Unit

UNIT 4.13 RASL-seq for Massively Parallel and Quantitative Analysis of Gene Expression

  1. Hairi Li,
  2. Jinsong Qiu,
  3. Xiang-Dong Fu

Published Online: 1 APR 2012

DOI: 10.1002/0471142727.mb0413s98

Current Protocols in Molecular Biology

Current Protocols in Molecular Biology

How to Cite

Li, H., Qiu, J. and Fu, X.-D. 2012. RASL-seq for Massively Parallel and Quantitative Analysis of Gene Expression. Current Protocols in Molecular Biology. 98:II:4.13:4.13.1–4.13.9.

Author Information

  1. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California

Publication History

  1. Published Online: 1 APR 2012
  2. Published Print: APR 2012

Abstract

Large-scale, quantitative analysis of gene expression can be accomplished by microarray or RNA-seq analysis. While these methods are applicable to genome-wide analysis, it is often desirable to quantify expression of a more limited set of genes in hundreds, thousands, or even tens of thousands of biological samples. For example, some studies may require monitoring a sizable panel of key genes under many different experimental conditions, during development, or following treatment with a large library of small molecules, for which current genome-wide methods are either inefficient or cost-prohibitive. This unit presents a method that permits quantitative profiling of several hundred selected genes in a large number of samples by coupling RNA-mediated oligonucleotide Annealing, Selection, and Ligation with Next-Gen sequencing (RASL-seq). The method even allows direct analysis of RNA levels in cell lysates and is also adaptable to full automation, making it ideal for large-scale analysis of multiple biological pathways or regulatory gene networks in the context of systematic genetic or chemical genetic perturbations. Curr. Protoc. Mol. Biol. 98:4.13.1-4.13.9. © 2012 by John Wiley & Sons, Inc.

Keywords:

  • gene expression;
  • RNA-mediated oligonucleotide;
  • annealing;
  • selection;
  • RASL-seq;
  • bar-coding strategies;
  • high-throughput screening