Evidence map›Paper›PMID 16522208›Full record

ArticleBMC bioinformatics2006

An improved map of conserved regulatory sites for Saccharomyces cerevisiae.

Kenzie D MacIsaac, Ting Wang, D Benjamin Gordon, David K Gifford, Gary D Stormo, Ernest Fraenkel

Abstract read
In one paragraph

Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 445 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
445citing papers in PubMed, 1 pooled it
–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

445 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Histone deacetylase-mediated regulation of endolysosomal pH.The Journal of biological chemistry · 2018
    Pooled it
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  18. Inferring independent sets of Gaussian variables after thresholding correlations.Journal of the American Statistical Association · 2025
    Article
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  20. Review

385 more citing papers are in PubMed but not listed here.

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

6 authors.

Kenzie D MacIsaacWhitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu
Ting Wang
D Benjamin Gordon
David K Gifford
Gary D Stormo
Ernest Fraenkel

Funding

Transcriptional Regulatory Networks in Living CellsR01HG002668 · NHGRI · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI YOUNG, RICHARD · 2003 to 2016
$17.9M
INSTITUTIONAL TRAINING GRANT IN GENOMIC SCIENCET32HG000045 · NHGRI · WASHINGTON UNIVERSITY · PI MICHAEL R BRENT, Barak A Cohen · 1997 to 2026
$8.4M
DNA PATTERN IDENTIFICATION AND ANALYSISR01HG000249 · NHGRI · WASHINGTON UNIVERSITY · PI STORMO, GARY D · 1990 to 2017
$5.6M
NHGRI NIH HHS 1R01 HG002668-01NHGRI NIH HHS 2T32HG00045NHGRI NIH HHS HG00249NHGRI NIH HHS R01 HG000249NHGRI NIH HHS R01 HG002668NHGRI NIH HHS T32 HG000045
6 · The paper itself

Abstract

backgroundThe regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data.

resultsApplying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators.

conclusionCombining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously.

Indexed as

AlgorithmsChromosome MappingConserved SequenceGene Expression Regulation, FungalPhylogenyRegulatory Elements, TranscriptionalSaccharomyces cerevisiaeSequence Analysis, DNATranscriptional ActivationTranscription FactorsTranscription Factors

Identifiers

PMID16522208
PMCPMC1435934

What OpenQuestion holds

Textmetadata
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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.