The Dockstore: enabling modular, community-focused sharing of Docker-based genomics tools and workflows
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
As genomic datasets continue to grow, the feasibility of downloading data to a local organization and running analysis on a traditional compute environment is becoming increasingly problematic. Current large-scale projects, such as the ICGC PanCancer Analysis of Whole Genomes (PCAWG), the Data Platform for the U.S. Precision Medicine Initiative, and the NIH Big Data to Knowledge Center for Translational Genomics, are using cloud-based infrastructure to both host and perform analysis across large data sets. In PCAWG, over 5,800 whole human genomes were aligned and variant called across 14 cloud and HPC environments; the processed data was then made available on the cloud for further analysis and sharing. If run locally, an operation at this scale would have monopolized a typical academic data centre for many months, and would have presented major challenges for data storage and distribution. However, this scale is increasingly typical for genomics projects and necessitates a rethink of how analytical tools are packaged and moved to the data. For PCAWG, we embraced the use of highly portable Docker images for encapsulating and sharing complex alignment and variant calling workflows across highly variable environments. While successful, this endeavor revealed a limitation in Docker containers, namely the lack of a standardized way to describe and execute the tools encapsulated inside the container. As a result, we created the Dockstore ( https://dockstore.org), a project that brings together Docker images with standardized, machine-readable ways of describing and running the tools contained within. This service greatly improves the sharing and reuse of genomics tools and promotes interoperability with similar projects through emerging web service standards developed by the Global Alliance for Genomics and Health (GA4GH).
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 | 0.050 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.000 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.000 | 0.002 |
| 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