<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Dozmorov MG</submitter><funding>NCATS NIH HHS</funding><funding>Komen Foundation</funding><funding>NIAAA NIH HHS</funding><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><funding>National Computational Infrastructure</funding><pagination>giab022</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8058593</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>10(4)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>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.&lt;h4>Results&lt;/h4>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.&lt;h4>Conclusions&lt;/h4>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.</pubmed_abstract><journal>GigaScience</journal><pubmed_title>Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines.</pubmed_title><pmcid>PMC8058593</pmcid><funding_grant_id>R01 CA246182</funding_grant_id><funding_grant_id>1R01CA246182–01A1</funding_grant_id><funding_grant_id>CCR19608826</funding_grant_id><funding_grant_id>UL1 TR002649</funding_grant_id><funding_grant_id>P50 AA022537</funding_grant_id><pubmed_authors>Reed J</pubmed_authors><pubmed_authors>Dozmorov MG</pubmed_authors><pubmed_authors>Harrell JC</pubmed_authors><pubmed_authors>Olex AL</pubmed_authors><pubmed_authors>Tyc KM</pubmed_authors><pubmed_authors>Boyd DC</pubmed_authors><pubmed_authors>Sheffield NC</pubmed_authors></additional><is_claimable>false</is_claimable><name>Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines.</name><description>&lt;h4>Background&lt;/h4>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.&lt;h4>Results&lt;/h4>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.&lt;h4>Conclusions&lt;/h4>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.</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Apr</publication><modification>2026-05-02T22:46:10.712Z</modification><creation>2025-04-04T22:14:43.583Z</creation></dates><accession>S-EPMC8058593</accession><cross_references><pubmed>33880552</pubmed><doi>10.1093/gigascience/giab022</doi></cross_references></HashMap>