Whole transcriptome analysis identifies differentially regulated networks between osteosarcoma and normal bone samples
We performed whole transcriptome analysis of osteosarcoma bone samples. Initially we sequenced total RNA from 36 fresh-frozen samples (18 tumoral bone samples and 18 non-tumoral paired samples) matching in pairs for each osteosarcoma patient. We also performed independent gene expression analysis of formalin-fixed paraffin-embedded (FFPE) samples to verify the RNAseq results. The use of FFPE samples allowed to analyse the effect of chemotherapy. Data were analysed with DESeq2 and Reactome packag...
Provenance — who produced it, who reused it
Linked to 23 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.
22 further papers cite this accession but reuse could not be confirmed.
Deep data QC
68/100 · DStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The D 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