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用于高通量筛选全基因组调控网络分析的PLATE-Seq技术

PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens.

作者信息

Bush Erin C, Ray Forest, Alvarez Mariano J, Realubit Ronald, Li Hai, Karan Charles, Califano Andrea, Sims Peter A

机构信息

Department of Systems Biology, Columbia University Medical Center, New York, NY, 10032, USA.

Sulzberger Columbia Genome Center, Columbia University Medical Center, New York, NY, 10032, USA.

出版信息

Nat Commun. 2017 Jul 24;8(1):105. doi: 10.1038/s41467-017-00136-z.

DOI:10.1038/s41467-017-00136-z
PMID:28740083
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5524642/
Abstract

Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low complexity rather than genome-wide assays. To address this limitation, we introduce pooled library amplification for transcriptome expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10- to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30 M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the connectivity map and library of integrated network-based cellular signatures.Despite the importance of pharmacological and functional genomic screens the readouts are of low complexity. Here the authors introduce PLATE-Seq, a low-cost genome-wide mRNA profiling method to complement high-throughput screening.

摘要

药理和功能基因组筛选在治疗靶点及相关药理抑制剂的发现和表征中发挥着重要作用。尽管这些筛选会影响数千种基因产物,但典型的读数基于低复杂性而非全基因组分析。为解决这一局限性,我们引入了用于转录组表达的混合文库扩增(PLATE-Seq),这是一种低成本的全基因组mRNA分析方法,专门设计用于补充高通量筛选分析。在逆转录过程中引入样本特异性条形码支持混合文库构建和低深度测序,其成本比传统RNA测序低10至20倍。使用基于网络的算法从PLATE-Seq数据推断蛋白质活性,其重现性与3000万读数测序相当。事实上,PLATE-Seq的重现性优于其他大规模扰动分析研究,如连通性图谱和基于网络的综合细胞特征文库。尽管药理和功能基因组筛选很重要,但其读数的复杂性较低。本文作者介绍了PLATE-Seq,一种低成本的全基因组mRNA分析方法,以补充高通量筛选。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/e7b3feaf9ebc/41467_2017_136_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/6b0e4b477c2a/41467_2017_136_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/6ae8e94d6099/41467_2017_136_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/e7b3feaf9ebc/41467_2017_136_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/6b0e4b477c2a/41467_2017_136_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/6ae8e94d6099/41467_2017_136_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ff/5524642/e7b3feaf9ebc/41467_2017_136_Fig3_HTML.jpg

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