Evidence map›Paper›PMID 27716038›Full record

ArticleBMC bioinformatics2016

ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline.

Qian Qin, Shenglin Mei, Qiu Wu, Hanfei Sun, Lewyn Li, Len Taing, Sujun Chen, Fugen Li, Tao Liu, Chongzhi Zang and 7 more

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing 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

71 citing papers in PubMed.

  1. Article
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  8. Churros: a Docker-based pipeline for large-scale epigenomic analysis.DNA research : an international journal for rapid publication of reports on genes and genomes · 2024
    Article
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  12. ChEAP: ChIP-exo analysis pipeline and the investigation ofComputational and structural biotechnology journal · 2023
    Article
  13. Article
  14. Article
  15. Article
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11 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

17 authors.

Qian QinShanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.
Shenglin MeiShanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.
Qiu WuShanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.
Hanfei SunShanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.
Lewyn LiCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
Len TaingDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.
Sujun ChenShanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China.
Fugen LiCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
Tao LiuDepartment of Biochemistry, University at Buffalo, Buffalo, NY, USA.
Chongzhi ZangDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.
Han XuDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.
Yiwen ChenDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.
Clifford A MeyerDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, USA.
Yong ZhangDepartment of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China.
Myles BrownCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
Henry W LongCenter for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. henry_long@dfci.harvard.edu.
X Shirley LiuShanghai Key laboratory of tuberculosis, Shanghai Pulmonary Hospital, Shanghai, China. xsliu@jimmy.harvard.edu.

Funding

Computational Models for Mechanisms of Global Transcription RegulationR01HG004069 · NHGRI · DANA-FARBER CANCER INST · PI LIU, XIAOLE SHIRLEY · 2006 to 2013
$3.6M
Developing Informatics Technologies to Model Cancer Gene RegulationU01CA180980 · NCI · DANA-FARBER CANCER INST · PI LIU, XIAOLE SHIRLEY · 2013 to 2015
$1.1M
NCI NIH HHS U01 CA180980NHGRI NIH HHS R01 HG004069
6 · The paper itself

Abstract

backgroundTranscription factor binding, histone modification, and chromatin accessibility studies are important approaches to understanding the biology of gene regulation. ChIP-seq and DNase-seq have become the standard techniques for studying protein-DNA interactions and chromatin accessibility respectively, and comprehensive quality control (QC) and analysis tools are critical to extracting the most value from these assay types. Although many analysis and QC tools have been reported, few combine ChIP-seq and DNase-seq data analysis and quality control in a unified framework with a comprehensive and unbiased reference of data quality metrics.

resultsChiLin is a computational pipeline that automates the quality control and data analyses of ChIP-seq and DNase-seq data. It is developed using a flexible and modular software framework that can be easily extended and modified. ChiLin is ideal for batch processing of many datasets and is well suited for large collaborative projects involving ChIP-seq and DNase-seq from different designs. ChiLin generates comprehensive quality control reports that include comparisons with historical data derived from over 23,677 public ChIP-seq and DNase-seq samples (11,265 datasets) from eight literature-based classified categories. To the best of our knowledge, this atlas represents the most comprehensive ChIP-seq and DNase-seq related quality metric resource currently available. These historical metrics provide useful heuristic quality references for experiment across all commonly used assay types. Using representative datasets, we demonstrate the versatility of the pipeline by applying it to different assay types of ChIP-seq data. The pipeline software is available open source at https://github.com/cfce/chilin .

conclusionChiLin is a scalable and powerful tool to process large batches of ChIP-seq and DNase-seq datasets. The analysis output and quality metrics have been structured into user-friendly directories and reports. We have successfully compiled 23,677 profiles into a comprehensive quality atlas with fine classification for users.

Indexed as

Gene Expression RegulationQuality ControlSoftwareChromatin ImmunoprecipitationChromosome MappingDatabases, GeneticData Interpretation, StatisticalDeoxyribonucleasesHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNADeoxyribonucleasesAnalysis pipelineChIP-seqDNase-seqQuality atlas

Identifiers

PMID27716038
PMCPMC5048594

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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.