ArticleBMC systems biology2015
Differential variability and correlation of gene expression identifies key genes involved in neuronal differentiation.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Article
- clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution.PeerJ · 2023Article
- Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.Frontiers in cell and developmental biology · 2021Article
- On the Use of Heterogeneous Stock Mice to Map Transcriptomes Associated With Excessive Ethanol Consumption.Frontiers in psychiatry · 2021Review
- RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models.Alcoholism, clinical and experimental research · 2020Article
- Identification of an early cell fate regulator by detecting dynamics in transcriptional heterogeneity and co-regulation during astrocyte differentiation.NPJ systems biology and applications · 2019Article
- Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs.Alcoholism, clinical and experimental research · 2018Article
- Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice.Genes, brain, and behavior · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding the dynamics of stem cell differentiation processes at the molecular level is a central challenge in developmental biology and regenerative medicine. Although the dynamic behaviors of differentiation regulators have been partially characterized, the architecture regulating the underlying molecular systems remains unclear.
resultSystem-level analysis of transcriptional data was performed to characterize the dynamics of molecular networks in neural differentiation of stem cells. Expression of a network module of genes tightly co-expressed in mouse embryonic stem (ES) cells fluctuated greatly among cell populations before differentiation, but became stable following neural differentiation. During the neural differentiation process, genes exhibiting both differential variance and differential correlation between undifferentiated and differentiating states were related to developmental functions such as body axis development, neuronal movement, and transcriptional regulation. Furthermore, these genes were genetically associated with neuronal differentiation, providing support for the idea they are not only differentiation markers but could also play important roles in neural differentiation. Comparisons with transcriptional data from human induced pluripotent stem (iPS) cells revealed that the system of genes dynamically regulated during neural differentiation is conserved between mouse and human.
conclusionsThe results of this study provide a systematic analytical framework for identifying key genes involved in neural differentiation by detecting their dynamical behaviors, as well as a basis for understanding the dynamic molecular mechanisms underlying the processes of neural differentiation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.