a single model of transcriptional regulation.
BMC BIOINFORMATICS
(2021)
Article
Biology
Yanglan Gan, Yongchang Xin, Xin Hu, Guobing Zou
Summary: Gene regulatory network models the interactions between transcription factors and target genes, and is crucial for understanding gene function. iMPRN, a computational method integrating multiple prior networks, can accurately infer and optimize regulatory networks leading to key insights into gene regulation.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2021)
Article
Biochemistry & Molecular Biology
Vijaykumar Yogesh Muley, Rainer Koenig
Summary: This study compares transcriptional regulatory pairs from multiple databases and finds that TF and TG are common across data resources, but their regulatory pairs are not. The regulatory pairs show weak expression correlation, but significant gene ontology overlap and co-citations in PubMed, with a small number of TF-TG pairs representing transcriptional repression relationships. The assembled TRN provides a valuable resource for benchmarking TRN prediction tools and for researchers in functional genomics, gene expression, and regulation analysis.
Article
Biotechnology & Applied Microbiology
Zhi-Jie Cao, Ge Gao
Summary: GLUE is a computational framework that bridges the gap between different omics layers by modeling regulatory interactions, and it outperforms state-of-the-art tools in accuracy, robustness, and scalability for heterogeneous single-cell multi-omics data. It has been successfully applied in various challenging tasks.
NATURE BIOTECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Chuanyuan Wang, Shiyu Xu, Zhi-Ping Liu
Summary: This study proposes a framework called Network Activity Evaluation (NAE) that evaluates the activity of gene regulatory events by measuring the consistency between gene expression data and network structure. The efficiency and advantages of the NAE framework are demonstrated through multiple experiments and comparison studies.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Mathematical & Computational Biology
M. Lefebvre, A. Gaignard, M. Folschette, J. Bourdon, C. Guziolowski
Summary: Efforts are being made to organize biological knowledge through linked open databases for automatic reconstruction of regulatory and signaling networks. However, manual operations are still required due to source-specific identification of biological entities and relationships, redundant information in multiple databases, and the challenge of recovering logical flows in biological pathways.
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2021)
Article
Biochemical Research Methods
Ramzan Umarov, Yu Li, Takahiro Arakawa, Satoshi Takizawa, Xin Gao, Erik Arner
Summary: Accurate identification of regulatory elements like promoters and enhancers is crucial for understanding gene expression patterns. While many attempts have been made to develop computational methods, reliable tools for analyzing long genomic sequences are still lacking. To address this issue, the authors propose a dynamic negative set updating scheme and use a two-model approach, achieving good performance at the genome level.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Review
Biochemical Research Methods
Mengyuan Zhao, Wenying He, Jijun Tang, Quan Zou, Fei Guo
Summary: The study focuses on the importance of GRN reconstruction technologies in biology and medical science, discussing different method classifications and their performance in networks of varying scales. The aim is to discover potential drug targets and identify cancer biomarkers.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Ana Carolina Leote, Xiaohui Wu, Andreas Beyer
Summary: Single-cell RNA sequencing methods often cannot accurately quantify the expression levels of all genes in a cell. In this study, we propose a network-based approach for dropout imputation, which utilizes gene-gene relationship information from external datasets. Our approach outperforms existing methods in various human scRNA-seq datasets, especially for lowly expressed genes. We also find that some genes cannot be adequately imputed by any method tested. Based on our findings, we developed an R-package called ADImpute that automatically determines the best imputation method for each gene in a dataset.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Genetics & Heredity
Pauline Schmitt, Baptiste Sorin, Timothee Froute, Nicolas Parisot, Federica Calevro, Sergio Peignier
Summary: This research introduces GReNaDIne, a Python package that implements 18 machine learning data-driven gene regulatory network inference methods. It also includes preprocessing techniques suitable for both RNA-seq and microarray dataset analysis, as well as normalization techniques dedicated to RNA-seq. In addition, it allows for combining the results of different inference tools to form robust ensembles. The package has been successfully evaluated under the DREAM5 challenge benchmark dataset.
Article
Genetics & Heredity
Guimin Qin, Longting Du, Yuying Ma, Yu Yin, Liming Wang
Summary: This study proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. Through analyzing expression status of malignant cells, constructing cell-specific networks, integrating transcriptional regulatory relationships, and performing hybrid clustering analysis, six identified cell types were obtained, along with potential tumor gene biomarkers. Survival analysis and literature verification supported the relevance of these candidate tumor gene biomarkers to glioma, particularly those belonging to the NADH ubiquinone oxidoreductase subunit gene family.
BMC MEDICAL GENOMICS
(2021)
Article
Genetics & Heredity
Yan Wang, Lvyu Yang, Hansheng Zhou, Kunlin Zhang, Mei Zhao
Summary: In this study, it was found that L-methionine can reverse the expression of genes and miRNAs affected by cocaine. Furthermore, L-methionine was shown to counteract the effects of cocaine by modulating the calcium channel gene network and pathways associated with drug addiction. These findings provide insights into the underlying mechanisms of L-methionine in response to cocaine abuse.
