Meta-Analysis of Transcriptome Data Related to Hippocampus Biopsies and iPSC-Derived Neuronal Cells from Alzheimer’s Disease Patients Reveals an Association with FOXA1 and FOXA2 Gene Regulatory Networks

2016 ◽  
Vol 50 (4) ◽  
pp. 1065-1082 ◽  
Author(s):  
Wasco Wruck ◽  
Friederike Schröter ◽  
James Adjaye
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Neuronal Cells ◽  
Transcriptome Data ◽  
Gene Regulatory

scholarly journals Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

2020 ◽  
Author(s):  
Mufang Ying ◽  
Peter Rehani ◽  
Panagiotis Roussos ◽  
Daifeng Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Disease Genes ◽  
Omics Data ◽  
Cell Type ◽  
Cellular Resolution ◽  
Gene Regulatory

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

scholarly journals Analytical Strategy to Prioritize Alzheimer’s Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

2017 ◽  
Vol 59 (4) ◽  
pp. 1237-1254 ◽  
Author(s):  
Shweta Bagewadi Kawalia ◽  
Tamara Raschka ◽  
Mufassra Naz ◽  
Ricardo de Matos Simoes ◽  
Philipp Senger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Candidate Genes ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Analytical Strategy ◽  
Public Expression ◽  
Gene Regulatory

Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods

Methods ◽  
2014 ◽  
Vol 67 (3) ◽  
pp. 294-303 ◽  
Author(s):  
Jing Qin ◽  
Yaohua Hu ◽  
Feng Xu ◽  
Hari Krishna Yalamanchili ◽  
Junwen Wang
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regularization Methods ◽  
Transcriptome Data ◽  
Gene Regulatory

scholarly journals Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 319
Author(s):  
Ionut Sebastian Mihai ◽  
Debojyoti Das ◽  
Gabija Maršalkaite ◽  
Johan Henriksson
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Single Gene ◽  
Biological Properties ◽  
Biological Features ◽  
Disease Relevance ◽  
Gene Regulatory ◽  
The Impact ◽  
Family Membership

The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.

scholarly journals Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data

2013 ◽  
Vol 14 (1) ◽  
pp. 278 ◽  
Author(s):  
Mingzhu Zhu ◽  
Jeremy L Dahmen ◽  
Gary Stacey ◽  
Jianlin Cheng
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Gene Regulatory ◽  
Soybean Nodulation
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