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gerstein_regulatory — Gerstein lab regulatory network hierarchies (2006-2010)

Description

Directed regulatory networks of several organisms, used by the Gerstein lab to study the organization of regulatory networks into hierarchies. Nodes are genes (or proteins), and a directed edge points from a regulator to one of its targets.

From Yu and Gerstein (2006), transcriptional regulatory networks, where the regulators are transcription factors:

  • yeast_2006: the yeast Saccharomyces cerevisiae (genes identified by systematic ORF names).
  • ecoli_2006: the bacterium Escherichia coli (genes identified by Blattner b-numbers).

From Bhardwaj, Yan and Gerstein (2010) (also used by Bhardwaj, Kim and Gerstein, 2010):

  • yeast: transcriptional regulatory network of S. cerevisiae, from genetic and biochemical experiments of several previous studies.
  • ecoli: transcriptional regulatory network of E. coli, from RegulonDB (version 6.2, July 2009).
  • mouse, rat, human: transcriptional regulatory networks from the Transcriptional Regulatory Element Database (TRED, as of June 2008).
  • mtuberculosis: transcriptional regulatory network of Mycobacterium tuberculosis, from published microarray data.
  • human_modification: protein modification network of human, where the regulators modify their target proteins post-translationally, from the Human Protein Reference Database (HPRD).
  • yeast_phosphorylation: phosphorylation network of S. cerevisiae, where the regulators are kinases that phosphorylate their target proteins, from two large-scale experimental studies.

In the source files of the 2010 networks, regulators and targets are often spelled with different capitalization (e.g., the E. coli regulator AcrR and its gene acrR); here, names that differ only in capitalization are merged into a single node, named after its first occurrence. Self-loops (autoregulation) and, for mtuberculosis and yeast_phosphorylation, repeated edges are present, as in the source data.

The networks have the following properties:

  • Nodes: name, the gene or protein name; level (2010 networks only), the level of regulators in the hierarchy of Bhardwaj, Yan and Gerstein (2010), "top" (regulators not regulated by other regulators), "middle" (regulators regulated by and regulating other regulators) or "bottom" (regulators regulated by other regulators but not regulating any), and empty for the non-regulator targets; hierarchy_level (yeast and ecoli only), the level of regulators in the hierarchy of Bhardwaj, Kim and Gerstein (2010), from 1 (bottom) to 4 (top), and 0 for the non-regulator targets. The levels are those given in the files of the source, which do not entirely match the networks: some regulators have no level, and some names in the level files do not correspond to nodes in the networks (these are ignored).
  • Edges: regulation (ecoli only), the type of regulation, + (activation), - (repression), +- (dual), or with a ? when uncertain.
Tags
Biological Gene regulation Unweighted Metadata
Citation
  • H. Yu, M. Gerstein, "Genomic analysis of the hierarchical structure of regulatory networks." Proceedings of the National Academy of Sciences 103(40), 14724-14731 (2006) 10.1073/pnas.0508637103 Sci-Hub
  • N. Bhardwaj, K.-K. Yan, M. B. Gerstein, "Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels." Proceedings of the National Academy of Sciences 107(15), 6841-6846 (2010) 10.1073/pnas.0910867107 Sci-Hub
  • N. Bhardwaj, P. M. Kim, M. B. Gerstein, "Rewiring of Transcriptional Regulatory Networks: Hierarchy, Rather Than Connectivity, Better Reflects the Importance of Regulators." Science Signaling 3(146), ra79 (2010) 10.1126/scisignal.2001014 Sci-Hub
Upstream URL OK
https://info.gersteinlab.org/Hierarchy
Networks
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
yeast_2006 3,458 8,371 2.42 17.00 21.44 8.65 -0.43 0.01 8 1.00 Directed Unipartite name 24 KiB 40 KiB 39 KiB 39 KiB
ecoli_2006 1,095 2,044 1.87 11.68 14.18 74.59 -0.26 0.02 10 0.89 Directed Unipartite name 6 KiB 10 KiB 9 KiB 11 KiB
yeast 4,441 12,873 2.90 22.04 30.68 4.17 -0.60 0.01 6 1.00 Directed Unipartite name level hierarchy_level 33 KiB 58 KiB 57 KiB 57 KiB
ecoli 1,471 3,123 2.12 16.84 19.26 44.92 -0.31 0.01 9 0.93 Directed Unipartite name level hierarchy_level regulation 10 KiB 18 KiB 17 KiB 17 KiB
mouse 1,165 2,403 2.06 11.04 14.78 25.52 -0.40 0.01 9 0.99 Directed Unipartite name level 10 KiB 15 KiB 15 KiB 14 KiB
rat 521 1,092 2.10 9.53 11.32 14.06 -0.38 0.01 8 0.99 Directed Unipartite name level 5 KiB 8 KiB 7 KiB 7 KiB
human 3,090 6,896 2.23 25.02 29.56 11.24 -0.38 0.01 8 1.00 Directed Unipartite name level 24 KiB 37 KiB 36 KiB 36 KiB
mtuberculosis 756 937 1.24 9.46 6.87 154.26 -0.33 0.01 12 0.91 Directed Unipartite name level 6 KiB 7 KiB 7 KiB 7 KiB
human_modification 1,684 2,782 1.65 7.24 10.57 73.32 -0.23 0.00 14 0.87 Directed Unipartite name level 16 KiB 23 KiB 22 KiB 21 KiB
yeast_phosphorylation 1,407 4,083 2.90 18.22 25.13 6.84 -0.49 0.01 8 1.00 Directed Unipartite name level 13 KiB 21 KiB 21 KiB 19 KiB