Corpus 1,285 assessed · 1,186 scored · 647 reproduced ≥75 · 174 flagged ·∅ 73.9/100
← Dataset search

GSE81503

GEO first seen 2016

CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

Organism
Homo sapiens; Mus musculus
Samples
6
Type
Other
Submitted
2016-05-17

Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions...

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.

Deposited / produced by
Jonathan CairnsPaula Freire-PritchettSteven W WingettCsilla VárnaiAndrew DimondVincent PlagnolDaniel ZerbinoStefan SchoenfelderBiola-Maria JavierreCameron OsbornePeter FraserMikhail Spivakov
Reused by

2 further papers cite 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