The fifth international hackathon for developing computational cloud-based tools and resources for pan-structural variation and genomics
Bibliographic record
Abstract
Background The goal of the Fifth Annual Baylor College of Medicine & DNAnexus Structural Variation Hackathon was to push forward the research on structural variants (SVs) by rapidly developing and deploying open-source software. The event took place in-person and virtually in August 2023, when 49 scientists from 14 countries and 8 U.S. states collaboratively worked on projects to address critical gaps in the field of genomics. The hackathon projects concentrated on developing bioinformatic workflows for the following challenges: RNA transcriptome comparison, simulation of mosaic variations, metagenomics, Mendelian variation, SVs in plant genomics, and assembly vs. mapping SV calling comparisons. Methods As a starting point we used publicly available data from state-of-the-art long- and short-read sequencing technologies. The workflows developed during the hackathon incorporated open-source software, as well as scripts written using Bash and Python. Moreover, we leveraged the advantages of Docker and Snakemake for workflow automation. Results The results of the hackathon consists of six prototype bioinformatic workflows that use open-source software for SV research. We made the workflows scalable and modular for usability and reproducibility. Furthermore, we tested the workflows on example public data to show that the workflows can work. The code and the data produced during the event have been made publicly available on GitHub (https://github.com/collaborativebioinformatics) to reproduce and built upon in the future. Conclusions The following sections describe the motivation, lessons learned, and software produced by teams during the hackathon. Here, we describe in detail the objectives, value propositions, implementation, and use cases for our workflows. In summary, the article reports the advancements in the development of software for SV detection made during the hackathon.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.249 | 0.113 |
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".