Volume: 19, Issue: 4

Preview

From Reads to Insights: Integrative Pipelines for Biological Interpretation of ATAC-seq Data

Ya Cui, Jason Sheng Li, Wei Li

ATAC-seq被广泛用于测定全基因组范围内染色质的开放区域。它利用Tn5转座酶仅能在开放的染色质插入的特性,为染色质开放区域的测定提供了一种简单、快速、起始细胞量低的解决方案。近年来,ATAC-seq的数据积累成指数增长,一些国际大型项目如TCGA和CommonMind等甚至为大量人群样本测定了ATAC-seq数据。如何全面、深入分析ATAC-seq数据成了科研人员不得不面对的问题。然而,目前研究人员直接用并不完全适用的ChIP-seq或者DNase-seq数据的分析软件来对ATAC-seq数据进行分析,针对ATAC-seq数据的综合分析流程也还没有完全确定。在《GPB》杂志的最新一期中,Liu等和Qiu等分别开发了针对ATAC-seq数据的整合分析流程:AIAP和CoBRA,为研究人员对ATAC-seq数据的综合分析提供了参考。本文针对AIAP和CoBRA这两个ATAC-seq数据的整合分析流程进行了介绍。

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Research Article

SPA: A Quantitation Strategy for MS Data in Patient-derived Xenograft Models

Xi Cheng, Lili Qian, Bo Wang, Minjia Tan, Jing Li

With the development of mass spectrometry (MS)-based proteomics technologies, patient-derived xenograft (PDX), which is generated from the primary tumor of a patient, is widely used for the proteome-wide analysis of cancer mechanism and biomarker identification of a drug. However, the proteomics data interpretation is still challenging due to complex data deconvolution from the PDX sample that is a cross-species mixture of human cancerous tissues and immunodeficient mouse tissues. In this study, by using the lab-assembled mixture of human and mouse cells with different mixing ratios as a benchmark, we developed and evaluated a new method, SPA (shared peptide allocation), for protein quantitation by considering the unique and shared peptides of both species. The results showed that SPA could provide more convenient and accurate protein quantitation in human–mouse mixed samples. Further validation on a pair of gastric PDX samples (one bearing FGFR2 amplification while the other one not) showed that our new method not only significantly improved the overall protein identification, but also detected the differential phosphorylation of FGFR2 and its downstream mediators (such as RAS and ERK) exclusively. The tool pdxSPA is freely available at https://github.com/Li-Lab-Proteomics/pdxSPA.

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Method

RePhine: An Integrative Method for Identification of Drug Response-related Transcriptional Regulators

Xujun Wang, Zhengtao Zhang, Wenyi Qin, Shiyi Liu, Cong Liu, Georgi Z. Genchev, Lijian Hui, Hongyu Zhao, Hui Lu

Transcriptional regulators (TRs) participate in essential processes in cancer pathogenesis and are critical therapeutic targets. Identification of drug response-related TRs from cell line-based compound screening data is often challenging due to low mRNA abundance of TRs, protein modifications, and other confounders (CFs). In this study, we developed a regression-based pharmacogenomic and ChIP-seq data integration method (RePhine) to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and ChIP-seq data. RePhine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological discovery. In simulation data with added noises or CFs and in pharmacogenomic data, RePhine demonstrated an improved performance in comparison with three commonly used methods (including Pearson correlation analysis, logistic regression model, and gene set enrichment analysis). Utilizing RePhine and Cancer Cell Line Encyclopedia data, we observed that RePhine-derived TR signatures could effectively cluster drugs with different mechanisms of action. RePhine predicted that loss-of-function of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor PLX4720. Experimental validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 treatment. Our results support that RePhine is a useful tool for inferring drug response-related TRs and for potential therapeutic applications. The source code for RePhine is freely available at https://github.com/coexps/RePhine.

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