Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs
RNA-Seq provides an unbiased way to study a transcriptome, including both coding and non-coding genes. To date, most RNA-Seq studies have critically depended on existing annotations, and thus focused on studying expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We apply this approach to mouse embryonic stem cells, neuronal precursor cells, and lung fibro...
Provenance — who produced it, who reused it
Linked to 6 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.
- Epigenetic conservation at gene regulatory elements revealed by... 2013 · 246 cites
- Long non-coding RNA identification over mouse brain development... 2013 · 79 cites
- Enhancer identification in mouse embryonic stem cells using inte... 2012 · 78 cites
3 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