PIMMS-Dash: Accessible analysis, interrogation, and visualisation of high-throughput transposon insertion sequencing (TIS) data
Bibliographic record
Abstract
Motivation: Current methods for visualising and interrogating high-throughput transposon insertion mutagenesis sequencing (TIS) data requires a significant time investment in learning bioinformatics, often producing static figures that do not facilitate real time analysis. Summary: We have created an accessible web-based browser tool for visualisation and downstream analysis of high-throughput TIS data results generated by the PIMMS analysis pipeline. This includes multiple interactive and sortable tables to aid the user to identify genes of interest, enabling the user to gain a greater understanding of the genes contributing to fitness in their experimental work. PIMMS-Dash permits researchers, with any level of bioinformatics knowledge, to interrogate data sets and generate publication quality figures. Availability: PIMMS-Dash is freely available and is accessible online at https://pimms-dashboard-uon.azurewebsites.net and a Docker containerised version is available at https://github.com/Streptococcal-Research-Group/PIMMS-Dash to run locally.
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.100 | 0.067 |
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".