Human variation database: an open-source database template for genomic discovery
Bibliographic record
Abstract
MOTIVATION: Current public variation databases are based upon collaboratively pooling data into a single database with a single interface available to the public. This gives little control to the collaborator to mine the database and requires that they freely share their data with the owners of the repository. We aim to provide an alternative mechanism: providing the source code and application programming interface (API) of a database, enabling researchers to set up local versions without investing heavily in the development of the resource and allowing for confidential information to remain secure. RESULTS: We describe an open-source database that can be installed easily at any research facility for the storage and analysis of thousands of next-generation sequencing variations. This database is built using PostgreSQL 8.4 (The PostgreSQL Global Development Group. postgres 8.4: http://www.postgresql.org) and provides a novel method for collating and searching across the reported results from thousands of next-generation sequence samples, as well as rapidly accessing vital information on the origin of the samples. The schema of the database makes rapid and insightful queries simple and enables easy annotation of novel or known genetic variations. A modular and cross-platform Java API is provided to perform common functions, such as generation of standard experimental reports and graphical summaries of modifications to genes. Included libraries allow adopters of the database to quickly develop their own queries. AVAILABILITY: The software is available for download through the Vancouver Short Read Analysis Package on Sourceforge, http://vancouvershortr.sourceforge.net. Instructions for use and deployment are provided on the accompanying wiki pages. CONTACT: afejes@bcgsc.ca.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.069 |
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".