CRISPR Knock-in Designer: Automatic Oligonucleotide Design Software to Introduce Point Mutations by Gene Editing Methods
Bibliographic record
Abstract
Background: Knock-in of precise point mutations into protein-coding genes is among the most important applications of Clustered Regularly Interspaced Palindromic Repeats (CRISPR)/Cas9. Precise introduction of amino acid changes is crucial to interrogate the function of specific protein residues and to create human disease models. Mutations of homologous protein residues in model animal species enable the studies of their consequences, leading to a better understanding of the disease in question. Objectives: Manual design of point mutation knock-ins is a time-consuming process consisting of many steps assisted by several computational tools. We have, therefore, designed CRISPR Knock-in Designer, which can perform the rapid and automatic design of point mutation knock-in DNA oligonucleotides upon provision of the mutation, a guide RNA, and identifier or sequence information. Method: We have used the shiny application framework in R for developing the website and several relevant R packages for the data processing and visualization steps. Automatic download of the relevant data occurs from the REST application programming interfaces provided by Ensembl. Results: The tool supports most experimentally established CRISPR types and has multiple options for the resulting oligonucleotides. We also provide allele-specific polymerase chain reaction-based and restriction enzymebased genotyping strategies in the program output. CRISPR Knock-in Designer adjusts to the genomic context of any target codon and designs knock-in strategies for two-exon straddling codons, which we explored in multiple species. CRISPR Knock-in Designer also provides input for two Prime Editing design tools to facilitate the introduction of a specific mutation sequence using this advanced technology. Conclusions: CRISPR Knock-in Designer provides an automatic way to design CRISPR/Cas-based amino acid substitution knock-in strategies including the genotyping strategies and generates inputs for Prime Editing design software.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".