期刊
GENOME BIOLOGY
卷 20, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-019-1658-7
关键词
-
资金
- NIH [U41HG007000, U24HG009446, R24 DK106766, U41 HG006620, DP2OD022870, U24HG009397, R01ES025009 02S1, T32 GM102057, R01GM109453]
- Howard Hughes Medical Institute International Student Research Fellowship
- Gabilan Stanford Graduate Fellowship award
- Huck Graduate Research Innovation Grant
- [DK107980]
- [HG004592]
- [HG003143]
BackgroundHi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study.ResultsUsing real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector, and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established measures, such as the ratio of intra- to interchromosomal interactions, and novel ones, such as QuASAR-QC, to identify low-quality experiments.ConclusionsIn this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.
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