Evidence map›Paper›PMID 16677396›Full record

ArticleGenome biology2006

Inferring transcriptional modules from ChIP-chip, motif and microarray data.

Karen Lemmens, Thomas Dhollander, Tijl De Bie, Pieter Monsieurs, Kristof Engelen, Bart Smets, Joris Winderickx, Bart De Moor, Kathleen Marchal

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
–field-weighted citation impact
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

44 citing papers in PubMed.

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  8. A graphical model method for integrating multiple sources of genome-scale data.Statistical applications in genetics and molecular biology · 2013
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  9. Computational methodology for ChIP-seq analysis.Quantitative biology (Beijing, China) · 2013
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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

9 authors.

Karen LemmensBIOI@SCD, Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg, B-3001 Heverlee, Belgium.
Thomas Dhollander
Tijl De Bie
Pieter Monsieurs
Kristof Engelen
Bart Smets
Joris Winderickx
Bart De Moor
Kathleen Marchal

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

'ReMoDiscovery' is an intuitive algorithm to correlate regulatory programs with regulators and corresponding motifs to a set of co-expressed genes. It exploits in a concurrent way three independent data sources: ChIP-chip data, motif information and gene expression profiles. When compared to published module discovery algorithms, ReMoDiscovery is fast and easily tunable. We evaluated our method on yeast data, where it was shown to generate biologically meaningful findings and allowed the prediction of potential novel roles of transcriptional regulators.

Indexed as

AlgorithmsChromatin ImmunoprecipitationGene Expression ProfilingGene Expression RegulationOligonucleotide Array Sequence AnalysisAmino AcidsCell CycleGalactoseRegulatory Elements, TranscriptionalRibosomesSoftwareTranscription FactorsYeastsAmino AcidsGalactoseTranscription Factors

Identifiers

PMID16677396
PMCPMC1779513

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.