123VCF: an intuitive and efficient tool for filtering VCF files
Bibliographic record
Abstract
BACKGROUND: The advent of Next-Generation Sequencing (NGS) has catalyzed a paradigm shift in medical genetics, enabling the identification of disease-associated variants. However, the vast quantum of data produced by NGS necessitates a robust and dependable mechanism for filtering irrelevant variants. Annotation-based variant filtering, a pivotal step in this process, demands a profound understanding of the case-specific conditions and the relevant annotation instruments. To tackle this complex task, we sought to design an accessible, efficient and more importantly easy to understand variant filtering tool. RESULTS: Our efforts culminated in the creation of 123VCF, a tool capable of processing both compressed and uncompressed Variant Calling Format (VCF) files. Built on a Java framework, the tool employs a disk-streaming real-time filtering algorithm, allowing it to manage sizable variant files on conventional desktop computers. 123VCF filters input variants in accordance with a predefined filter sequence applied to the input variants. Users are provided the flexibility to define various filtering parameters, such as quality, coverage depth, and variant frequency within the populations. Additionally, 123VCF accommodates user-defined filters tailored to specific case requirements, affording users enhanced control over the filtering process. We evaluated the performance of 123VCF by analyzing different types of variant files and comparing its runtimes to the most similar algorithms like BCFtools filter and GATK VariantFiltration. The results indicated that 123VCF performs relatively well. The tool's intuitive interface and potential for reproducibility make it a valuable asset for both researchers and clinicians. CONCLUSION: The 123VCF filtering tool provides an effective, dependable approach for filtering variants in both research and clinical settings. As an open-source tool available at https://project123vcf.sourceforge.io , it is accessible to the global scientific and clinical community, paving the way for the discovery of disease-causing variants and facilitating the advancement of personalized medicine.
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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 | 0.000 | 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".