Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines

This article has been Reviewed by the following groups

Read the full article

Abstract

Background

Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined.

Results

We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality.

Conclusions

Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

Article activity feed

  1. Now published in GigaScience doi: 10.1093/gigascience/giab022

    Mikhail G. Dozmorov 2Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA3Department of Pathology, Virginia Commonwealth University, Richmond, VA, 23284, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Mikhail G. DozmorovFor correspondence: Mikhail.Dozmorov@vcuhealth.org Joshua.Harrell@vcuhealth.orgKatarzyna M. Tyc 4VCU Massey Cancer Center, Richmond, VA, 23298, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Katarzyna M. TycNathan C. Sheffield 5Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Nathan C. SheffieldDavid C. Boyd 3Department of Pathology, Virginia Commonwealth University, Richmond, VA, 23284, USA6Integrative Life Sciences Doctoral Program, Virginia Commonwealth University, Richmond, VA, 23298, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for David C. BoydAmy L. Olex 7C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, 23298, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Amy L. OlexJason Reed 4VCU Massey Cancer Center, Richmond, VA, 23298, USA8Department of Physics, Virginia Commonwealth University, Richmond, VA, 23220, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Jason ReedJ. Chuck Harrell 3Department of Pathology, Virginia Commonwealth University, Richmond, VA, 23284, USAFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for J. Chuck HarrellFor correspondence: Mikhail.Dozmorov@vcuhealth.org Joshua.Harrell@vcuhealth.org

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giab022 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102709 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102710