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Are you interested in performing splice-aware quantification of your #scrnaseq data, obtaining unspliced, spliced, and ambiguous UMI counts quickly & in <3GB of RAM? If so, check out the new manuscript by @DongzeHe, @CSoneson and me on #bioRxiv bit.ly/3vJr0Ji. 1/🧵
Understanding the origin of sequencing reads — the molecules from which they arise, the "gene" with which those molecules are associated, and the splicing status of those molecules — is a key task in single-cell RNA-seq quantification.
The short, (effectively) single-end nature of the reads used in popular technologies leads to situations in which it can be difficult or impossible to predict if a read was sequenced from an unspliced (nascent) or spliced (mature) RNA; these reads are designated as "ambiguous."
Identification of m6A has been possible for some time using @nanopore direct RNA-Seq. Today we published m6Anet, achieving high accuracy single base, single molecule resolution nature.com/articles/s4159…
What is m6Anet, and why this makes a big difference🧵#biodata22 @naturemethods
What is m6Anet, and why this makes a big difference🧵#biodata22 @naturemethods
m6ANet (github.com/GoekeLab/m6anet) implements a supervised machine learning appraoch, i.e. it uses labeled data (direct RNA-Seq data at known m6A sites) for training a model that then makes predictions in the absence of any labels
However, not all reads at known m6A positions are modified which is known as a multiple instance learning (MIL) problem. m6ANet models m6A detection as a MIL problem, therby automatically handling the mismatch between data and labels. 
