SRX326765
SRAProvenance — who produced it, who reused it
Linked to 0 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.
No linked papers found in the corpus yet.
Deep data QC
50/100 · FStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Whole-genome shotgun sequencing of the Asian longhorned beetle (Anoplophora glabripennis) on Illumina HiSeq 2000 — short 101 bp reads at roughly 14.6 Gb total. In QC terms this dataset earns a failing grade, driven overwhelmingly by a duplication rate of 39.36%, which scored just 2/100: such heavy read redundancy means a large fraction of the ~72M reads are non-independent PCR or optical duplicates, inflating apparent depth while contributing little real coverage and biasing variant calling and any coverage-based analysis like assembly or CNV detection. Per-base quality is mediocre but not disqualifying — 81.4% of bases at Q30 (57/100) is acceptable for variant work but signals some error accumulation, while low adapter (2.06%) and negligible N content are genuine strengths. Most core quality metrics here were actually measured, so the duplication and quality readings are reliable; however, the headline totals (read and base counts) are only reported rather than independently verified, and the very low evidence_strength flag means the overall grade should be treated as provisional pending a fuller measured pass. For reuse, you can likely salvage usable data by aggressive duplicate removal, but budget for the effective coverage loss that implies.
The F grade is a transparent weighted average. Each metric below scored from 0–100% against the published WGS 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.