Evidence map›Paper›PMID 24688750›Full record

ArticleComputational and structural biotechnology journal2014

Leveraging biological replicates to improve analysis in ChIP-seq experiments.

Yajie Yang, Justin Fear, Jianhong Hu, Irina Haecker, Lei Zhou, Rolf Renne, David Bloom, Lauren M McIntyre

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Guideline
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  15. Sex-biased expression is associated with chromatin state inbioRxiv : the preprint server for biology · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Bioinformatics Methods for ChIP-seq Histone Analysis.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
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

8 authors.

Yajie YangDepartment of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida, USA ; UF Genetics Institute, University of Florida, Gainesville, Florida, USA.
Justin FearDepartment of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida, USA ; UF Genetics Institute, University of Florida, Gainesville, Florida, USA.
Jianhong HuHuman Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA.
Irina HaeckerDepartment of Applied Entomology, University of Giessen, Giessen, Germany.
Lei ZhouDepartment of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida, USA.
Rolf RenneDepartment of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida, USA ; UF Genetics Institute, University of Florida, Gainesville, Florida, USA ; UF Shands Cancer Center, University of Florida, Gainesville, Florida, USA.
David BloomDepartment of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida, USA.
Lauren M McIntyreDepartment of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida, USA ; UF Genetics Institute, University of Florida, Gainesville, Florida, USA.

Funding

Molecular Genetics of HSV ReactivationR01AI048633 · NIAID · UNIVERSITY OF FLORIDA · PI BLOOM, DAVID C. · 2001 to 2023
$7.2M
LANA AND CELLULAR GENE EXPRESSION IN KAPOSIS SARCOMAR01CA088763 · NCI · UNIVERSITY OF FLORIDA · PI RENNE, ROLF F · 2000 to 2015
$3.2M
Quantitative Comparisons between genotypes and model speciesR01GM102227 · NIGMS · UNIVERSITY OF FLORIDA · PI MCINTYRE, LAUREN M. · 2012 to 2015
$1.2M
NCI NIH HHS R01 CA088763NIAID NIH HHS R01 AI048633NIGMS NIH HHS R01 GM102227
6 · The paper itself

Abstract

ChIP-seq experiments identify genome-wide profiles of DNA-binding molecules including transcription factors, enzymes and epigenetic marks. Biological replicates are critical for reliable site discovery and are required for the deposition of data in the ENCODE and modENCODE projects. While early reports suggested two replicates were sufficient, the widespread application of the technique has led to emerging consensus that the technique is noisy and that increasing replication may be worthwhile. Additional biological replicates also allow for quantitative assessment of differences between conditions. To date it has remained controversial about how to confirm peak identification and to determine signal strength across biological replicates, particularly when the number of replicates is greater than two. Using objective metrics, we evaluate the consistency of biological replicates in ChIP-seq experiments with more than two replicates. We compare several approaches for binding site determination, including two popular but disparate peak callers, CisGenome and MACS2. Here we propose read coverage as a quantitative measurement of signal strength for estimating sample concordance. Determining binding based on genomic features, such as promoters, is also examined. We find that increasing the number of biological replicates increases the reliability of peak identification. Critically, binding sites with strong biological evidence may be missed if researchers rely on only two biological replicates. When more than two replicates are performed, a simple majority rule (>50% of samples identify a peak) identifies peaks more reliably in all biological replicates than the absolute concordance of peak identification between any two replicates, further demonstrating the utility of increasing replicate numbers in ChIP-seq experiments.

Indexed as

biological replicatesChIP-seqpeak identification

Identifiers

PMID24688750
PMCPMC3962196

What OpenQuestion holds

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LicenceCC BY
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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.