BisQC: an operational pipeline for multiplexed bisulfite sequencing
Bibliographic record
Abstract
BACKGROUND: Bisulfite sequencing is the most efficient single nucleotide resolution method for analysis of methylation status at whole genome scale, but improved quality control metrics are needed to better standardize experiments. RESULTS: We describe BisQC, a step-by-step method for multiplexed bisulfite-converted DNA library construction, pooling, spike-in content, and bioinformatics. We demonstrate technical improvements for library preparation and bioinformatic analyses that can be done in standard laboratories. We find that decoupling amplification of bisulfite converted (bis) DNA from the indexing reaction is an advantage, specifically in reducing total PCR cycle number and pre-selecting high quality bis-libraries. We also introduce a progressive PCR method for optimal library amplification and size-selection. At the sequencing stage, we thoroughly test the benefits of pooling non-bis DNA library with bis-libraries and find that BisSeq libraries can be pooled with a high proportion of non-bis DNA libraries with minimal impact on BisSeq output. For informatics analysis, we propose a series of optimization steps including the utilization of the mitochondrial genome as a QC standard, and we assess the validity of using duplicate reads for coverage statistics. CONCLUSION: We demonstrate several quality control checkpoints at the library preparation, pre-sequencing, post-sequencing, and post-alignment stages, which should prove useful in determining sample and processing quality. We also determine that including a significant portion of non-bisulfite converted DNA with bisulfite converted DNA has a minimal impact on usable bisulfite read output.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".