Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs
We report a novel single-cell total RNA-seq method, RamDA-seq, by combining a novel reverse transcription (RT) technology, RT with random displacement amplification (RT-RamDA), and not-so-random (NSR) primers. RT-RamDA provides global cDNA amplification directly from RNA during RT without any universal adopters, which benefits RT efficiency, simplification of the procedure, avoiding the step of PCR amplification, and decontamination of genomic DNA. NSR enables random priming while preventing cDN...
Provenance — who produced it, who reused it
Linked to 14 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.
- Benchmarking algorithms for gene regulatory network inference fr... 2020 · 817 cites
- Network inference with Granger causality ensembles on single-cel... 2022 · 125 cites
11 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