Alternative polyadenylation is a main driver of transcriptome diversity in mammals, generating transcript isoforms with different 3′ ends via cleavage and polyadenylation at distinct polyadenylation (poly(A)) sites. The regulation of cell type-specific poly(A) site choice is not completely resolved, and requires quantitative poly(A) site usage data across cell types. 3′ end-based single-cell RNA-seq can now be broadly used to obtain such data, enabling the identification and quantification of poly(A) sites with direct experimental support.
Researchers at the Biozentrum University of Basel have developed SCINPAS, a computational method to identify poly(A) sites from scRNA-seq datasets. SCINPAS modifies the read deduplication step to favor the selection of distal reads and extract those with non-templated poly(A) tails. This approach improves the resolution of poly(A) site recovery relative to standard software. SCINPAS identifies poly(A) sites in genic and non-genic regions, providing complementary information relative to other tools. The workflow is modular, and the key read deduplication step is general, enabling the use of SCINPAS in other typical analyses of single cell gene expression. Taken together, these researchers show that SCINPAS is able to identify experimentally-supported, known and novel poly(A) sites from 3′ end-based single-cell RNA sequencing data.
Scheme of SCINPAS workflow
The inputs to SCINPAS are indicated in the green box. Alignments of reads from primary samples are generated with CellRanger. The SCINPAS processing steps are shown in the cyan boxes and the outputs of the workflow are indicated in the orange box. File formats for inputs and outputs are indicated in parentheses.
Availability – SCINPAS is packaged into a nextflow workflow. The code and analysis are available from: https://github.com/zavolanlab/SCINPAS