Method for the Identification of poly(A) sites in single-cell sequencing data

The Zavolan group developed a computational method to identify poly(A) sites in single-cell RNA sequencing data called SCINPAS. A key feature of SCINPAS is a modification compared to other methods of the read deduplication step, which improves the resolution of the poly(A) site recovery. The modified read deduplication step can be applied to other analyses of single-cell sequencing data. Their method has been published in the article "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS " in Nucleic Acids Research Genomics & Bioinformatics.

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. We propose 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, we show that SCINPAS is able to identify experimentally-supported, known and novel poly(A) sites from 3' end-based single-cell RNA sequencing data.

Read the Publication in NAR Genomics & Bioinformatics (Open Access)

Website Zavolan Lab

Abstract, figure and title from Moon et al (2023) NAR Genomics & Bioinformatics published under a CC BY-NC 4.0 license.