WO2024158966 - DEGENERATE CRISPR CAS13A CRRNAS FOR DETECTION OF HIGHLY VARIABLE RNA SEQUENCES
National phase entry is expected:
Publication Number
WO/2024/158966
Publication Date
02.08.2024
International Application No.
PCT/US2024/012867
International Filing Date
25.01.2024
Title **
[English]
DEGENERATE CRISPR CAS13A CRRNAS FOR DETECTION OF HIGHLY VARIABLE RNA SEQUENCES
[French]
ARNCR CRISPR/CAS13A DÉGÉNÉRÉ POUR LA DÉTECTION DE SÉQUENCES D'ARN HAUTEMENT VARIABLES
Applicants **
THE GOVERNMENT OF THE UNITED STATES OF AMERICA, AS REPRESENTED BY THE SECRETARY OF THE NAVY
Naval Research Laboratory
875 North Randolph Street, Suite 1425
Arlington, Virginia 22203, US
Inventors
LESKI, Tomasz
4555 Overlook Ave SW
Washington, District of Columbia 20375, US
SPANGLER, Joseph
4555 Overlook Ave SW
Washington, District of Columbia 20375, US
WANG, Zheng
4555 Overlook Ave SW
Washington, District of Columbia 20375, US
DEAN, Scott
4555 Overlook Ave SW
Washington, District of Columbia 20375, US
STENGER, David
4555 Overlook Ave SW
Washington, District of Columbia 20375, US
Priority Data
63/441,196
26.01.2023
US
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
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| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
| Pages for Publication | * |
| Number of Pages with Drawings | * |
| Pages of Specification | * |
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International Searching Authority |
MOIP
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| Applicant's Legal Status |
Legal Entity
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| Entry into National Phase under |
Chapter I
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| Translation |
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Recalculate
* The data is based on automatic recognition. Please verify and amend if necessary.
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing | 1007 | |
| EPO | Filing, Examination | 6507 | |
| Japan | Filing | 592 | |
| South Korea | Filing | 575 | |
| USA | Filing, Examination | 2635 |
Total: 11316 USD
The term for entry into the National Phase has expired. This quotation is for informational purposes only
Abstract[English]
A technique for the design of minimum CRISPR RNA (crRNA) sets aids in the detection of diverse nucleic acid targets using sequence degeneracy. As a working example, candidate degenerate Cas13a crRNA sets were designed for detection of diverse RNA targets (Lassa virus). A decision tree machine learning (ML) algorithm (RuleFit) was applied to define the top attributes that determine the specificity of degenerate crRNAs to elicit collateral nuclease activity. This general ML approach can be applied to the design of degenerate crRNA sets for any CRISPR/Cas system.[French]
Une technique pour la conception d'ensembles minimum d'ARN CRISPR (ARNcr) aide à la détection de diverses cibles d'acide nucléique à l'aide d'une dégénérescence de séquence. Par exemple, des ensembles d'ARNcr de Cas13a dégénérés candidats ont été conçus pour la détection de diverses cibles d'ARN (virus Lassa). Un algorithme (RuleFit) d'apprentissage automatique (ML) par arbre de décision a été appliqué pour définir les attributs supérieurs qui déterminent la spécificité des ARNcr dégénérés pour déclencher une activité nucléasique collatérale. Cette approche ML générale peut être appliquée à la conception d'ensembles d'ARNcr dégénérés pour n'importe quel système CRISPR/Cas.