Validation of bcbio-nextgen Pipeline Based on NextSeq500 Exome Sequencing
Bibliographic record
Abstract
We evaluated the performance of bcbio-nextgen pipeline for clinical whole exome sequencing (WES) using Genome in a Bottle (GIAB) NA12878 reference sample with Agilent Clinical Research Exome (CRE) v2 panel and Agilent SureSelect XT2 library preparation kit. The current literature on NA12878 reference materials predominantly describes the performance of WES-based pipelines for Illumina HiSeqX. Here, we evaluated the analytical performance of the bcbio-nextgen pipeline for WES for Illumina NextSeq500. We compared the performance of the bcbio-nextgen pipeline with the performance of other WES-based pipelines reported in literature. Our comparative analyses showed that the sensitivity and precision of our study compared favourably with the performance of other bioinformatics pipelines. Notably, our results demonstrate that analytical sensitivity of WES deteriorates outside of capture regions and suggests that any variants identified outside of capture regions are more likely to be lower confidence variants and therefore require confirmatory testing. Careful consideration of intersect between genomic regions of interest (ROI) and capture regions is required to achieve optimal performance of WES for clinical applications.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".