NGSTroubleFinder: A tool for detection and quantification of contamination and kinship across human NGS data
Bibliographic record
Abstract
Abstract Summary Quality control is a fundamental but often neglected step in any NGS pipeline. Detecting issues like cross-sample contamination and sample swaps is essential to control the data integrity. Here, we present NGSTroubleFinder, a novel python tool to detect cross-sample contamination in human Whole-Genome and Whole-Transcriptome Sequencing data, sample swaps and mismatches between the reported and the inferred genetic and transcriptomic sexes. NGSTroubleFinder is implemented in Python and incorporates a custom-built parallelized pileup engine written in C. The tool reports extensive information on the samples both in textual and HTML format including key plots for easy interpretation of the results. Availability and Implementation NGSTroubleFinder is written in Python and C, and it can be easily installed with pip. The tool source code and the models are freely available on github ( https://github.com/STALICLA-RnD/NGSTroubleFinder ) and a containerized version is available on dockerhub ( https://hub.docker.com/r/staliclarnd/ngstroublefinder ).
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".