Predicting effects of gene regulatory sequences on endophenotypes using machine learning
AssigneeInari Agriculture Technology, Inc.
InventorsRoss Everett ALTMAN, Karl Anton Grothe KREMLING
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Abstract
A method for generating a gene regulatory sequence with a desired endophenotype profile includes obtaining a plurality of gene regulatory sequences and inputting the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes. The method further includes selecting one or more desired endophenotypes based on the plurality of endophenotypes and selecting a gene regulatory sequence in accordance with the one or more desired endophenotypes.
Claims32
A method of modifying an endophenotype in a plant, the method comprising, by one or more computing devices:
- obtaining a plurality of gene regulatory sequences;
- inputting the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes;
- selecting one or more desired endophenotypes based on the plurality of endophenotypes;
- selecting a gene regulatory sequence in accordance with the one or more desired endophenotypes, and
- introducing the selected gene regulatory sequence into the plant, thereby modifying the endophenotype of the plant.
A method for generating a gene regulatory sequence with a desired endophenotype profile, the method comprising, by one or more computing devices:
- obtaining a plurality of gene regulatory sequences;
- inputting the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes;
- selecting one or more desired endophenotypes based on the plurality of endophenotypes;
- and
- selecting a gene regulatory sequence in accordance with the one or more desired endophenotypes.
(canceled)
A system including one or more computing devices, comprising:
- one or more non-transitory computer-readable storage media including instructions;
- and
- one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions to:
- obtain a plurality of gene regulatory sequences;
- input the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes;
- select one or more desired endophenotypes based on the plurality of endophenotypes;
- and
- select a gene regulatory sequence in accordance with the one or more desired endophenotypes.
-42. (canceled)
A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of one or more computing devices, cause the one or more processors to:
- obtain a plurality of gene regulatory sequences;
- input the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes;
- select one or more desired endophenotypes based on the plurality of endophenotypes;
- and
- select a gene regulatory sequence in accordance with the one or more desired endophenotypes.
-53. (canceled)
A method for predicting the effect of a mutated gene regulatory sequence the method comprising, by one or more computing devices:
- inputting a plurality of gene regulatory sequences to a first trained machine-learning model, the plurality of gene regulatory sequences comprising one or more mutated gene regulatory sequences;
- utilizing the first trained machine-learning model to generate a first set of gene-level endophenotype profiles based on the plurality of gene regulatory sequences, comprising cis regulatory effects of the one or more mutated gene regulatory sequences;
- inputting the first set of gene-level endophenotype profiles to a second trained machine-learning model;
- and
- utilizing the second trained machine-learning model to generate a second set of gene-level endophenotype profiles based on the first set of gene-level endophenotype profiles, wherein generating the second set of gene-level endophenotype profiles comprises predicting one or more updated gene-level endophenotype profiles based on the plurality of gene regulatory sequences including the trans regulatory effects of the one or more mutated gene regulatory sequences.
-69. (canceled)
-88. (canceled)
Dependencies are inferred from the claim text.