METB-10. Analyzing both germline and somatic variants using Variant WorkBench in the Kids First Data Resource Portal: Children’s Brain Tumor Network as an example
Bibliographic record
Abstract
Abstract Aiming at facilitating researchers to uncover new insights into the biology of childhood cancers and structural birth defects, the Gabriella Miller Kids First Pediatric Research Program (Kids First) is initiated. The Kids First Data Resource Center (KFDRC) developed the Kids First Data Resource Portal (KFDRP; https://portal.kidsfirstdrc.org/), a centralized data platform for both Kids First and collaborative cohorts. On behalf of KFDRC, we present as part of KFDRP the upgraded Variant WorkBench (VWB) with more data incorporated, on a more efficient platform, in a more streamlined data flow design, and capable of analyzing both germline and somatic genomic variants. First, the current collection of Kids First data include reharmonized genomics data of over 922,000 files in more than 35,400 participants from 35 studies. We also provide updated variant/gene annotation databases from more than 50 public resources (e.g. gnomAD, ClinVar, HPO etc.). Second, VWB is running on Velsera’s Cavatica Data Studio platform with a new Spark version 3.5.1 plus Python 3.11, achieving a ∼10 fold acceleration in terms of executing PySpark codes when compared to previous versions. Third, we redesigned the data flow from KFDRP to VWB, where portal users can now import Kids First data with which they have dbGaP approval directly to a Cavatica project and start analyzing in VWB. As an example, we show how to use VWB to identify deleterious variants within the same genes in both germline and somatic genomes of the same participant from the Children’s Brain Tumor Network, the largest Kid First cohort so far. In conclusion, the upgraded Variant WorkBench enables accelerated exploration of pediatric disease genomics under the Kids First program.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.025 |
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".