Diploid genome architecture revealed by multi-omic data of hybrid mice

: 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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Methods for mapping three-dimensional genome architecture

Methods ◽

10.1016/j.ymeth.2019.10.011 ◽

2020 ◽

Vol 170 ◽

pp. 1-3

Author(s):

Surabhi Chowdhary ◽

Amoldeep S. Kainth ◽

David S. Gross

Keyword(s):

Three Dimensional ◽

Genome Architecture

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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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Mitochondrial genome architecture and phylogenetic relationships of Odontesthes argentinensis within Atherinomorpha

Genetica ◽

10.1007/s10709-021-00116-8 ◽

2021 ◽

Author(s):

Javier Calvelo ◽

Alejandro D’Anatro

Keyword(s):

Mitochondrial Genome ◽

Phylogenetic Relationships ◽

Genome Architecture ◽

Odontesthes Argentinensis

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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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GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data

Bioinformatics ◽

10.1093/bioinformatics/btab238 ◽

2021 ◽

Author(s):

Julia Markowski ◽

Rieke Kempfer ◽

Alexander Kukalev ◽

Ibai Irastorza-Azcarate ◽

Gesa Loof ◽

...

Keyword(s):

Genetic Variants ◽

Embryonic Stem Cell Line ◽

Embryonic Stem ◽

Mouse Embryonic Stem Cell ◽

Supplementary Information ◽

Model Organisms ◽

Genome Architecture ◽

Chromatin Conformation ◽

Haplotype Reconstruction ◽

Allele Specific

Abstract Motivation Genome Architecture Mapping (GAM) was recently introduced as a digestion- and ligation-free method to detect chromatin conformation. Orthogonal to existing approaches based on chromatin conformation capture (3C), GAM’s ability to capture both inter- and intra-chromosomal contacts from low amounts of input data makes it particularly well suited for allele-specific analyses in a clinical setting. Allele-specific analyses are powerful tools to investigate the effects of genetic variants on many cellular phenotypes including chromatin conformation, but require the haplotypes of the individuals under study to be known a-priori. So far however, no algorithm exists for haplotype reconstruction and phasing of genetic variants from GAM data, hindering the allele-specific analysis of chromatin contact points in non-model organisms or individuals with unknown haplotypes. Results We present GAMIBHEAR, a tool for accurate haplotype reconstruction from GAM data. GAMIBHEAR aggregates allelic co-observation frequencies from GAM data and employs a GAM-specific probabilistic model of haplotype capture to optimise phasing accuracy. Using a hybrid mouse embryonic stem cell line with known haplotype structure as a benchmark dataset, we assess correctness and completeness of the reconstructed haplotypes, and demonstrate the power of GAMIBHEAR to infer accurate genome-wide haplotypes from GAM data. Availability GAMIBHEAR is available as an R package under the open source GPL-2 license at https://bitbucket.org/schwarzlab/gamibhear Maintainer [email protected] Supplementary information Supplementary information is available at Bioinformatics online.

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