SNVerGUI: a desktop tool for variant analysis of next-generation sequencing data
Bibliographic record
Abstract
BACKGROUND: Advances in next generation sequencing (NGS) technology have made it possible to interrogate comprehensively genome-wide genetic variations. However, most existing tools for variation detection are based on command-line interface, which discourages the main end users of NGS data, such as biologists, geneticists and clinicians, from utilising the software. METHOD AND RESULTS: We have developed the SNVerGUI, a graphical user interface (GUI) based tool for variant detection and analysis. Compared with other methods for variant calling, our approach is unique in that it is applicable to both individual and pooled sequencing data. With friendly GUI, end users can easily adjust running parameters to optimise variant calling for their specific needs. SNVerGUI supports commonly used input and output file formats that allows SNVerGUI to be seamlessly integrated into common NGS data analysis pipelines. SNVerGUI is implemented in Java, which is platform-independent and therefore easy to install and run on the commonly used operating systems, such as Linux, Mac, and Windows. Using two real datasets, we have shown that SNVerGUI is capable of analysing very high volume NGS data in a feasible time on personal computers. CONCLUSIONS: SNVerGUI is a fast and easy desktop GUI tool for the identification of genomic variants from pooled sequencing and individual sequencing data. Using this software, users can perform sophisticated variant detection by simply configuring several parameters in a friendly graphical user interface. SNVerGUI makes variant analysis as simple and effortless as possible, and we expect it to become popular among geneticists, clinicians, and biologists. SNVerGUI can be freely downloaded from http://snver.sourceforge.net/snvergui/, and will be continuously updated upon users' feedback.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.027 |
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".