Transcriptomics of the depressed and PTSD brain.
TLDR
This article reviewed the state of the field focusing on the technologies used to study the transcriptome for the stress neurobiologist, and also attempt to identify central questions about the heterogeneity of stress for those applying transcriptomic approaches.About:
This article is published in Neurobiology of Stress.The article was published on 2021-10-11 and is currently open access. It has received 4 citations till now.read more
Citations
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Journal ArticleDOI
Genetics and epigenetics of stress: New avenues for an old concept
Journal ArticleDOI
Matthew J. Friedman and the VA National PTSD Brain Bank: New Transcriptomic Insight into PTSD Pathophysiology
TL;DR: This paper applied stereological techniques to the challenging study of postmortem human brains to investigate genetic and other factors contributing to Alzheimer's, schizophrenia, mood disorders, and PTSD, and found that these factors are contributory to post-mortem human brain diseases.
Journal ArticleDOI
Cross-Species Convergence of Brain Transcriptomic and Epigenomic Findings in Posttraumatic Stress Disorder: A Systematic Review.
D. Núñez-Ríos,José Jaime Martínez-Magaña,Sheila Tiemi Nagamatsu,John H. Krystal,Karen G Martínez-González,Paola Giusti-Rodríguez,Janitza L. Montalvo-Ortiz +6 more
TL;DR: In this paper , a systematic literature search according to the PRISMA criteria was performed to identify transcriptomic and epigenomic studies of PTSD, focusing on brain tissue from human postmortem samples or animal stress paradigms.
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Identification of novel targets and pathways to distinguish suicide dependent or independent on depression diagnosis
TL;DR: Wang et al. as mentioned in this paper identified potential phenotypic, tissue, and sex-specific novel targets and pathways to distinguish depression or suicide from major depressive disorder (MDD) comorbid with suicide.
References
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
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Analyzing real-time PCR data by the comparative C(T) method.
TL;DR: This protocol provides an overview of the comparative CT method for quantitative gene expression studies and various examples to present quantitative gene Expression data using this method.
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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Bo Li,Colin N. Dewey +1 more
TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
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WGCNA: an R package for weighted correlation network analysis.
Peter Langfelder,Steve Horvath +1 more
TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
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