Development of a database system for mapping insertional mutations onto the mouse genome with large-scale experimental data
Bibliographic record
Abstract
BACKGROUND: Insertional mutagenesis is an effective method for functional genomic studies in various organisms. It can rapidly generate easily tractable mutations. A large-scale insertional mutagenesis with the piggyBac (PB) transposon is currently performed in mice at the Institute of Developmental Biology and Molecular Medicine (IDM), Fudan University in Shanghai, China. This project is carried out via collaborations among multiple groups overseeing interconnected experimental steps and generates a large volume of experimental data continuously. Therefore, the project calls for an efficient database system for recording, management, statistical analysis, and information exchange. RESULTS: This paper presents a database application called MP-PBmice (insertional mutation mapping system of PB Mutagenesis Information Center), which is developed to serve the on-going large-scale PB insertional mutagenesis project. A lightweight enterprise-level development framework Struts-Spring-Hibernate is used here to ensure constructive and flexible support to the application. The MP-PBmice database system has three major features: strict access-control, efficient workflow control, and good expandability. It supports the collaboration among different groups that enter data and exchange information on daily basis, and is capable of providing real time progress reports for the whole project. MP-PBmice can be easily adapted for other large-scale insertional mutation mapping projects and the source code of this software is freely available at http://www.idmshanghai.cn/PBmice. CONCLUSION: MP-PBmice is a web-based application for large-scale insertional mutation mapping onto the mouse genome, implemented with the widely used framework Struts-Spring-Hibernate. This system is already in use by the on-going genome-wide PB insertional mutation mapping project at IDM, Fudan University.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".