Pooled short hairpin RNA sequencing (shRNA-seq) screens are becoming increasingly popular in functional genomics research, and there is a need to establish optimal analysis tools to handle such data. Our open-source shRNA processing pipeline in edgeR provides a complete analysis solution for shRNA-seq screen data, that begins with the raw sequence reads and ends with a ranked lists of candidate shRNAs for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, hairpins in the rows) with options for allowing mismatches and small shifts in hairpin position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or a generalized linear modelling that can handle complex experimental designs. A detailed users' guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from http://www.bioconductor.org.

Pooled short hairpin RNA sequencing (shRNA-seq) screens are becoming increasingly popular in functional genomics research, and there is a need to establish optimal analysis tools to handle such data. Our open-source shRNA processing pipeline in edgeR provides a complete analysis solution for shRNA-seq screen data, that begins with the raw sequence reads and ends with a ranked lists of candidate shRNAs for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, hairpins in the rows) with options for allowing mismatches and small shifts in hairpin position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or a generalized linear modelling that can handle complex experimental designs. A detailed users’ guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from http://www.bioconductor.org.

BackgroundSNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering.ResultsHere we develop KRLMM, a new method for converting raw intensities into genotype calls that aims to overcome this issue. Our method is unique in that it applies careful between sample normalization and allows a variable number of clusters k (1, 2 or 3) for each SNP, where k is predicted using the available data. We compare our method to four genotyping algorithms (GenCall, GenoSNP, Illuminus and OptiCall) on several Illumina data sets that include samples from the HapMap project where the true genotypes are known in advance. All methods were found to have high overall accuracy (> 98%), with KRLMM consistently amongst the best. At low minor allele frequency, the KRLMM, OptiCall and GenoSNP algorithms were observed to be consistently more accurate than GenCall and Illuminus on our test data.ConclusionsMethods that tailor their approach to calling low frequency variants by either varying the number of clusters (KRLMM) or using information from other SNPs (OptiCall and GenoSNP) offer improved accuracy over methods that do not (GenCall and Illuminus). The KRLMM algorithm is implemented in the open-source crlmm package distributed via the Bioconductor project (http://www.bioconductor.org).

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