Corpus 1,272 assessed · 1,173 scored · 643 reproduced ≥75 · 168 flagged ·∅ 74.1/100
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GSE55914

GEO first seen 2014

RNA-RNA interactions enable specific targeting of noncoding RNAs to nascent pre-mRNAs and chromatin sites

Organism
Mus musculus
Samples
46
Type
Genome binding/occupancy pro...
Submitted
2014-03-14

We developed a general method based on RNA Antisense Purification (RAP) to identify the intermolecular RNA-RNA interactions of a target RNA (RAP-RNA). RAP-RNA identifies endogenous RNA-RNA complexes through in vivo crosslinking, RNA capture with antisense oligonucleotides, and high-throughput RNA sequencing. This approach provides a systematic view of other RNAs that interact with a target RNA, and furthermore can distinguish between direct and indirect RNA-RNA interactions through the use of cr...

Provenance — who produced it, who reused it

Linked to 7 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.

Deposited / produced by
Jesse M EngreitzKlara SirokmanPatrick McDonelAlexander ShishkinChristine SurkaAmy ChowPamela RussellSharon R GrossmanMitchell GuttmanEric S Lander
Reused by

1 further paper cites this accession but reuse could not be confirmed.

Deep data QC

metadata only · no data-level QC for this type

Standardized, 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.

QC cost 22 s compute

measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently