A blood RNA signature for tuberculosis disease risk: a prospective cohort study
Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease might lead to interventions that combat the tuberculosis epidemic. We aimed to assess whether global gene expression measured in whole blood of healthy people allowed identification of prospective signatures of risk of active tuberculosis disease. RESULTS:Between July 6, 2005, and April 23, 2007, we enrolled 6363 from the ACS study and 4466 from independent...
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.
- A modular transcriptional signature identifies phenotypic hetero... 2018 · 239 cites
- Mouse transcriptome reveals potential signatures of protection a... 2020 · 143 cites
- Multimodally profiling memory T cells from a tuberculosis cohort... 2021 · 115 cites
4 further papers cite this accession but reuse could not be confirmed.
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Expression profiling by RNA-seq from human. Grade A (100/100) with exceptional metrics: Q30 at 90.2%, duplication at 16.53% (scored 100/100), and zero adapter content. Exceptionally low duplication is rare and valuable; benchmark-quality data suitable for any downstream analysis.
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.