FRONTIERS IN GENETICS
(2023)
Article
Biochemistry & Molecular Biology
Tao Shi, Razgar Seyed Rahmani, Paul F. Gugger, Muhua Wang, Hui Li, Yue Zhang, Zhizhong Li, Qingfeng Wang, Yves Van de Peer, Kathleen Marchal, Jinming Chen
MOLECULAR BIOLOGY AND EVOLUTION
(2020)
Article
Biochemistry & Molecular Biology
Arthur Zwaenepoel, Yves Van de Peer
MOLECULAR BIOLOGY AND EVOLUTION
(2020)
Article
Multidisciplinary Sciences
Fu-Sheng Yang, Shuai Nie, Hui Liu, Tian-Le Shi, Xue-Chan Tian, Shan-Shan Zhou, Yu-Tao Bao, Kai-Hua Jia, Jing-Fang Guo, Wei Zhao, Na An, Ren-Gang Zhang, Quan-Zheng Yun, Xin-Zhu Wang, Chanaka Mannapperuma, Ilga Porth, Yousry Aly El-Kassaby, Nathaniel Robert Street, Xiao-Ru Wang, Yves Van de Peer, Jian-Feng Mao
NATURE COMMUNICATIONS
(2020)
Article
Biology
Sarah Farhat, Phuong Le, Ehsan Kayal, Benjamin Noel, Estelle Bigeard, Erwan Corre, Florian Maumus, Isabelle Florent, Adriana Alberti, Jean-Marc Aury, Tristan Barbeyron, Ruibo Cai, Corinne Da Silva, Benjamin Istace, Karine Labadie, Dominique Marie, Jonathan Mercier, Tsinda Rukwavu, Jeremy Szymczak, Thierry Tonon, Catharina Alves-de-Souza, Pierre Rouze, Yves van de Peer, Patrick Wincker, Stephane Rombauts, Betina M. Porcel, Laure Guillou
Summary: This study sequenced and analyzed the genomes of two early-diverging parasitic dinoflagellate Amoebophrya strains, revealing compact genomes, shared orthologs with Dinophyceae, and high levels of gene synteny conservation. Interestingly, non-canonical introns with diverse splicing motifs were identified, suggesting rapid protein evolution in these unicellular parasites. Loss of organelles was also confirmed, raising questions about speciation and evolutionary mechanisms in parasitic unicellular lineages.
Article
Genetics & Heredity
Andrew A. Crawford, Sean Bankier, Elisabeth Altmaier, Catriona L. K. Barnes, David W. Clark, Raili Ermel, Nele Friedrich, Pim van der Harst, Peter K. Joshi, Ville Karhunen, Jari Lahti, Anubha Mahajan, Massimo Mangino, Maria Nethander, Alexander Neumann, Maik Pietzner, Katyayani Sukhavasi, Carol A. Wang, Stephan J. L. Bakker, Johan L. M. Bjorkegren, Harry Campbell, Johan Eriksson, Christian Gieger, Caroline Hayward, Marjo-Riitta Jarvelin, Stela McLachlan, Andrew P. Morris, Claes Ohlsson, Craig E. Pennell, Jackie Price, Igor Rudan, Arno Ruusalepp, Tim Spector, Henning Tiemeier, Henry Volzke, James F. Wilson, Tom Michoel, Nicolas J. Timpson, George Davey Smith, Brian R. Walker
Summary: Cortisol plays a role in metabolism, cardiovascular health, mood, inflammation, and cognition. Genetic variants in the SERPINA6/SERPINA1 locus are associated with morning plasma cortisol levels and influence the expression of corticosteroid binding globulin (CBG) and alpha 1-antitrypsin. Genetically-determined increases in morning plasma cortisol are linked to higher odds of chronic ischaemic heart disease and myocardial infarction.
JOURNAL OF HUMAN GENETICS
(2021)
Article
Plant Sciences
Hui Liu, Hai-Meng Lyu, Kaikai Zhu, Yves Van de Peer, Zong-Ming (Max) Cheng
Summary: This study identified multiple gene families in plant genomes containing intronless or intron-poor genes, which play important roles in response to drought and salt stress, epigenetic processes, and plant development. The origin, evolution, and potential functions of these intronless and intron-poor sub-families provide insights into plant genome evolution and gene functional divergence.
