RegulonDB (version 2.0): a database on transcriptional regulation in Escherichia coli

Salgado, Heladia; Santos, A García de los; Garza–Ramos, Ulises; Helden, Jacques van; Díaz, Edgar Leyva; Collado‐Vides, Julio

doi:10.1093/nar/27.1.59

Cited by 37 publications

(19 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, the trp operon would include the genes trpA, trpB, trpC, trpD, and trpE. In their network, Ong and Page included variables that represented the activity levels of 141 known operons determined from previous work (Salgado et al, 1999;Craven et al, 2000) and the genes in each operon. The level of expression of each gene is controlled by the activity of its operon.…”

Section: Effects On Reconstruction Of Causal Relationsmentioning

confidence: 99%

Temporal Aggregation Bias and Inference of Causal Regulatory Networks

Bay

Chrisman

Pohorille

et al. 2004

Journal of Computational Biology

View full text Add to dashboard Cite

Time course experiments with microarrays have begun to provide a glimpse into the dynamic behavior of gene expression. In a typical experiment, scientists use microarrays to measure the abundance of mRNA at discrete time points after the onset of a stimulus. Recently, there has been much work on using these data to infer causal regulatory networks that model how genes influence each other. However, microarray studies typically have slow sampling rates that can lead to temporal aggregation of the signal. That is, each successive sampling point represents the sum of all signal changes since the previous sample. In this paper, we show that temporal aggregation can bias algorithms for causal inference and lead them to discover spurious relations that would not be found if the signal were sampled at a much faster rate. We discuss the implications of temporal aggregation on inference, the problems it creates, and potential directions for solutions.

show abstract

Section: Effects On Reconstruction Of Causal Relationsmentioning

confidence: 99%

Temporal Aggregation Bias and Inference of Causal Regulatory Networks

Bay

Chrisman

Pohorille

et al. 2004

Journal of Computational Biology

View full text Add to dashboard Cite

show abstract

“…RegulonDB is an exhaustive database, accessible through the Internet, containing information compiled from the literature about genetic regulation and operon organization in Escherichia coli (6,7). The present work is based on a collection of 361 known transcription units obtained from RegulonDB.…”

mentioning

confidence: 99%

Operons in Escherichia coli : Genomic analyses and predictions

Salgado

Moreno–Hagelsieb

Smith

et al. 2000

Proc. Natl. Acad. Sci. U.S.A.

317

319

View full text Add to dashboard Cite

The rich knowledge of operon organization in Escherichia coli, together with the completed chromosomal sequence of this bacterium, enabled us to perform an analysis of distances between genes and of functional relationships of adjacent genes in the same operon, as opposed to adjacent genes in different transcription units. We measured and demonstrated the expected tendencies of genes within operons to have much shorter intergenic distances than genes at the borders of transcription units. A clear peak at short distances between genes in the same operon contrasts with a flat frequency distribution of genes at the borders of transcription units. Also, genes in the same operon tend to have the same physiological functional class. The results of these analyses were used to implement a method to predict the genomic organization of genes into transcription units. The method has a maximum accuracy of 88% correct identification of pairs of adjacent genes to be in an operon, or at the borders of transcription units, and correctly identifies around 75% of the known transcription units when used to predict the transcription unit organization of the E. coli genome. Based on the frequency distance distributions, we estimated a total of 630 to 700 operons in E. coli. This step opens the possibility of predicting operon organization in other bacteria whose genome sequences have been finished.

show abstract

“…Nowadays, RegulonDB is a relational database service to the scientific community involved in the study of bacteria, offering in an organized and computable form, knowledge on transcriptional regulation that has been manually curated, from original scientific publications. This includes curated information of transcription initiation through the activation or repression of transcription factors (TFs), which bind to individual sites around promoters [17]. Table I shows the differences among these biological databases, where can be seen mainly the differences of the knowledge domains.…”

Section: Biological Databasesmentioning

confidence: 99%

Integrating Biological Databases in the Context of Transcriptional Regulatory Networks

Pereira¹,

Mendes²

2014

IJBBB

View full text Add to dashboard Cite

Abstract-Several studies show that biological knowledge is growing at a continuous rate and distributed among different databases, making the process of data integration a hard task to perform, because they have different structures, different ways of storing data and also different approaches to export information, and are usually developed to provide information for a specific organism. Due to the large amount of biological data, the process of data integration has been one of the major challenges in the field of bioinformatics as well as discovering information about Transcriptional Regulatory Networks (TRN). When using a single source, this task is not easy to perform since the source often lacks enough information for the successful completion of the task. Therefore it is necessary to find information in several databases in order to create a useful body of knowledge. This work presents a new approach of integrating data related with TRNs for the Escherichia coli by creating a new integrated data repository gathering information from KEGG, EcoCyc, Regulon and NCBI databases.

show abstract

RegulonDB (version 2.0): a database on transcriptional regulation in Escherichia coli

Cited by 37 publications

References 5 publications

Temporal Aggregation Bias and Inference of Causal Regulatory Networks

Temporal Aggregation Bias and Inference of Causal Regulatory Networks

Operons in Escherichia coli : Genomic analyses and predictions

Integrating Biological Databases in the Context of Transcriptional Regulatory Networks

Contact Info

Product

Resources

About