SmMIP-tools: a computational toolset for processing and analysis of single-molecule molecular inversion probes-derived data
Bibliographic record
Abstract
MOTIVATION: Single-molecule molecular inversion probes (smMIPs) provide an exceptionally cost-effective and modular approach for routine or large-cohort next-generation sequencing. However, processing the derived raw data to generate highly accurate variants calls remains challenging. RESULTS: We introduce SmMIP-tools, a comprehensive computational method that promotes the detection of single nucleotide variants and short insertions and deletions from smMIP-based sequencing. Our approach delivered near-perfect performance when benchmarked against a set of known mutations in controlled experiments involving DNA dilutions and outperformed other commonly used computational methods for mutation detection. Comparison against clinically approved diagnostic testing of leukaemia patients demonstrated the ability to detect both previously reported variants and a set of pathogenic mutations that did not pass detection by clinical testing. Collectively, our results indicate that increased performance can be achieved when tailoring data processing and analysis to its related technology. The feasibility of using our method in research and clinical settings to benefit from low-cost smMIP technology is demonstrated. AVAILABILITY AND IMPLEMENTATION: The source code for SmMIP-tools, its manual and additional scripts aimed to foster large-scale data processing and analysis are all available on github (https://github.com/abelson-lab/smMIP-tools). Raw sequencing data generated in this study have been submitted to the European Genome-Phenome Archive (EGA; https://ega-archive.org) and can be accessed under accession number EGAS00001005359. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".