Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline
Bibliographic record
Abstract
The continued improvement of combinatorial CRISPR screening platforms necessitates the development of new computational pipelines for scoring combinatorial screening data. Unlike for single-guide RNA (sgRNA) pooled screening platforms, combinatorial scoring for multiplexed systems is confounded by guide design parameters such as the number of gRNAs per construct, the position of gRNAs along constructs, and additional features that may impact gRNA expression, processing or capture. In this protocol we describe Orthrus, an R package for processing, scoring and analyzing combinatorial CRISPR screening data that addresses these challenges. This protocol walks through the application of Orthrus to previously published combinatorial screening data from the CHyMErA experimental system, a platform we recently developed that pairs Cas9 with Cas12a gRNAs and enables programmed targeting of multiple genomic sites. We demonstrate Orthrus' features for screen quality assessment and two distinct scoring modes for dual guide RNAs (dgRNAs) that target the same gene twice or dgRNAs that target two different genes. Running Orthrus requires basic R programming experience, ~5-10 min of computational time and 15-60 min total.
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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".