scholarly journals A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control

2014 ◽  
Vol 30 (16) ◽  
pp. 2360-2366 ◽  
Author(s):  
Yinming Jiao ◽  
Martin Widschwendter ◽  
Andrew E. Teschendorff
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Epigenetic Control ◽  
Integrative Framework ◽  
Genome Wide ◽  
Differential Gene

scholarly journals A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins

2020 ◽  
Vol 14 ◽  
Author(s):  
Mette Soerensen ◽  
Dominika Marzena Hozakowska-Roszkowska ◽  
Marianne Nygaard ◽  
Martin J. Larsen ◽  
Veit Schwämmle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cognitive Functioning ◽  
Association Study ◽  
Gene Expression Data ◽  
Monozygotic Twins ◽  
Later Life ◽  
Expression Data ◽  
Genome Wide ◽  
A Genome

scholarly journals SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data

2021 ◽  
Author(s):  
Magdalena Navarro ◽  
T Ian Simpson
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Gene List ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Expression Data ◽  
Link Type ◽  
Differential Gene

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]

scholarly journals An integrated analysis of genome‐wide DNA methylation and gene expression data in hepatocellular carcinoma

FEBS Open Bio ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 1093-1103 ◽  
Author(s):  
Xiang‐Jun Sun ◽  
Ming‐Chun Wang ◽  
Feng‐Hua Zhang ◽  
Xiao Kong
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Gene Expression Data ◽  
Integrated Analysis ◽  
Expression Data ◽  
Genome Wide

scholarly journals Reconciling differential gene expression data with molecular interaction networks

2013 ◽  
Vol 29 (5) ◽  
pp. 622-629 ◽  
Author(s):  
Christopher L. Poirel ◽  
Ahsanur Rahman ◽  
Richard R. Rodrigues ◽  
Arjun Krishnan ◽  
Jacqueline R. Addesa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Differential Gene

scholarly journals Python for gene expression

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 870
Author(s):  
Leonid Bystrykh
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Programming Language ◽  
Gene Expression Data ◽  
Expression Data ◽  
R Programming Language ◽  
Substantial Progress ◽  
Differential Gene ◽  
R Programming ◽  
Computational Part

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

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