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

Abstract

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of these experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed that can handle the large volume of data produced. Here we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from S. pombe, E. coli, and Z. mobilis mutant collections. We also provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined in this protocol was used to score interactions for approximately 4 million chemical-by-mutant combinations in our recently-published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of the data on which to demonstrate the analysis in this protocol. BEAN-counter is open-source, written in Python, and freely available for academic use at https://github.com/csbio/BEAN-counter. Users should be proficient at the command line, while advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those in the community interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

Integrating yeast chemical genomics and mammalian cell pathway analysis

“…The relative abundances of barcoded mutants after compound treatment were quantified using amplicon sequencing. Chemicalgenetic interaction z-scores for enrichment or depletion in the presence of the compound relative to the DMSO control were generated from sequencing data using the BEAN-counter software pipeline (https://www.github.com/csbio/BEAN-counter) [18,20]. The screens were performed in four batches: each batch was processed independently using BEAN-counter.…”

Jerveratrum-Type Steroidal Alkaloids Inhibit β-1,6-Glucan Biosynthesis in Fungal Cell Walls

“…Barcodes were sequenced on an Illumina MiSeq using MiSeq reagent kit version 3 (150 cycles; Illumina, Inc., San Diego, CA, USA). The barcode counts detected for each deletion mutant were quantified using BEAN-counter software to generate fitness-based chemical-genetic interaction scores ( 58 ). To compare multiple chemical-genomic profiles, we employed chemical-genomic profiles for jervine (12.5 μg/ml) and D75 (6.25 μM) as representatives (Table S1).…”

Evaluation ofSaccharomyces cerevisiaeWine Yeast Competitive Fitness in Enologically Relevant Environments by Barcode Sequencing

“…However, the development of parallel DNA sequencing technology has made possible competitive fitness profiling of mixed populations across multiple samples (Smith et al 2010). Furthermore, the development of specific analytical approaches (Robinson et al 2014;Simpkins et al 2019) and novel methods of barcode introduction (Roy et al 2018) has seen several studies using mixed populations to explore the relationship between yeast and environment (Cubillos et al 2009;Han et al 2010;Gresham et al 2011;Sideri et al 2014;Payen et al 2016;Piotrowski et al 2017;Maclean et al 2017).…”