Article
Plant Sciences
Jia-Yu Xue, Shan-Shan Dong, Ming-Qiang Wang, Tian-Qiang Song, Guang-Can Zhou, Zhen Li, Yves Van de Peer, Zhu-Qing Shao, Wei Wang, Min Chen, Yan-Mei Zhang, Xiao-Qin Sun, Hong-Feng Chen, Yong-Xia Zhang, Shou-Zhou Zhang, Fei Chen, Liang-Sheng Zhang, Cymon Cox, Yang Liu, Qiang Wang, Yue-Yu Hang
Summary: The early diversification of angiosperms was a rapid process, leading to conflicting hypotheses regarding overall angiosperm phylogeny. Studies of mitochondrial genomes filled taxon-sampling gaps in various angiosperm lineages and provided additional evidence for exploring the early evolution and diversification of angiosperms. Despite conflicting phylogenies, there was congruence regarding deep relationships of several major angiosperm lineages, with third codon positions of mitochondrial genes producing better resolved phylogenetic relationships.
JOURNAL OF SYSTEMATICS AND EVOLUTION
(2022)
Article
Biotechnology & Applied Microbiology
Kathryn Bartley, Wan Chen, Richard I. Lloyd Mills, Francesca Nunn, Daniel R. G. Price, Stephane Rombauts, Yves Van de Peer, Lise Roy, Alasdair J. Nisbet, Stewart T. G. Burgess
Summary: This study provides the first evaluation of temporal gene expression across all stages of PRM, shedding light on the developmental, feeding, reproduction, and survival strategies employed by this mite. The publicly available PRM resource on OrcAE serves as a valuable tool for researchers investigating the biology and novel interventions of this parasite.
Article
Multidisciplinary Sciences
Lisette J. A. Kogelman, Katrine Falkenberg, Alfonso Buil, Pau Erola, Julie Courraud, Susan Svane Laursen, Tom Michoel, Jes Olesen, Thomas F. Hansen
Summary: The study found differential gene expression during migraine attacks compared to after treatment, involving pathways related to fatty acid oxidation, signaling pathways, and immune pathways. Network analysis revealed mechanisms affected by changes in gene interactions, and integration of genomic and transcriptomic data revealed pathways related to sumatriptan treatment.
SCIENTIFIC REPORTS
(2021)
Article
Biochemical Research Methods
Ramin Hasibi, Tom Michoel
Summary: Graph neural networks are effective in representing nodes in molecular interaction networks and correlating them with gene expression data to explain gene expression variations. Using gene expression data as node features, combined with a novel graph feature auto-encoder framework, can better predict unobserved node features.
BMC BIOINFORMATICS
(2021)
Article
Genetics & Heredity
Muhammad Ammar Malik, Tom Michoel
Summary: In this study, a restricted maximum-likelihood (REML) method is proposed to estimate the contribution of latent variance components and known factors to the sample covariance in gene expression data. By proving that maximum-likelihood latent variables can be chosen orthogonal to known factors, the method achieves efficient solutions using standard matrix operations and obtains latent factors that do not overlap with known factors.
G3-GENES GENOMES GENETICS
(2022)
Article
Genetics & Heredity
Julien Guemri, Morgane Pierre-Jean, Solene Brohard, Nouara Oussada, Caroline Horgues, Eric Bonnet, Florence Mauger, Jean-Francois Deleuze
Summary: This study developed a targeted sequencing protocol to identify potential noninvasive biomarkers of Alzheimer's disease (AD) based on methylated ccfDNA. The authors identified methylated CpGs that were consistent with previous AD studies, as well as potential novel sites. These findings suggest that methylated ccfDNA could be a useful noninvasive biomarker for AD.
Proceedings Paper
Biochemical Research Methods
Muhammad Ammar Malik, Adriaan-Alexander Ludl, Tom Michoel
Summary: Multi-trait genome-wide association studies (GWAS) use multi-variate statistical methods to identify associations between genetic variants and multiple correlated traits simultaneously. Reverse regression, a promising approach in high-dimensional settings, allows for predicting the strength of associations between genotypes and traits, as well as identifying true transcriptional regulatory networks.
COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2021
(2022)
Article
Biochemistry & Molecular Biology
Adriaan-Alexander Ludl, Tom Michoel
Summary: Causal gene networks model the flow of information within a cell, and efforts to reconstruct these networks from omics data face challenges of correlation versus causation. Different methods, including instrumental variable and mediation-based approaches, play complementary roles in identifying causal genes. While causal inference methods show significant overlap with ground-truth networks, coexpression methods do not perform better than random. A subsampling analysis indicates that the performance of mediation saturates at large sample sizes, while instrumental variable methods contain false positive predictions due to genomic linkage. Further development of methods to control for residual correlations and pleiotropic effects could enhance causal inference from genomics and transcriptomics data.
Review
Biochemistry & Molecular Biology
Yves Van de Peer, Tia-Lynn Ashman, Pamela S. Soltis, Douglas E. Soltis
Summary: Polyploidy is hypothesized to play a role in both evolutionary dead-ends and diversification. Research suggests that whole-genome duplications may be linked with extinction events or global changes, while polyploids thrive in harsh environments. Biotic interactions, particularly with pathogens or mutualists, have differing effects on polyploids compared to nonpolyploids. Stress response is identified as a key factor in the establishment and success of polyploidy.
