Systematic assessment of next-generation sequencing for quantitative small RNA profiling: A-to-I editing pools
Small RNA-seq is increasingly being used for profiling of small RNAs. Quantitative characteristics of long RNA-seq have been extensively described, but small RNA-seq involves fundamentally different methods for library preparation, with distinct protocols and technical variations that have not been fully and systematically studied. Using common sets of reference samples, we evaluated the accuracy, reproducibility and bias of small RNA-seq library preparation for five distinct protocols and acros...
Provenance — who produced it, who reused it
Linked to 1 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.
Deep data QC
99/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Bulk RNA-seq of synthetic construct with unavailable instrument metadata (HTTP fetch error), but good base quality (92.3% Q30, 75 bp read length). This dataset may represent positive control sequencing or validation of bioinformatic pipelines using in silico-generated transcripts. Researchers should contact GEO to clarify the experimental design and synthetic sequence composition.
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