Restrander: rapid orientation and artefact removal for long-read cDNA data.

In transcriptomic analyses, it is helpful to keep track of the strand of the RNA molecules. However, the Oxford Nanopore long-read cDNA sequencing protocols generate reads that correspond to either the first or second-strand cDNA, therefore the strandedness of the initial transcript has to be inferred bioinformatically. Reverse transcription and PCR can also introduce artefacts which should be flagged in data pre-processing. Here we introduce Restrander, a lightning-fast and highly accurate tool for restranding and removing artefacts in long-read cDNA sequencing data. Thanks to its C++ implementation, Restrander was faster than Oxford Nanopore Technologies' existing tool Pychopper, and correctly restranded more reads due to its strategy of searching for polyA/T tails in addition to primer sequences from the reverse transcription and template-switch steps. We found that restranding improved the process of visualising and exploring data, and increased the number of novel isoforms discovered by bambu, particularly in regions where sense and anti-sense transcripts co-occur. The artefact detection implemented in Restrander quantifies reads lacking the correct 5' and 3' ends, a useful feature in quality control for library preparation. Restrander is pre-configured for all major cDNA protocols, and can be customised with user-defined primers. Restrander is available at https://github.com/mritchielab/restrander.

Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing.

A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.