Evidence map›Paper›PMID 17210927›Full record

ArticleGenome research2007

Predicting tissue-specific enhancers in the human genome.

Len A Pennacchio, Gabriela G Loots, Marcelo A Nobrega, Ivan Ovcharenko

Abstract readComparative Study
In one paragraph

Article in Genome research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing 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

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

  1. Pooled it
  2. Article
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  6. Article
  7. RNA editing: Expanding the potential of RNA therapeutics.Molecular therapy : the journal of the American Society of Gene Therapy · 2023
    Review
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  19. In Vivo Hepatic Enhancer Elements in the Human ABCG2 Locus.Drug metabolism and disposition: the biological fate of chemicals · 2017
    Article
  20. Review

29 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

4 authors.

Len A PennacchioGenomics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Gabriela G Loots
Marcelo A Nobrega
Ivan Ovcharenko

Funding

Generation of an In Vivo Human Genome Transcriptional Enhancer DatasetR01HG003988 · NHGRI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI Len Alexander Pennacchio · 2006 to 2026
$24.1M
Comparative Genomic Resources for CV ResearchU01HL066681 · NHLBI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI RUBIN, EDWARD M · 2000 to 2007
$14.1M
Deciphering Principles of Regulatory GenomicsR01HG003963 · NHGRI · UNIVERSITY OF CALIF-LAWRNC LVRMR NAT LAB · PI LOOTS, GABRIELA G · 2007 to 2010
$1.1M
NHGRI NIH HHS HG003988NHGRI NIH HHS R01 HG003963NHGRI NIH HHS R01 HG003988NHLBI NIH HHS HL066681NHLBI NIH HHS U01 HL066681
6 · The paper itself

Abstract

Determining how transcriptional regulatory signals are encoded in vertebrate genomes is essential for understanding the origins of multicellular complexity; yet the genetic code of vertebrate gene regulation remains poorly understood. In an attempt to elucidate this code, we synergistically combined genome-wide gene-expression profiling, vertebrate genome comparisons, and transcription factor binding-site analysis to define sequence signatures characteristic of candidate tissue-specific enhancers in the human genome. We applied this strategy to microarray-based gene expression profiles from 79 human tissues and identified 7187 candidate enhancers that defined their flanking gene expression, the majority of which were located outside of known promoters. We cross-validated this method for its ability to de novo predict tissue-specific gene expression and confirmed its reliability in 57 of the 79 available human tissues, with an average precision in enhancer recognition ranging from 32% to 63% and a sensitivity of 47%. We used the sequence signatures identified by this approach to successfully assign tissue-specific predictions to approximately 328,000 human-mouse conserved noncoding elements in the human genome. By overlapping these genome-wide predictions with a data set of enhancers validated in vivo, in transgenic mice, we were able to confirm our results with a 28% sensitivity and 50% precision. These results indicate the power of combining complementary genomic data sets as an initial computational foray into a global view of tissue-specific gene regulation in vertebrates.

Indexed as

Enhancer Elements, GeneticGenome, HumanAnimalsFemaleGene Expression ProfilingHumansLiverMaleMuscle, SkeletalPregnancySpecies SpecificityTissue DistributionTranscription FactorsVertebratesTranscription Factors

Identifiers

PMID17210927
PMCPMC1781352

What OpenQuestion holds

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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.