Review
Biochemical Research Methods
Zhaoqian Liu, Anjun Ma, Ewy Mathe, Marlena Merling, Qin Ma, Bingqiang Liu
Summary: The relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. High-throughput Omics technologies offer an opportunity for understanding the structures and functions of microbiome, but data analysis remains challenging. Network analyses provide an efficient way to understand complex microbial communities.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Juan D. Henao, Michael Lauber, Manuel Azevedo, Anastasiia Grekova, Fabian Theis, Markus List, Christoph Ogris, Benjamin Schubert
Summary: This study integrated regression-based methods that can handle missingness into KiMONo, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. The results showed that two-step approaches that explicitly handle missingness performed best for imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best for balanced omics-layers dimensions. The study demonstrated the feasibility of robust multi-omics network inference in the presence of missing data with KiMONo.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Multidisciplinary Sciences
S. Vickovic, B. Lotstedt, J. Klughammer, S. Mages, A. Segerstolpe, O. Rozenblatt-Rosen, A. Regev
Summary: The spatial organization of cells and molecules is crucial for tissue function and disease. Spatial transcriptomics, a technique for capturing and locating RNA in tissues, has been advanced with the development of a fully automated platform called Spatial Multi-Omics (SM-Omics). SM-Omics combines spatial transcriptomics and antibody-based protein measurement, allowing high-throughput analysis of multiple omics in a short time.
NATURE COMMUNICATIONS
(2022)
Article
Biology
Ekaterina V. Poverennaya, Mikhail A. Pyatnitskiy, Georgii V. Dolgalev, Viktoria A. Arzumanian, Olga I. Kiseleva, Ilya Yu. Kurbatov, Leonid K. Kurbatov, Igor V. Vakhrushev, Daniil D. Romashin, Yan S. Kim, Elena A. Ponomarenko
Summary: TOMM34 plays a crucial role in mitochondrial functioning and is associated with various cellular processes such as purine metabolism, DNA replication and repair, and protein degradation. Our multi-omics study provides new insights into the cellular functions of TOMM34.
Article
Biochemistry & Molecular Biology
Yanhong Li, Jie Wang, Mauricio A. Elzo, Mingchuan Gan, Tao Tang, Jiahao Shao, Tianfu Lai, Yuan Ma, Xianbo Jia, Songjia Lai
Summary: Through experiments, it was found that in rabbits induced to be obese by a high-fat diet, there were significant differences in the expression of certain miRNAs and core genes, with the miRNAs being able to regulate transcription factors that impact lipid metabolism. These results contribute to a better understanding of the molecular mechanisms of skeletal muscle metabolism in rabbits.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Engineering, Chemical
Yi-Fan Tong, Qi-En He, Jun-Xuan Zhu, En-Ci Ding, Kai Song
Summary: A systematic method was proposed to infer differential gene regulatory networks among different stages of lung adenocarcinoma samples, revealing potential mechanisms and biomarkers for tumor progression.
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
Multidisciplinary Sciences
Christoph Ogris, Yue Hu, Janine Arloth, Nikola S. Mueller
Summary: The constantly decreasing costs of high-throughput analysis generate vast amounts of multi-omics data, our versatile approach KiMONo demonstrates robustness to noise and general applicability to multi-omics data, showing potential for detecting biomarker candidates.
SCIENTIFIC REPORTS
(2021)
Article
Multidisciplinary Sciences
Frederique Ruf-Zamojski, Zidong Zhang, Michel Zamojski, Gregory R. Smith, Natalia Mendelev, Hanqing Liu, German Nudelman, Mika Moriwaki, Hanna Pincas, Rosa Gomez Castanon, Venugopalan D. Nair, Nitish Seenarine, Mary Anne S. Amper, Xiang Zhou, Luisina Ongaro, Chirine Toufaily, Gauthier Schang, Joseph R. Nery, Anna Bartlett, Andrew Aldridge, Nimisha Jain, Gwen Childs, Olga G. Troyanskaya, Joseph R. Ecker, Judith L. Turgeon, Corrine K. Welt, Daniel J. Bernard, Stuart C. Sealfon
Summary: In order to investigate transcriptional regulatory mechanisms, a team profiled the transcriptome, chromatin accessibility, and methylation status of over 70,000 single nuclei from adult mouse pituitaries. They identified transcriptional and chromatin accessibility programs distinguishing each major cell type, as well as co-regulated gene sets that recapitulate cell type clustering. The study highlights the centrality of chromatin accessibility in shaping cell-defining transcriptional programs.
NATURE COMMUNICATIONS
(2021)
Article
Biochemical Research Methods
Lingyu Cui, Hongfei Li, Jilong Bian, Guohua Wang, Yingjian Liang
Summary: This study proposes a novel strategy to construct gene regulatory networks (GRNs) at the resolution of single cells. It integrates single-cell RNA sequencing and single-cell Assay for Transposase-Accessible Chromatin using sequencing data and uses an unsupervised learning neural network to identify the GRN in each gene block.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Chunman Zuo, Hao Dai, Luonan Chen
Summary: DCCA is a computational tool for joint analysis of single-cell multi-omics data, capable of dissecting cellular heterogeneity, denoising and aggregating data, and constructing links between multi-omics data. By fine-tuning networks and inferring new transcriptional regulatory relations, DCCA demonstrates superior capability in analyzing and understanding complex biological processes.
Article
Biology
Chenxu Xuan, Yan Wang, Bai Zhang, Hanwen Wu, Tao Ding, Jie Gao
Summary: This article proposes an algorithm that integrates single-cell multi-omics data to construct gene regulatory networks, and it has been verified using hepatocellular carcinoma (HCC) data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biochemical Research Methods
Anna Pacinkova, Vlad Popovici
Summary: IntOMICS is a new R/Bioconductor package that integrates multi-omics data using a Bayesian framework. It utilizes gene expression, copy number variation, DNA methylation, and biological prior knowledge to infer regulatory networks. The unique feature of IntOMICS is its ability to estimate biological knowledge from experimental data, making it a valuable resource for exploratory systems biology.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2023)
Review
Immunology
Jessica M. Zielinski, Jason J. Luke, Silvia Guglietta, Carsten Krieg
Summary: High throughput single cell multi-omics platforms have enabled unprecedented insights into biological and clinical questions, creating whole atlases of cell types and interaction networks. Combining different -omic workflows on a single cell level can help examine cellular phenotypes and immune effector functions, as well as accelerate cellular biomarker discovery and definition of druggable target pathways. Challenges in the field include sharing disruptive technologies to scientific communities while incorporating new approaches like genomic cytometry and single cell metabolomics.
FRONTIERS IN IMMUNOLOGY
(2021)
Review
Biochemistry & Molecular Biology
Claudio Fiocchi
Summary: The recent advancements in technologies like sequencing and mass spectroscopy, combined with artificial intelligence-powered analytic tools, have revolutionized big data research in complex diseases, including inflammatory bowel disease (IBD). This review provides a comprehensive assessment of the current knowledge on omes, omics, and multi-omics in IBD, highlighting their importance in understanding disease mechanisms and potential clinical applications such as biomarker identification and precision medicine. The review also critically analyzes the limitations of current IBD multi-omics studies and suggests ways to optimize the use of multi-omics data for better clinical and therapeutic outcomes. Finally, the review predicts the future incorporation of multi-omics analyses in the routine management of IBD.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)