A comparative study of Bisulphite-seq analysis pipeline
Bibliographic record
Abstract
Recent advances in next generation sequencing (NGS) technology provide the opportunity to rapidly understand whole genome methylation profile. However, there are challenges in handling and interpretation of the methylation sequence data because of its large volume and the consequences of bisulphite modification. Most of the current pipelines include a specific aligner to decode and quantify the fraction of methylated cytosine per base; further this quantitative data is studied for differential methylation and annotated for genomic features. We have examined the performance of three pipelines for alignment and differential methylation profiling using the published data from plant and animals. We compared the consistency across these tools and explored various visualization features. We also illustrate our in-house visualization based analytic tool for a higher quality comprehension of whole genome methylation profile. Our comparative study showcases the performance of the widely accepted tools and can guide the scientific community in choosing the appropriate method for their methylation data analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".