Evidence map›Paper›PMID 22955983›Full record

ArticleGenome research2012

Predicting cell-type-specific gene expression from regions of open chromatin.

Anirudh Natarajan, Galip Gürkan Yardimci, Nathan C Sheffield, Gregory E Crawford, Uwe Ohler

Open access · hybridAbstract read
In one paragraph

Article in Genome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers.

0numbers the graph read from it
0cells of the map it votes in
155citing 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

155 citing papers in PubMed, 275 citations in OpenAlex.

  1. Article
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  9. Leveraging Machine Learning for Predicting Circadian Transcription in mRNAs and lncRNAs.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024
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  16. Article
  17. PlantCADB: A Comprehensive Plant Chromatin Accessibility Database.Genomics, proteomics & bioinformatics · 2023
    Article
  18. Article
  19. Article
  20. Article

95 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

5 authors at 1 institution in 1 country.

Anirudh NatarajanProgram in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27708, USA.
Galip Gürkan Yardimci
Nathan C Sheffield
Gregory E Crawford
Uwe Ohler
Duke University · US

Funding

Comprehensive Identification of Active Functional Elements in Human ChromatinU54HG004563 · NHGRI · DUKE UNIVERSITY · PI CRAWFORD, GREGORY E · 2007 to 2011
$9.7M
Modeling the structure & evolution of regulatory regions in eukaryotic gemomesR01HG004065 · NHGRI · DUKE UNIVERSITY · PI OHLER, UWE · 2007 to 2011
$1.9M
NHGRI NIH HHS R01 HG004065NHGRI NIH HHS U54 HG004563
6 · The paper itself

Abstract

Complex patterns of cell-type-specific gene expression are thought to be achieved by combinatorial binding of transcription factors (TFs) to sequence elements in regulatory regions. Predicting cell-type-specific expression in mammals has been hindered by the oftentimes unknown location of distal regulatory regions. To alleviate this bottleneck, we used DNase-seq data from 19 diverse human cell types to identify proximal and distal regulatory elements at genome-wide scale. Matched expression data allowed us to separate genes into classes of cell-type-specific up-regulated, down-regulated, and constitutively expressed genes. CG dinucleotide content and DNA accessibility in the promoters of these three classes of genes displayed substantial differences, highlighting the importance of including these aspects in modeling gene expression. We associated DNase I hypersensitive sites (DHSs) with genes, and trained classifiers for different expression patterns. TF sequence motif matches in DHSs provided a strong performance improvement in predicting gene expression over the typical baseline approach of using proximal promoter sequences. In particular, we achieved competitive performance when discriminating up-regulated genes from different cell types or genes up- and down-regulated under the same conditions. We identified previously known and new candidate cell-type-specific regulators. The models generated testable predictions of activating or repressive functions of regulators. DNase I footprints for these regulators were indicative of their direct binding to DNA. In summary, we successfully used information of open chromatin obtained by a single assay, DNase-seq, to address the problem of predicting cell-type-specific gene expression in mammalian organisms directly from regulatory sequence.

Indexed as

Chromatin Assembly and DisassemblyGene Expression RegulationRegulatory Sequences, Nucleic AcidBase CompositionBinding SitesCell LineCluster AnalysisDeoxyribonuclease IDNA FootprintingGene Expression ProfilingGenomeHumansOrgan SpecificityPromoter Regions, GeneticProtein BindingTranscription FactorsDeoxyribonuclease ITranscription Factors

Identifiers

PMID22955983
PMCPMC3431488
OpenAlexW2112741367

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.