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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: May 6, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Design and analysis of Bar-seq experiments.

David G Robinson¹, Wei Chen, John D Storey

¹Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544.

G3 (Bethesda, Md.)

|November 7, 2013

Summary

This summary is machine-generated.

This study optimizes pooled genetic screening using Bar-seq analysis. Biological replicates and sufficient read depth are crucial for accurate results in high-throughput DNA sequencing experiments.

Keywords:

Bar-seq Sacchromyces cerevisiae functional genomics galactose yeast

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Area of Science:

Genetics
Genomics
Molecular Biology

Background:

High-throughput quantitative DNA sequencing allows for parallel phenotyping of thousands of mutants.
Optimal experimental design and analytical methods for these pooled screens are not well-established.

Purpose of the Study:

To systematically evaluate the impact of experimental design parameters and sequence read depth on Bar-seq analysis.
To develop computational methods for accurate effect size estimation and statistical significance testing in pooled screens.

Main Methods:

Utilized Bar-seq analysis on the Saccharomyces cerevisiae yeast deletion library.
Adapted RNA-seq analysis methods for computational analysis of DNA sequencing data.
Simulated variations in experimental designs and subsampled sequence reads to assess parameter effects.

Main Results:

Biological replicates are essential for statistical power in Bar-seq data; technical replicates offer less value.
With four-fold biological replication, 6 million reads per condition provide 96% power to detect two-fold changes at a 5% false discovery rate.
Developed guidelines for experimental design and computational analysis enabling large-scale pooled screens.

Conclusions:

Established best practices for experimental design and computational analysis in high-throughput pooled DNA sequencing screens.
The findings enhance the efficiency and statistical rigor of methods like Bar-seq and Tn-seq.
Facilitates the study of large mutant collections across numerous conditions within a single sequencing run.