Functional organization of the transcriptome in human brain

By Michael C. Oldham, G. Konopka, K. Iwamoto, Peter Langfelder, Tadafumi Kato, Steve Horvath, Daniel H. Geschwind. Nature Neuroscience, 2008

761 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/17332c0e0367566a6c3fdf59ea487e1cf18e7254

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Steve Horvath
2. Peter Langfelder
3. Ye Deng
4. Jizhong Zhou
5. Lin Song
6. Daniel H. Geschwind
7. Zhili He
8. Feng Luo

Citations in the corpus

• Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia (2016, positive): “440 441 Consistent with prior studies of the brain transcriptome (27, 67-70), we find gene co442 expression to be organized into modules of distinct cellular and functional categories (data file 443 7).”
• Strategies for aggregating gene expression data: The collapseRows R function (2011, background): “For example, in the case of transcriptional networks, genes showing positive correlation are more likely than uncorrelated or negatively correlated genes to have known protein-protein interactions [2-4].”
• Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2 (2011, background): “Based on the oftinvoked principle of guilt by association (26), the abundance changes in the microbial populations with strong module memberships are probably driven by the same underlying factors.”
• Absence of CNTNAP2 leads to epilepsy, neuronal migration abnormalities and core autism-related deficits (2011, neutral): “Modules are likely to represent biological pathways in a way that they are usually coregulated or interact (Oldham et al., 2008).”
• The modular and integrative functional architecture of the human brain (2015, background): “For example, gene expression in the brain is modular; the transcriptomes of human brain regions are robustly organized into modules of coexpressed genes that reflect the underlying cellular composition of brain tissue (8), and the spatial topography of cortex is also strongly reflected in its geneti”
• Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease (2013, methodology): “…gene signatures, from the Allen Brain Atlas (http://www.brain-map.org/), were enriched in distinct network modules as previously observed (Oldham et al., 2008): neurons in the synaptic transmission modules (11-fold, p = 3.7 3 10 24), astrocytes in the GABA biosynthesis module (22 fold, p…”
• Is My Network Module Preserved and Reproducible? (2011, methodology): “We used microarray data from a recent publication [30]”
• Canonical Genetic Signatures of the Adult Human Brain (2015, positive): “Studies using these methods suggest that, largely because of their nonparametric statistically robust nature, conserved differential expression relationships may be more descriptive of transcriptome organization than absolute magnitude of expression level 3, 5, 11–13 .”
• Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, background): “…organization within the hierarchy of brain region, cell type, organelle and molecular pathways using only transcriptional data, and can thus enable integration with other information, such as known pathway annotations, protein interactions and other molecular profiling data 11,12,26,27 FIG.”
• Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology (2017, background): “One potential way forward is to investigate topological overlap (Costantini, 2014; Oldham et al., 2008; Zhang & Horvath, 2005) (Fig.”
• Functional and Evolutionary Insights Into Human Brain Development Through Global Transcriptome Analysis (2009, methodology): “We therefore performed weighted gene coexpression network analysis to identify groups of coregulated genes, or ‘‘modules,’’ with similar patterns of connectivity (high topological overlap) (Oldham et al., 2008).”
• Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “unsigned networks depends on the application; both signed [22] and unsigned [30,61,65] weighted gene networks have been successfully used in gene expression analysis.”