shRNA-seq data analysis with edgeR

Introduction

Pooled short hairpin RNA sequencing (shRNA-seq) screens couple RNA interference (RNAi) with second generation sequencing to enable researchers to elucidate gene function in an unbiased, high-throughput manner¹. Several recent high impact studies have exploited this technology to discover novel genes involved in processes including cell fate decisions of normal and cancer cells, and to generate genetic interaction maps in mammalian cells^2–4.

Pooled shRNA screening relies on the stable genomic integration (often by viral transduction) of expression cassettes that allow stable or inducible expression of shRNAs targeting specific genes in a large population of cells. Depending on the biological question of interest, typically two or more cell populations are compared either in the presence or absence of a selective pressure, or as a time-course before and after a selective pressure is applied. Gain of shRNA representation within a pool suggests that target gene knockdown confers some sort of advantage to a cell. Similarly, genes whose knockdown is disadvantageous may be identified through loss of shRNA representation. Screening requires a library of shRNA constructs in a lentiviral or retroviral vector backbone that is used to generate a pool of virus for transducing cells of interest. The relative abundance of these shRNAs in transduced cells is then quantified by PCR amplification of proviral integrants from genomic DNA using primers designed to amplify all shRNA cassettes equally, followed by second-generation amplicon sequencing (Figure 1A). Sample-specific primer indexing allows many different conditions to be analyzed in parallel.

Figure 1. Summary of the raw data, workflow and diagnostic plots from edgeR.

(A) Structure of the amplicons sequenced in a typical shRNA-seq screen. Each one contains sample and hairpin specific sequences at predetermined locations. After sequencing, the raw data is available in a fastq file. (B) The main steps and functions used in an analysis of shRNA-seq screen data in edgeR are shown. (C) Example of a multidimensional scaling (MDS) plot showing the relationships between replicate dimethyl sulfoxide (DMSO) and Nutlin treated samples (data from Sullivan et al. (2012)³). MDS plots provide a quick display of overall variability in the screen and can highlight inconsistent samples. (D) Plot of log₂-fold-change versus hairpin abundance (log₂CPM) for the same data. Hairpins with a false discovery rate < 0.05 from an exact test analysis in edgeR (highlighted in red) may be prioritized for further validation.

As the popularity of this approach grows, there is a need to develop suitable analysis pipelines to handle the large volumes of raw data that each screen generates. The major steps in an analysis involve processing the raw sequence reads, assessing the data quality and determining representational differences in the screen in a statistically rigorous way.

Two pipelines are currently available for this task. The shALIGN program⁵ is a custom Perl script that trims the sequence reads to the pre-defined base positions and then matches these to a library of hairpin sequences. Mismatch bases are permitted, and any ambiguous matches are ignored from the final hairpin count. Statistical analysis of the data is then performed using the shRNAseq R package⁵, which calculates log-ratios of the counts from each screen replicate, normalizes these values and ranks hairpins by their median, mean or t-statistic. Another solution is the BiNGS!SL-seq program⁶ that uses Bowtie to perform sequence mapping followed by statistical analysis in edgeR⁷.

In this article, we describe a complete analysis solution for shRNA-seq screens accessible from within the edgeR package available from Bioconductor⁸.

Implementation

A summary of the main steps in a typical shRNA-seq analysis alongside the functions in edgeR that perform each task is given in Figure 1B.

Case studies and further extensions

We provide example data sets and a complete analysis script that demonstrate how to use the edgeR package to prioritize data from four different pooled shRNA-seq screens¹⁴. These examples were chosen to showcase edgeR’s ability to deal with experiments of varying size (from tens to thousands of hairpins) and complexity, from two-group situations, to settings with four groups, or a time-course design, where a GLM with a slope and intercept term is most appropriate. We have also developed a Galaxy tool^15–17 that implements this workflow as a point-and-click application to improve accessibility for researchers who are unfamiliar with the R programming environment (Figure 2).

Figure 2. Screenshots of the Galaxy tool for shRNA-seq screen analysis powered by edgeR.

(A) From the main screen, the user selects the appropriate input files and analysis options. (B) The results of an analysis are summarized in an HTML page that includes various diagnostic plots. (C) Output also includes a table of ranked results at the hairpin and gene-level (where appropriate) as well as barcode plots (D) that highlight the ranks of hairpins targeting a specific gene relative to all other hairpins in the data set.

Discussion

Although the major functionality of edgeR has been developed with RNA-seq data in mind, the analysis of numerous in-house data sets¹⁴ and the results of others³ have demonstrated its utility for count data derived from shRNA-seq screens. edgeR provides users with a unique tool for the analysis of data from this emerging application of second generation sequencing technology, that is capable of handling both the biological variability and experimental complexity inherent in these screens. Provision of a Galaxy module puts these powerful statistical methods within reach of experimentalists. Future work will be focused on the use of a suitable control data set to compare this analysis pipeline with other approaches such as shRNAseq⁵. We anticipate that the approach for differential representation analysis described in this paper will also be useful in the analysis of short-guided RNA-seq (sgRNA-seq) knockout screens as facilitated by the clustered regularly interspaced short palindromic repeats-Cas9 (CRISPR-Cas9) system^18,19.

Software availability

edgeR is an R⁹ package distributed as part of the Bioconductor project⁸ (http://www.bioconductor.org). The Galaxy tool that implements this workflow is available from http://toolshed.g2.bx.psu.edu/view/shians/shrnaseq.