Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development
Single-cell RNA-seq is one of the most important and widely used approaches to characterize cell types and to understand major parts of biological systems. As of today, most methods are only able to capture parts of the whole transcriptome, mainly the protein-coding genes, and therefore lack information about non-coding biotypes or full-length transcripts. Here, we present “Vast transcriptome Analysis of Single-cells by dA-tailing (VASA-seq)”, a method for highly-sensitive, full-length and total...
Provenance — who produced it, who reused it
Linked to 4 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Spatial mapping of the total transcriptome by in situ polyadenyl... 2022 · 101 cites
2 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently