SRX326768
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
97/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
This is whole-genome shotgun sequencing of the Asian longhorned beetle (Anoplophora glabripennis) on Illumina HiSeq 2000, and it earns a clean A (97/100): the reads are genuinely high quality, with 92.8% of bases at Q30 and a mean base quality of 36.4, meaning base calls are reliable enough to support confident variant calling and assembly. The metric that pulled the score down most is the 11.48% duplication rate, which is moderate and worth noting because PCR/optical duplicates inflate apparent coverage and can bias allele-frequency or coverage-based analyses unless flagged and removed; negligible adapter (0.05%) and N content (0.005%) mean little upfront trimming is needed. Critically, the headline counts — total_bases (~23.7 Gb) and total_reads — are reported rather than independently measured, but the QC-defining quality, duplication, adapter, and GC metrics were all actually measured, so the grade rests on real evidence rather than extrapolation. Overall this is a trustworthy dataset to reuse for ~100 bp paired WGS work; just deduplicate before any coverage- or frequency-sensitive step and treat the read/base totals as provider-stated until verified.
The A 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.