Systematic discovery and classification of human cell line essential genes
Bibliographic record
Abstract
Abstract The study of gene essentiality in human cells is crucial for elucidating gene function and holds great potential for finding therapeutic targets for diseases such as cancer. Technological advances in genome editing using clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 systems have set the stage for identifying human cell line core and context-dependent essential genes. However, first generation negative selection screens using CRISPR technology demonstrate extreme variability across different cell lines. To advance the development of the catalogue of human core and context-dependent essential genes, we have developed an optimized, ultracomplex, genome-scale gRNA library of 176,500 guide RNAs targeting 17,661 genes and have applied it to negative and positive selection screens in a human cell line. Using an improved Bayesian analytical approach, we find CRISPR-based screens yield double to triple the number of essential genes than were previously observed using systematic RNA interference, including many genes at moderate expression levels that are largely refractory to RNAi methods. We further characterized four essential genes of unknown significance and found that they all likely exist in protein complexes with other essential genes. For example, RBM48 and ARMC7 are both essential nuclear proteins, strongly interact and are commonly amplified across major cancers. Our findings suggest the CRISPR-Cas9 system fundamentally alters the landscape for systematic reverse genetics in human cells for elucidating gene function, identifying disease genes, and uncovering therapeutic targets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".