VAP: a versatile aggregate profiler for efficient genome-wide data representation and discovery
Bibliographic record
Abstract
The analysis of genomic data such as ChIP-Seq usually involves representing the signal intensity level over genes or other genetic features. This is often illustrated as a curve (representing the aggregate profile of a group of genes) or as a heatmap (representing individual genes). However, no specific resource dedicated to easily generating such profiles is currently available. We therefore built the versatile aggregate profiler (VAP), designed to be used by experimental and computational biologists to generate profiles of genomic datasets over groups of regions of interest, using either an absolute or a relative method. Graphical representation of the results is automatically generated, and subgrouping can be performed easily, based on the orientation of the flanking annotations. The outputs include statistical measures to facilitate comparisons between groups or datasets. We show that, through its intuitive design and flexibility, VAP can help avoid misinterpretations of genomics data. VAP is highly efficient and designed to run on laptop computers by using a memory footprint control, but can also be easily compiled and run on servers. VAP is accessible at http://lab-jacques.recherche.usherbrooke.ca/vap/.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".