Evidence map›Paper›PMID 32663510›Full record

ArticleMethods (San Diego, Calif.)2021

FreeHi-C spike-in simulations for benchmarking differential chromatin interaction detection.

Ye Zheng, Peigen Zhou, Sündüz Keleş

Open access · greenAbstract read
In one paragraph

Article in Methods (San Diego, Calif.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 50% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 1 country.

Ye ZhengBiostatistics, Bioinformatics and Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA. Electronic address: yzheng74@wisc.edu.
Peigen ZhouDepartment of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA. Electronic address: pzhou9@wisc.edu.
Sündüz KeleşDepartment of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI 53706, USA. Electronic address: keles@stat.wisc.edu.
University of Wisconsin–Madison · USFred Hutch Cancer Center · US

Funding

Statistical Methods for the Analysis of ChlP-chip DataR01HG003747 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI KELES, SUNDUZ · 2007 to 2024
$4.8M
Statistical Methods For Annotating Repetitive Genomic Regions Through ENCODE-deriU01HG007019 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI BRESNICK, EMERY H, DEWEY, COLIN NOEL · 2012 to 2015
$1.5M
Statistical methods for profiling long-range interactions of repetitive genomic regions with Hi-C dataR21HG009744 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI KELES, SUNDUZ · 2018 to 2019
$395k
NHGRI NIH HHS R01 HG003747NHGRI NIH HHS R21 HG009744NHGRI NIH HHS U01 HG007019
6 · The paper itself

Abstract

High-throughput genome-wide chromatin conformation capture assay (Hi-C) is routinely used to profile long-range genomic interactions and three-dimensional organization of genomes. A key application of Hi-C is the comparative analysis of genomic interactions across different time points, cellular conditions, or multiple stimuli. While operating characteristics of methods for Hi-C data processing such as normalization, pairwise interaction and higher-order organization detection have been relatively well studied, properties of methods for differential chromatin interaction detection are less investigated. We have recently developed FreeHi-C to enable data-driven non-parametric simulations from Hi-C experiments. Here, we extend FreeHi-C with a user/data-driven spike-in module to facilitate comparisons of differential chromatin interaction detection methods where the ground truth differential chromatin interactions are known under a wide variety of settings. We use FreeHi-C to benchmark four differential chromatin interaction detection methods, namely HiCcompare, multiHiCcompare, diffHic, and Selfish, using three comparative analysis settings with different sequencing depths and spike-in proportions. This comparison reveals distinguished performances in terms of the standard metrics such as the false discovery rate control, detection power, significance order, precision-recall curve, and receiver operating characteristic curve as well as overall genomic properties of the types of differential chromatin interactions detectable by each method. Furthermore, it highlights the lack of power for all methods in small replication settings.

Indexed as

SoftwareAnimalsChromatinChromosome MappingComputational BiologyComputer SimulationEpigenomicsHumansChromatinDifferential chromatin interactionsHi-CSimulationsSpike-ins

Identifiers

PMID32663510
PMCPMC7855360
OpenAlexW3041941942

What OpenQuestion holds

Textmetadata
LicenceTDM
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.