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PLATE-Seq for Genome-wide Regulatory Network Analysis of High-throughput Screens

Overview
Journal Nat Commun
Specialty Biology
Date 2017 Jul 26
PMID 28740083
Citations 60
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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.

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References
1.
Aytes A, Mitrofanova A, Lefebvre C, Alvarez M, Castillo-Martin M, Zheng T . Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell. 2014; 25(5):638-651. PMC: 4051317. DOI: 10.1016/j.ccr.2014.03.017. View

2.
Peck D, Crawford E, Ross K, Stegmaier K, Golub T, Lamb J . A method for high-throughput gene expression signature analysis. Genome Biol. 2006; 7(7):R61. PMC: 1779561. DOI: 10.1186/gb-2006-7-7-r61. View

3.
Hashimshony T, Wagner F, Sher N, Yanai I . CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012; 2(3):666-73. DOI: 10.1016/j.celrep.2012.08.003. View

4.
Bisikirska B, Bansal M, Shen Y, Teruya-Feldstein J, Chaganti R, Califano A . Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression. Cancer Res. 2015; 76(3):664-74. PMC: 4738055. DOI: 10.1158/0008-5472.CAN-15-0828. View

5.
Jaitin D, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I . Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014; 343(6172):776-9. PMC: 4412462. DOI: 10.1126/science.1247651. View