regulondb — RegulonDB E. coli transcriptional regulatory networks (2026)

Description

Networks of transcriptional regulation in the bacterium Escherichia coli K-12, from RegulonDB, the database of the Computational Genomics Program of the Center for Genomic Sciences (CCG) of the National Autonomous University of Mexico (UNAM), release 14.5 (2026). RegulonDB is curated mainly from the literature (and from high-throughput experiments), and records which regulators (transcription factors, but also small RNAs and compounds such as ppGpp) activate or repress the transcription of which genes and transcription units, and which sigma factors of the RNA polymerase recognize their promoters. Each regulatory interaction has a function (activation +, repression -, dual -+, or unknown) and a confidence level based on its evidence: confirmed (C), strong (S) or weak (W) (or unknown, ?).

The entry contains the network files of the release:

regulator_gene: directed edges from each regulator to the genes whose transcription it regulates (NetworkRegulatorGene, with the additional columns of NetworkRegulatorGene_internal).
regulator_conformation_gene: the same, with an edge for each active conformation of the regulator (e.g. a transcription factor bound to its effector) that regulates the gene (NetworkRegulatorConfGene); the regulator and the gene may be joined by several edges.
regulator_tu: directed edges from each regulator to the transcription units (sets of genes transcribed from a promoter) that it regulates (NetworkRegulatorTU).
regulator_regulator: directed edges between regulators, from each regulator to the regulators whose genes it regulates (NetworkRegulatorRegulator).
sigma_gene and sigma_tu: directed edges from each sigma factor to the genes or transcription units transcribed from promoters that it recognizes (NetworkSigmaGene and NetworkSigmaTU).

The entry also contains the network of transcription factors and the genes they regulate of earlier releases (network_tf_gene.txt, the precursor of NetworkRegulatorGene), from the copies of the file kept by the Internet Archive, one network for each of its seven distinct versions:

tf_gene_v9.2: release 9.2 (2016-08-09), 4,424 interactions;
tf_gene_v9.4: release 9.4 (2017-08-05), 4,490 interactions;
tf_gene_v10.0: release 10.0 (2018-06-18), 4,564 interactions;
tf_gene_v10.6.3: release 10.6.3 (2019-02-12), 4,627 interactions;
tf_gene_v10.7: release 10.7 (2020-04-05), 4,670 interactions;
tf_gene_v10.9: release 10.9 (2021-06-29), 4,727 interactions;
tf_gene_v11.0: release 11.0 (2022-08-22), 4,775 interactions.

These contain only transcription factors (not small RNAs or compounds). Each line of the file is an edge, so that a transcription factor and a gene can be joined by several edges with different functions (e.g. one for activation and one for repression, in about 200 cases). The files of releases 9.2 to 10.7 identify transcription factors and genes by their names only. The interactions of release 10.9 mostly have no confidence level (null upstream).

Except for regulator_regulator, the networks are bipartite: regulators (or sigma factors) and their targets are distinct nodes, even when a regulator regulates its own gene. The releases of RegulonDB before 9.2 (e.g. version 8, of 2013) are no longer available for download; data derived from earlier releases are part of the entries ecoli_transcription, gerstein_regulatory and signed_biological.

The files of this entry are available only with an access token, since the RegulonDB terms of use forbid integrating RegulonDB, in part or as a whole, into other databank systems without the prior written consent of CCG-UNAM (RegulonDB is free for academic and noncommercial use).

The networks have the following properties:

name: the name of the regulator, sigma factor, gene or transcription unit (transcription units are named by their genes followed by their promoter in brackets, e.g. fkpA[fkpAp1]); id: the RegulonDB identifier (empty in sigma_gene and sigma_tu, and in the tf_gene_<release> networks before release 10.9, whose files give only names).
In regulator_gene, regulator_conformation_gene, regulator_tu and the tf_gene_<release> networks, is_regulator: whether the node is a regulator (otherwise a gene or a transcription unit).
In regulator_gene, regulator_conformation_gene and regulator_tu, regulator_genes: the gene(s) that code for the regulator (empty for regulators that are not proteins and for targets).
In regulator_gene, regulator_type: the kind of regulator (TF, sRNA, compound or Regulator; empty for genes); synonyms: the synonyms of the regulator or the gene.
In sigma_gene and sigma_tu, is_sigma: whether the node is a sigma factor.
Edge function: + (activation), - (repression), -+ (dual; +- in the tf_gene_<release> networks), ? or empty (unknown); confidence: the confidence level (C, S, W or ?; in the tf_gene_<release> networks, the upstream levels Confirmed, Strong and Weak are written in the same way, and the level is empty where it is null upstream; the files of releases 9.2 and 10.6.3 do not list the column of the levels in their headers, but contain it). In release 10.7, whose file gives the function as activator, repressor or unknown, these are written +, - and ?.
In the tf_gene_<release> networks, edge evidence: the evidence codes of the interaction (e.g. GEA, BPP).
In regulator_gene, edge confidence_internal: the confidence level given in the "internal" version of the file (NetworkRegulatorGene_internal), which differs from confidence for 207 of the 7,351 interactions; evidence_category: the categories of the evidence of the interaction (e.g. Classical expression), from the internal file.
In regulator_conformation_gene, edge conformation: the name of the active conformation of the regulator.
In sigma_gene and sigma_tu, edge promoter_evidence: the evidence codes for the promoter and its recognition by the sigma factor.

