MOLGENIS VIP: an end-to-end DNA variant interpretation pipeline for research and diagnostics configurable to support rapid implementation of new methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Achieving high yield in genetics research and genome diagnostics is a significant challenge because it requires a combination of multiple strategies and large-scale genomic analysis using the latest methods. Existing diagnostic software infrastructures are often unable to cope with high demands for versatility and scalability. We developed MOLGENIS VIP, a flexible, scalable, high-throughput, open-source, and "end-to-end" pipeline to process different types of sequencing data into portable, prioritized variant lists for immediate clinical interpretation in a wide variety of scenarios. VIP supports interpretation of short- and long-read sequencing data, using best-practice annotations and classification trees without complex IT infrastructures. VIP is developed within the long-living MOLGENIS open-source project to provide sustainability and has integrated feedback from a growing international community of users. VIP has undergone genome diagnostic laboratory testing and harnesses experiences from multiple Dutch, European, Canadian, and African diagnostic and infrastructural initiatives (VKGL, EU-Solve-RD, EJP-RD, CINECA, GA4GH). We provide a step-by-step protocol for installing and using VIP. We demonstrate VIP using 25 664 previously classified variants from the VKGL, and 18 and 41 diagnosed patients from a routine diagnostics and a Solve-RD research cohort, respectively. We believe that VIP accelerates causal variant detection and innovation in genome diagnostics and research.
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.
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 | 0.001 | 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 it