Evidence map›Paper›PMID 18269733›Full record

ArticleBMC genomics2008

Predicted transcription factor binding sites as predictors of operons in Escherichia coli and Streptomyces coelicolor.

Emma Laing, Khushwant Sidhu, Simon J Hubbard

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 30 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Emma LaingFaculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK. e.laing@surrey.ac.uk
Khushwant Sidhu
Simon J Hubbard
University of Manchester · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs a polycistronic transcriptional unit of one or more adjacent genes, operons play a key role in regulation and function in prokaryotic biology, and a better understanding of how they are constituted and controlled is needed. Recent efforts have attempted to predict operonic status in sequenced genomes using a variety of techniques and data sources. To date, non-homology based operon prediction strategies have mainly used predicted promoters and terminators present at the extremities of transcriptional unit as predictors, with reasonable success. However, transcription factor binding sites (TFBSs), typically found upstream of the first gene in an operon, have not yet been evaluated.

resultsHere we apply a method originally developed for the prediction of TFBSs in Escherichia coli that minimises the need for prior knowledge and tests its ability to predict operons in E. coli and the 'more complex', pharmaceutically important, Streptomyces coelicolor. We demonstrate that through building genome specific TFBS position-specific-weight-matrices (PSWMs) it is possible to predict operons in E. coli and S. coelicolor with 83% and 93% accuracy respectively, using only TFBS as delimiters of operons. Additionally, the 'palindromicity' of TFBS footprint data of E. coli is characterised.

conclusionTFBS are proposed as novel independent features for use in prokaryotic operon prediction (whether alone or as part of a set of features) given their efficacy as operon predictors in E. coli and S. coelicolor. We also show that TFBS footprint data in E. coli generally contains inverted repeats with significantly (p < 0.05) greater palindromicity than random sequences. Consequently, the palindromicity of putative TFBSs predicted can also enhance operon predictions.

Indexed as

Models, GeneticAlgorithmsBinding SitesEscherichia coliGene Expression Regulation, BacterialGenome, BacterialOperonRepetitive Sequences, Nucleic AcidStreptomyces coelicolorSubstrate SpecificityTranscription FactorsTranscription Factors

Identifiers

PMID18269733
PMCPMC2276206
OpenAlexW2111276087

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.