Incorporation of unique molecular identifiers in TruSeq adapters improves the accuracy of quantitative sequencing
Bibliographic record
Abstract
Quantitative analysis of next-generation sequencing data requires discriminating duplicate reads generated by PCR from identical molecules that are of unique origin. Typically, PCR duplicates are defined as sequence reads that align to the same genomic coordinates using reference-based alignment. However, identical molecules can be independently generated during library preparation. The false positive rate of coordinate-based deduplication has not been well characterized and may introduce unforeseen biases during analyses. We developed a cost-effective sequencing adapter design by modifying Illumina TruSeq adapters to incorporate a unique molecular identifier (UMI) while maintaining the capacity to undertake multiplexed sequencing. Incorporation of UMIs enables identification of bona fide PCR duplicates as identically mapped reads with identical UMIs. Using TruSeq adapters containing UMIs (TrUMIseq adapters), we find that accurate removal of PCR duplicates results in enhanced data quality for quantitative analysis of allele frequencies in heterogeneous populations and gene expression. Method Summary TrUMIseq adapters incorporate unique molecular identifiers in TruSeq adapters while maintaining the capacity to multiplex sequencing libraries using existing workflows. The use of UMIs increases the accuracy of quantitative sequencing assays, including RNAseq and allele frequency estimation, by enabling accurate detection of PCR duplicates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".