RNA-seq alignment to individualized genomes
The source of most errors in RNA sequencing (RNA-seq) read alignment is in the repetitive structure of the genome and not with the alignment algorithm. Genetic variation away from the reference sequence exacerbates this problem causing reads to be assigned to the wrong location. We developed a method, implemented as the software package Seqnature, to construct the imputed genomes of individuals (individualized genomes) of experimental model organisms including inbred mouse strains and geneticall...
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.
- RNA-Seq Alignment to Individualized Genomes Improves Transcript... 2014 · 106 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