Evidence map›Paper›PMID 30943272›Full record

ArticlePloS one2019

Identification of factors associated with duplicate rate in ChIP-seq data.

Shulan Tian, Shuxia Peng, Michael Kalmbach, Krutika S Gaonkar, Aditya Bhagwate, Wei Ding, Jeanette Eckel-Passow, Huihuang Yan, Susan L Slager

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Bioinformatics Methods for ChIP-seq Histone Analysis.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  5. 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

9 authors.

Shulan TianDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
Shuxia PengDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
Michael KalmbachDivision of Research and Education Support Systems, Department of Information Technology, Mayo Clinic, Rochester, Minnesota, United States of America.
Krutika S GaonkarDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
Aditya BhagwateDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
Wei DingDivision of Hematology, Mayo Clinic, Rochester, Minnesota, United States of America.
Jeanette Eckel-PassowDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.
Huihuang YanDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.ORCID 0000-0003-0756-2922
Susan L SlagerDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.

Funding

Mayo Clinic Center for Translational Science ActivitiesUL1TR000135 · NCATS · MAYO CLINIC ROCHESTER · PI KHOSLA, SUNDEEP · 2012 to 2015
$41.2M
Genetic Epidemiology of Chronic Lymphocytic LeukemiaU01CA118444 · NCI · MAYO CLINIC ROCHESTER · PI SLAGER, SUSAN L · 2006 to 2015
$7.1M
NCATS NIH HHS UL1 TR000135NCI NIH HHS U01 CA118444
6 · The paper itself

Abstract

Chromatin immunoprecipitation and sequencing (ChIP-seq) has been widely used to map DNA-binding proteins, histone proteins and their modifications. ChIP-seq data contains redundant reads termed duplicates, referring to those mapping to the same genomic location and strand. There are two main sources of duplicates: polymerase chain reaction (PCR) duplicates and natural duplicates. Unlike natural duplicates that represent true signals from sequencing of independent DNA templates, PCR duplicates are artifacts originating from sequencing of identical copies amplified from the same DNA template. In analysis, duplicates are removed from peak calling and signal quantification. Nevertheless, a significant portion of the duplicates is believed to represent true signals. Obviously, removing all duplicates will underestimate the signal level in peaks and impact the identification of signal changes across samples. Therefore, an in-depth evaluation of the impact from duplicate removal is needed. Using eight public ChIP-seq datasets from three narrow-peak and two broad-peak marks, we tried to understand the distribution of duplicates in the genome, the extent by which duplicate removal impacts peak calling and signal estimation, and the factors associated with duplicate level in peaks. The three PCR-free histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq data had about 40% duplicates and 97% of them were within peaks. For the other datasets generated with PCR amplification of ChIP DNA, as expected, the narrow-peak marks have a much higher proportion of duplicates than the broad-peak marks. We found that duplicates are enriched in peaks and largely represent true signals, more conspicuous in those with high confidence. Furthermore, duplicate level in peaks is strongly correlated with the target enrichment level estimated using nonredundant reads, which provides the basis to properly allocate duplicates between noise and signal. Our analysis supports the feasibility of retaining the portion of signal duplicates into downstream analysis, thus alleviating the limitation of complete deduplication.

Indexed as

Cell Line, TumorChromatin Immunoprecipitation SequencingData AnalysisDatasets as TopicHeLa CellsHumansMCF-7 CellsPolymerase Chain ReactionReproducibility of Results

Identifiers

PMID30943272
PMCPMC6447195

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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.