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BMC Biol. 2018 Aug 16;16(1):91. doi: 10.1186/s12915-018-0555-y.

A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0.

Author information

1
Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México.
2
School of Biosciences, University of Birmingham, Birmingham, UK.
3
Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
4
Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
5
Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
6
Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México. collado@ccg.unam.mx.
7
Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. collado@ccg.unam.mx.

Abstract

BACKGROUND:

Our understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data.

RESULTS:

In this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge.

CONCLUSIONS:

This version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on other microbial organisms.

KEYWORDS:

ChIP-seq; Integrative analyses; Systems biology; Transcriptional regulation; Transcriptomics; gSELEX

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