METB-09. VARIANT WORKBENCH IN THE KIDS FIRST PROGRAM FACILITATES GENOMIC DISCOVERIES IN PEDIATRIC NEURO-ONCOLOGY RESEARCH
Bibliographic record
Abstract
Abstract The Gabriella Miller Kids First Pediatric Research Program (Kids First) aims at facilitating researchers to uncover new insights into the biology of childhood cancer and structural birth defects, including the discovery of shared genetic pathways between these disorders. Kids First Data Resource Center developed the Kids First Data Resource Portal (KFDRP), which is a centralized data platform for both Kids First and collaborative cohorts. Currently, KFDRP facilitates data access for 40 studies with over 35,500 participants. To assist researchers and clinicians in identifying potential disease-causing germline variants in the human genome, KFDRP released the Variant WorkBench (VWB), which facilitates variant querying, manipulation, analysis, and visualization. Cavatica is a widely used bioinformatics analysis platform developed by Velsera. Compared to a previous version, the Cavatica-based VWB has better support for data import/export, more convenient sharing of notebooks and data, and easier billing details, with the same level of scalable, cloud-based computing. Additionally, Cavatica-based VWB has incorporated multiple public reference databases, including Cancer Hotspots, ClinVar, COSMIC, dbNSFP, gnomAD, TOPMed, as well as gene-phenotype links provided by OMIM, HPO, Orphanet, and the Deciphering Developmental Disorders Project. These databases have been converted to parquet files, reducing process time and memory usage compared to regular text files, thus speeding up data retrieval and querying. Furthermore, Cavatica empowers users to create and share custom analysis workflows tailored to specific research questions and datasets, enhancing data utility and analysis accuracy. Here we present a gene-based variant filtering workflow that aggregates information from dbNSFP, HGMD, TOPMed, gnomAD, and ClinVar. We identified 1202 pathogenic/likely pathogenic variants in NF1, a gene that helps regulate cell growth, across 582 pediatric brain tumor samples within 6 mins. Among the variants, there are 21 protein-altering variants. Overall, the upgraded VWB is more powerful, user-friendly, and adaptable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.143 | 0.099 |
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".