生物实验室 - 癌症 - 药物基因组学：肿瘤异质性以及云端肿瘤多组学数据的药物基因组学分析

BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud.

作者信息

Park Sungjoon, Lee Dohoon, Kim Youngkuk, Lim Sangsoo, Chae Heejoon, Kim Sun

机构信息

Department of Computer Science and Engineering, Seoul National University, Seoul 08840, Republic of Korea.

Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08840, Republic of Korea.

出版信息

Bioinformatics. 2021 Dec 22;38(1):275-277. doi: 10.1093/bioinformatics/btab478.

DOI:10.1093/bioinformatics/btab478

PMID:34185062

Abstract

MOTIVATION

Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline.

RESULTS

We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools.

AVAILABILITY AND IMPLEMENTATION

http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

多年来，分子生物学中的多组学数据迅速积累。此类数据包含医学研究和药物发现的宝贵信息。不幸的是，由于数据量巨大且分析流程复杂，对于大多数小型研究实验室而言，医学和药物发现中的数据驱动研究具有挑战性。

结果

我们展示了BioVLAB - 癌症 - 药物基因组学，这是一个生物信息学系统，可促进对乳腺癌多组学数据的分析，以在亚马逊网络服务上分析和研究肿瘤内异质性和药物基因组学。我们的系统将多组学数据作为输入，根据TCGA数据进行肿瘤异质性分析，并使用DNA甲基化谱将肿瘤基因表达与CCLE中的细胞系数据进行解卷积和匹配。我们相信我们的系统可以帮助小型研究实验室进行肿瘤多组学分析，而无需担心计算基础设施以及数据库和工具的维护。