Beta-Binomial Modeling of CRISPR Pooled Screen Data Identifies Target Genes with Greater Sensitivity and fewer False Negatives
Bibliographic record
Abstract
\beginabstract The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas9 gene-editing platform is simple, cost-effect, and robust, and it has allowed high-throughput pooled screen approaches to be accessible by any laboratory. As a result, large amounts of next-generation sequencing data is being generated at a fast rate, but utilizing this data is challenging due to three hurdles: designing a statistical model to identify the hit genes, developing an accurate alignment algorithm to extract readout for each single-guide RNA (sgRNA) from the sequence data, and minimizing the parameters that have to be tuned. Several methods have been proposed to tackle the challenges above, but most of them use the negative binomial distribution to model CRISPR pooled screen data even though the structure of the sgRNA screen data is far away from the nature of RNA-seq data which tend to be over-dispersed. In RNA-seq data the length of the transcript varies, while the length of sgRNAs used for the CRISPR system is designed to be the same in any gene which often leads to under-dispersion. Here we propose CRISPRBetaBinomial (CB\textsuperscript2 ) which uses the beta-binomial distribution which better models the CRISPR pooled screen data (\urlhttps://CRAN.R-project.org/package=CB2 ) \citeJeong01062019. We used published screen datasets to benchmark the accuracy of CB\textsuperscript2 and compared it to eight published methods. CB\textsuperscript2 outperforms the other methods in both alignment and target identification (Figure \reffig:teaser ). CB\textsuperscript2 will also accelerate discovering novel biological findings from CRISPR pooled screens with the cooperation of CRISPRcloud (\urlhttp://crispr.nrihub.org ). \endabstract
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".