Tags

Biological Gene regulation Unweighted Metadata

Citation

H. Salgado, S. Gama-Castro, P. Lara, C. Mejia-Almonte, G. Alarcón-Carranza, A. G. López-Almazo, et al., "RegulonDB v12.0: a comprehensive resource of transcriptional regulation in E. coli K-12." Nucleic Acids Research 52, D255-D264 (2024) 10.1093/nar/gkad1072 Sci-Hub

Upstream URL OK

https://regulondb.ccg.unam.mx/datasets
https://web.archive.org/web/20221016225322/http://regulondb.ccg.unam.mx/menu/download/datasets/files/network_tf_gene.txt

Upstream license

Restricted access. Upstream does not permit data redistribution, hence the network files cannot be downloaded without authorization. See here how to gain access.

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
regulator_gene	3,112	7,351	2.36	21.64	24.14	47.41	-0.34	0.00	9	0.97	Directed	Bipartite	name id is_regulator regulator_genes regulator_type synonyms	function confidence confidence_internal evidence_category	109 KiB	152 KiB	150 KiB	134 KiB
regulator_conformation_gene	3,112	7,477	2.40	21.93	24.14	47.41	-0.35	0.00	9	0.97	Directed	Bipartite	name id regulator_genes is_regulator	function confidence conformation	85 KiB	124 KiB	123 KiB	107 KiB
regulator_tu	1,906	3,322	1.74	12.33	14.08	31.23	-0.28	0.00	12	0.94	Directed	Bipartite	name id regulator_genes is_regulator	function confidence	57 KiB	77 KiB	76 KiB	63 KiB
regulator_regulator	243	594	2.44	6.44	10.24	10.03	-0.09	0.10	8	0.88	Directed	Unipartite	name id regulator_genes	function confidence	10 KiB	13 KiB	13 KiB	11 KiB
sigma_gene	2,173	2,597	1.20	38.05	15.43	9.91	-0.59	0.00	6	1.00	Directed	Bipartite	name id is_sigma	function confidence promoter_evidence	51 KiB	70 KiB	69 KiB	58 KiB
sigma_tu	1,641	1,634	1.00	28.66	1.00	0.01	-0.62	0.00	2	0.69	Directed	Bipartite	name id is_sigma	function confidence promoter_evidence	46 KiB	61 KiB	60 KiB	50 KiB
tf_gene_v9.2	1,991	4,424	2.22	18.69	19.64	24.35	-0.28	0.00	10	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	49 KiB	70 KiB	69 KiB	58 KiB
tf_gene_v9.4	2,014	4,490	2.23	18.80	19.90	38.78	-0.28	0.00	12	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	50 KiB	72 KiB	71 KiB	59 KiB
tf_gene_v10.0	2,030	4,564	2.25	18.90	20.05	38.78	-0.28	0.00	12	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	50 KiB	71 KiB	70 KiB	60 KiB
tf_gene_v10.6.3	2,052	4,627	2.25	18.96	20.11	36.74	-0.27	0.00	12	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	51 KiB	74 KiB	72 KiB	61 KiB
tf_gene_v10.7	2,058	4,670	2.27	19.13	20.11	36.74	-0.27	0.00	12	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	51 KiB	73 KiB	72 KiB	61 KiB
tf_gene_v10.9	2,068	4,727	2.29	19.22	20.18	36.74	-0.27	0.00	12	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	51 KiB	72 KiB	71 KiB	60 KiB
tf_gene_v11.0	2,086	4,775	2.29	19.35	20.27	36.74	-0.27	0.00	12	0.94	Directed	Bipartite	name id is_regulator	function confidence evidence	60 KiB	83 KiB	82 KiB	74 KiB

Ridiculograms^*

regulator_conformation_gene drawing — regulator_conformation_gene

regulator_regulator drawing — regulator_regulator

tf_gene_v10.6.3 drawing — tf_gene_v10.6.3

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.