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Related Experiment Video

Updated: Jan 30, 2026

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications

Published on: April 14, 2015

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments.

Scott W Simpkins¹, Raamesh Deshpande², Justin Nelson¹

¹Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA.

Nature Protocols

|January 13, 2019

Summary

This summary is machine-generated.

This study introduces BEAN-counter, a Python software pipeline for analyzing high-throughput fitness-based profiling data from pooled mutant collections. It efficiently processes large datasets from genetic and chemical-genetic interaction experiments for gene and chemical function discovery.

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Area of Science:

Genomics and Bioinformatics
Systems Biology
Chemical Biology

Background:

Genome-wide mutant collections facilitate high-throughput functional characterization of genes and chemicals.
Increasing experimental throughput necessitates advanced tools for analyzing large-scale sequencing data.
Genetic and chemical-genetic interaction experiments are crucial for understanding gene and chemical function.

Purpose of the Study:

To describe the application of the BEAN-counter software pipeline for high-throughput fitness-based profiling of pooled mutant collections.
To provide a protocol for analyzing large-volume, high-dimensional quantitative data from sequencing experiments.
To offer recommendations for designing large-scale, multiplexed barcode sequencing experiments.

Main Methods:

Utilized the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for data analysis.
Applied the approach to fitness-based profiling of pooled mutant yeast collections.
Demonstrated the pipeline using a representative subset of data from a large chemical-genetic interaction screen.

Main Results:

The BEAN-counter pipeline successfully processed data from mutant collections of yeast, Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis.
The protocol was used to score approximately 4 million chemical-by-mutant combinations in a large-scale chemical-genetic interaction screen.
BEAN-counter is an open-source Python software, freely available for academic use, designed for command-line proficiency.

Conclusions:

BEAN-counter provides an effective solution for analyzing high-throughput, multiplexed barcode sequencing data.
The software and protocol enable high-dimensional, quantitative characterization of gene and chemical function.
This approach is valuable for researchers conducting large-scale genetic and chemical-genetic interaction studies.