Identifying cancer mutation targets across thousands of samples: MuteProc, a high throughput mutation analysis pipeline
Bibliographic record
Abstract
BACKGROUND: In the past decade, bioinformatics tools have matured enough to reliably perform sophisticated primary data analysis on Next Generation Sequencing (NGS) data, such as mapping, assemblies and variant calling, however, there is still a dire need for improvements in the higher level analysis such as NGS data organization, analysis of mutation patterns and Genome Wide Association Studies (GWAS). RESULTS: We present a high throughput pipeline for identifying cancer mutation targets, capable of processing billions of variations across thousands of samples. This pipeline is coupled with our Human Variation Database to provide more complex down stream analysis on the variations hosted in the database. Most notably, these analysis include finding significantly mutated regions across multiple genomes and regions with mutational preferences within certain types of cancers. The results of the analysis is presented in HTML summary reports that incorporate gene annotations from various resources for the reported regions. CONCLUSION: MuteProc is available for download through the Vancouver Short Read Analysis Package on Sourceforge: http://vancouvershortr.sourceforge.net. Instructions for use and a tutorial are provided on the accompanying wiki pages at https://sourceforge.net/apps/mediawiki/vancouvershortr/index.php?title=Pipeline_introduction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".