Evidence map›Paper›PMID 24475069›Full record

ArticlePloS one2014

A new exhaustive method and strategy for finding motifs in ChIP-enriched regions.

Caiyan Jia, Matthew B Carson, Yang Wang, Youfang Lin, Hui Lu

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. CHD7 and SOX2 act in a common gene regulatory network during mammalian semicircular canal and cochlear development.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Article
  6. MicrosatNavigator: exploring nonrandom distribution and lineage-specificity of microsatellite repeat motifs on vertebrate sex chromosomes across 186 whole genomes.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2023
    Article
  7. Article
  8. A survey on deep learning in DNA/RNA motif mining.Briefings in bioinformatics · 2021
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Performance evaluation for MOTIFSIM.Biological procedures online · 2018
    Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Review of Different Sequence Motif Finding Algorithms.Avicenna journal of medical biotechnology
    Review
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.

Caiyan JiaSchool of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, China ; Department of Bioengineering/Bioinformatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
Matthew B CarsonCenter for Healthcare Studies, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America ; Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
Yang WangSchool of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, China.
Youfang LinSchool of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, China.
Hui LuDepartment of Bioengineering/Bioinformatics, University of Illinois at Chicago, Chicago, Illinois, United States of America ; Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai JiaoTong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ChIP-seq, which combines chromatin immunoprecipitation (ChIP) with next-generation parallel sequencing, allows for the genome-wide identification of protein-DNA interactions. This technology poses new challenges for the development of novel motif-finding algorithms and methods for determining exact protein-DNA binding sites from ChIP-enriched sequencing data. State-of-the-art heuristic, exhaustive search algorithms have limited application for the identification of short (l, d) motifs (l ≤ 10, d ≤ 2) contained in ChIP-enriched regions. In this work we have developed a more powerful exhaustive method (FMotif) for finding long (l, d) motifs in DNA sequences. In conjunction with our method, we have adopted a simple ChIP-enriched sampling strategy for finding these motifs in large-scale ChIP-enriched regions. Empirical studies on synthetic samples and applications using several ChIP data sets including 16 TF (transcription factor) ChIP-seq data sets and five TF ChIP-exo data sets have demonstrated that our proposed method is capable of finding these motifs with high efficiency and accuracy. The source code for FMotif is available at http://211.71.76.45/FMotif/.

Indexed as

Binding SitesChromatin ImmunoprecipitationHigh-Throughput Nucleotide SequencingNucleotide MotifsAlgorithmsAnimalsComputational BiologyDNA-Binding ProteinsEmbryonic Stem CellsMicePosition-Specific Scoring MatricesSensitivity and SpecificityTranscription FactorsDNA-Binding ProteinsTranscription Factors

Identifiers

PMID24475069
PMCPMC3901781

What OpenQuestion holds

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Registered trials

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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.