Evidence map›Paper›PMID 23531767›Full record

ArticleGenome research2013

Integrative analysis of C. elegans modENCODE ChIP-seq data sets to infer gene regulatory interactions.

Eric L Van Nostrand, Stuart K Kim

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed, 36 citations in OpenAlex.

  1. Transcriptomic profiling of the embryonicbioRxiv : the preprint server for biology · 2026
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  6. Nematode chromosomes.Genetics · 2022
    Review
  7. Article
  8. The regulatory landscape of neurite development inFrontiers in molecular neuroscience · 2022
    Review
  9. Article
  10. Article
  11. WormQTL2: an interactive platform for systems genetics in Caenorhabditis elegans.Database : the journal of biological databases and curation · 2020
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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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

2 authors at 1 institution in 1 country.

Eric L Van NostrandDepartment of Genetics and Department of Developmental Biology, Stanford University Medical Center, Stanford, California 94305, USA.
Stuart K Kim
Stanford Medicine · US

Funding

INSTITUTIONAL TRAINING GRANT IN GENOME SCIENCET32HG000044 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL P. SNYDER · 1995 to 2026
$32.2M
Enhancing and expanding the CGC Strain CollectionP40OD010440 · OD · UNIVERSITY OF MINNESOTA · PI Ann E. Rougvie · 2012 to 2026
$7.5M
Mechanisms of Aging in C. elegansR01AG025941 · NIA · STANFORD UNIVERSITY · PI DAVIS, RONALD WAYNE · 2007 to 2017
$3.1M
NHGRI NIH HHS T32 HG000044NIA NIH HHS R01 AG025941NIH HHS P40 OD010440
6 · The paper itself

Abstract

The C. elegans modENCODE Consortium has defined in vivo binding sites for a large array of transcription factors by ChIP-seq. In this article, we present examples that illustrate how this compendium of ChIP-seq data can drive biological insights not possible with analysis of individual factors. First, we analyze the number of independent factors bound to the same locus, termed transcription factor complexity, and find that low-complexity sites are more likely to respond to altered expression of a single bound transcription factor. Next, we show that comparison of binding sites for the same factor across developmental stages can reveal insight into the regulatory network of that factor, as we find that the transcription factor UNC-62 has distinct binding profiles at different stages due to distinct cofactor co-association as well as tissue-specific alternative splicing. Finally, we describe an approach to infer potential regulators of gene expression changes found in profiling experiments (such as DNA microarrays) by screening these altered genes to identify significant enrichment for targets of a transcription factor identified in ChIP-seq data sets. After confirming that this approach can correctly identify the upstream regulator on expression data sets for which the regulator was previously known, we applied this approach to identify novel candidate regulators of transcriptional changes with age. The analysis revealed nine candidate aging regulators, of which three were previously known to have a role in longevity. We experimentally showed that two of the new candidate aging regulators can extend lifespan when overexpressed, indicating that this approach can identify novel functional regulators of complex processes.

Indexed as

Binding SitesComputational BiologyGene Expression RegulationGene Regulatory NetworksAgingAnimalsCaenorhabditis elegansCaenorhabditis elegans ProteinsChromatin ImmunoprecipitationCluster AnalysisGene Expression ProfilingHigh-Throughput Nucleotide SequencingHomeodomain ProteinsNucleotide MotifsOrgan SpecificityPosition-Specific Scoring MatricesCaenorhabditis elegans ProteinsHomeodomain ProteinsTranscription FactorsUnc-62 protein, C elegans

Identifiers

PMID23531767
PMCPMC3668362
OpenAlexW1968794038

What OpenQuestion holds

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LicenceCC BY-NC
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.