The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

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Science for the Next Century: Deep Phenotyping

Journal of Dental Research ◽

10.1177/00220345211001850 ◽

2021 ◽

pp. 002203452110018

Author(s):

J.T. Wright ◽

M.C. Herzberg

Keyword(s):

Health Care ◽

Human Health ◽

Care Delivery ◽

The Past ◽

Large Populations ◽

Health And Disease ◽

Methodological Approaches ◽

Omic Data ◽

Precision Health ◽

Deep Phenotyping

Our ability to unravel the mysteries of human health and disease have changed dramatically over the past 2 decades. Decoding health and disease has been facilitated by the recent availability of high-throughput genomics and multi-omics analyses and the companion tools of advanced informatics and computational science. Understanding of the human genome and its influence on phenotype continues to advance through genotyping large populations and using “light phenotyping” approaches in combination with smaller subsets of the population being evaluated using “deep phenotyping” approaches. Using our capability to integrate and jointly analyze genomic data with other multi-omic data, the knowledge of genotype-phenotype relationships and associated genetic pathways and functions is being advanced. Understanding genotype-phenotype relationships that discriminate human health from disease is speculated to facilitate predictive, precision health care and change modes of health care delivery. The American Association for Dental Research Fall Focused Symposium assembled experts to discuss how studies of genotype-phenotype relationships are illuminating the pathophysiology of craniofacial diseases and developmental biology. Although the breadth of the topic did not allow all areas of dental, oral, and craniofacial research to be addressed (e.g., cancer), the importance and power of integrating genomic, phenomic, and other -omic data are illustrated using a variety of examples. The 8 Fall Focused talks presented different methodological approaches for ascertaining study populations and evaluating population variance and phenotyping approaches. These advances are reviewed in this summary.

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Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Communications Biology ◽

10.1038/s42003-020-01583-z ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Daniel J. Panyard ◽

Kyeong Mo Kim ◽

Burcu F. Darst ◽

Yuetiva K. Deming ◽

Xiaoyuan Zhong ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Drug Targets ◽

Large Scale ◽

Prediction Models ◽

Genome Wide Association ◽

Large Samples ◽

Genome Wide ◽

Metabolomic Data ◽

Related Phenotype ◽

Omic Data

AbstractThe study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

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Integrating molecular graph data of drugs and multiple -omic data of cell lines for drug response prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2021.3096960 ◽

2021 ◽

pp. 1-1

Author(s):

Giang Thi Thu Nguyen ◽

Duc-Hoa Vu ◽

Duc-Hau Le

Keyword(s):

Cell Lines ◽

Drug Response ◽

Molecular Graph ◽

Response Prediction ◽

Graph Data ◽

Omic Data

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Aggregation of Omic Data and Secretome Prediction Enable the Discovery of Candidate Plasma Biomarkers for Beef Tenderness

International Journal of Molecular Sciences ◽

10.3390/ijms21020664 ◽

2020 ◽

Vol 21 (2) ◽

pp. 664 ◽

Cited By ~ 3

Author(s):

Sabrina Boudon ◽

Joelle Henry-Berger ◽

Isabelle Cassar-Malek

Keyword(s):

Plasma Proteins ◽

Biological Information ◽

Plasma Proteome ◽

Complex Phenotype ◽

Beef Tenderness ◽

Plasma Biomarkers ◽

Bovine Plasma ◽

Computational Reconstruction ◽

Beef Sector ◽

Omic Data

Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.

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Optimization algorithm for omic data subspace clustering

10.1145/3486713.3486742 ◽

2021 ◽

Author(s):

Madalina Ciortan ◽

Matthieu Defrance

Keyword(s):

Optimization Algorithm ◽

Subspace Clustering ◽

Omic Data

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Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

10.1101/2022.01.13.476163 ◽

2022 ◽

Author(s):

Malvika Sudhakar ◽

Raghunathan Rengaswamy ◽

Karthik Raman

Keyword(s):

Tumour Progression ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Driver Mutations ◽

Cancer Type ◽

Expression Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Types ◽

Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

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Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis

Nature Communications ◽

10.1038/s41467-021-26165-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Natalie M. Clark ◽

Trevor M. Nolan ◽

Ping Wang ◽

Gaoyuan Song ◽

Christian Montes ◽

...

Keyword(s):

Transcription Factor ◽

Cell Division ◽

Network Inference ◽

Phosphorylation Sites ◽

Molecular Signaling ◽

Signaling Events ◽

Spatiotemporal Clustering ◽

Integrated Omics ◽

Omic Data ◽

Integrative Network

AbstractBrassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) to integrate these data. We use our network predictions to identify putative phosphorylation sites on BES1 and experimentally validate their importance. Additionally, we identify BRONTOSAURUS (BRON) as a transcription factor that regulates cell division, and we show that BRON expression is modulated by BR-responsive kinases and transcription factors. This work demonstrates the power of integrative network analysis applied to multi-omic data and provides fundamental insights into the molecular signaling events occurring during BR response.

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