CRISPRStudio: A User-Friendly Software for Rapid CRISPR Array Visualization
Bibliographic record
Abstract
The CRISPR-Cas system biologically serves as an adaptive defense mechanism against phages. However, there is growing interest in exploiting the hypervariable nature of the CRISPR locus, often of viral origin, for microbial typing and tracking. Moreover, the spacer content of any given strain provides a phage resistance profile. Large-scale CRISPR typing studies require an efficient method for showcasing CRISPR array similarities across multiple isolates. Historically, CRISPR arrays found in microbes have been represented by colored shapes based on nucleotide sequence identity and, while this approach is now routinely used, only scarce computational resources are available to automate the process, making it very time-consuming for large datasets. To alleviate this tedious task, we introduce CRISPRStudio, a command-line tool developed to accelerate CRISPR analysis and standardize the preparation of CRISPR array figures. It first compares nucleotide spacer sequences present in a dataset and then clusters them based on sequence similarity to assign a meaningful representative color. CRISPRStudio offers versatility to suit different biological contexts by including options such as automatic sorting of CRISPR loci and highlighting of shared spacers, while remaining fast and user-friendly.
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 | 0.000 | 0.000 |
| 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".