Suppr超能文献

患者来源异种移植物的染色质构象捕获(Hi-C)测序:分析指南。

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

机构信息

Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA.

Department of Pathology, Virginia Commonwealth University, Richmond, VA 23284, USA.

出版信息

Gigascience. 2021 Apr 21;10(4). doi: 10.1093/gigascience/giab022.

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.

摘要

背景

对患者来源异种移植(PDX)小鼠模型进行测序可以研究在小鼠宿主中移植的人类肿瘤样本的分子机制。因此,对人类和小鼠的遗传物质进行测序。已经开发了几种方法来去除 RNA-seq 或外显子组测序 PDX 数据中的小鼠测序reads,并提高下游信号。然而,对于更近期的染色质构象捕获技术(Hi-C),小鼠reads 的影响仍未定义。

结果

我们使用具有 10%和 30%小鼠reads 的虚拟 PDX Hi-C 数据评估了去除小鼠reads 对 Hi-C 数据质量的影响。此外,我们使用不同的文库制备策略生成了 2 个实验性 PDX Hi-C 数据集。我们评估了 3 种比对策略(直接、Xenome、组合)和 3 种管道(Juicer、HiC-Pro、HiCExplorer)对 Hi-C 数据质量的影响。

结论

与直接比对策略相比,去除小鼠reads 对数据质量的影响很小。无论采用哪种小鼠reads 去除策略,Juicer 都能提取出更多有效的染色质相互作用。然而,管道的影响很小,而文库制备策略对所有质量指标的影响最大。总的来说,我们的研究为 PDX Hi-C 数据处理提供了全面的指导方针。

相似文献

3
Read Mapping for Hi-C Analysis.Hi-C 分析的读取映射。
Methods Mol Biol. 2025;2856:25-62. doi: 10.1007/978-1-0716-4136-1_3.

本文引用的文献

1
Refgenie: a reference genome resource manager.Refgenie:参考基因组资源管理器。
Gigascience. 2020 Feb 1;9(2). doi: 10.1093/gigascience/giz149.
2
The 3D Genome as a Target for Anticancer Therapy.作为抗癌治疗靶点的三维基因组
Trends Mol Med. 2020 Feb;26(2):141-149. doi: 10.1016/j.molmed.2019.09.011. Epub 2019 Oct 31.
7
Hi-C analysis: from data generation to integration.Hi-C分析:从数据生成到整合
Biophys Rev. 2019 Feb;11(1):67-78. doi: 10.1007/s12551-018-0489-1. Epub 2018 Dec 20.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验