Abstract LB501: Making discoveries with Kids First Variant DataBase and WorkBench
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 (CC) and structural birth defects (SBD), including the discovery of shared genetic pathways between these disorders. Kids First has two initiatives, which are whole genome sequencing (WGS) of biospecimens from families with CC/SBD, and establishing Kids First Data Resource. Kids First Data Resource Center (KFDRC) developed Kids First Data Resource Portal (KFDRP; https://portal.kidsfirstdrc.org/), which is a centralized platform to search, view, analyze, and identify currently accessible data from both Kids First and collaborative cohorts, incorporating omics and phenotypic information of 30 studies and 26,300 participants. Recently KFDRC released two new KFDRP components named Variant DataBase (VDB) and Variant WorkBench (VWB), enabling users to query, mangle, analyze and visualize germline genomic variants. The current release includes ~309 million unique variants in a matrix of more than 61.3 billion individual-chromosomal position occurrences from over 11,500 participants in 17 studies. While VDB provides a quick variant summary, VWB supports scripting languages such as Python, Spark, SQL, R, and MarkDown as in-depth analysis tools enabled by Apache Zeppelin notebooks. In addition to variant calls and phenotypic information such as Human Phenotype Ontology (HPO) terms and Mondo IDs, VWB hosts rich external variant annotations in the public domain, such as Cancer Hotspots, ClinVar, COSMIC, dbNSFP, gnomAD, TOPMed, as well as gene-phenotype links provided by OMIM, HPO, Orphanet, and the Deciphering Developmental Disorders Project. Users can also load additional databases within a notebook (such as the subscription-based Human Gene Mutation Database [HGMD]), import custom datasets as temporary query tables, export analysis outputs to local drives, visualize analysis results in multiple chart styles, display local figures, and save notebooks for sharing, further use and Cavatica projects. In an effort to screen tier 1 genes (n=578) from the most recent Cancer Gene Census provided by COSMIC for rare deleterious variants in all six currently available CC cohorts in KFDRP, we identified over 1.2 million germline variants that are with minor allele frequency no more than 0.00001 among gnomAD/TOPMed datasets and with ratio of number of “damaging” predictions over number of all predictions in dbNSFP between 0.5 and 1, or that are already cataloged in the most recent version of ClinVar/HGMD. The whole process took less than two hours which is much faster than conventional methods. These variant tools in KFDRP enable efficient genomic variant analysis in cancer research with advanced big data technology. Citation Format: Yiran Guo, Jeremy Costanza, Christophe Botek, Miguel Brown, David Higgins, Yuankun Zhu, Bailey Farrow, Allison Heath, Adam Resnick, Vincent Ferretti, Kids First Data Resource Center. Making discoveries with Kids First Variant DataBase and WorkBench [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB501.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.060 |
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".