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

Author

Listed:
  • Erin C. Bush

    (Columbia University Medical Center
    Columbia University Medical Center)

  • Forest Ray

    (Columbia University Medical Center)

  • Mariano J. Alvarez

    (Columbia University Medical Center
    DarwinHealth Inc.)

  • Ronald Realubit

    (Columbia University Medical Center
    Columbia University Medical Center)

  • Hai Li

    (Columbia University Medical Center
    Columbia University Medical Center)

  • Charles Karan

    (Columbia University Medical Center
    Columbia University Medical Center)

  • Andrea Califano

    (Columbia University Medical Center
    Columbia University Medical Center
    Columbia University Medical Center
    Columbia University Medical Center)

  • Peter A. Sims

    (Columbia University Medical Center
    Columbia University Medical Center
    Columbia University Medical Center)

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.

Suggested Citation

  • Erin C. Bush & Forest Ray & Mariano J. Alvarez & Ronald Realubit & Hai Li & Charles Karan & Andrea Califano & Peter A. Sims, 2017. "PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens," Nature Communications, Nature, vol. 8(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00136-z
    DOI: 10.1038/s41467-017-00136-z
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    Cited by:

    1. Marcin Pilarczyk & Mehdi Fazel-Najafabadi & Michal Kouril & Behrouz Shamsaei & Juozas Vasiliauskas & Wen Niu & Naim Mahi & Lixia Zhang & Nicholas A. Clark & Yan Ren & Shana White & Rashid Karim & Huan, 2022. "Connecting omics signatures and revealing biological mechanisms with iLINCS," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Osama Al-Dalahmah & Michael G. Argenziano & Adithya Kannan & Aayushi Mahajan & Julia Furnari & Fahad Paryani & Deborah Boyett & Akshay Save & Nelson Humala & Fatima Khan & Juncheng Li & Hong Lu & Yu S, 2023. "Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic and targetable tissue states," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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