Granted Patent B2

US 12,619,875 · App. 18/513,367 · Granted May 5, 2026

Inventors: Sofia Kyriazopoulou Panagiotopoulou (Redwood City, CA); Kai-How Farh (Hillsborough, CA)

Assignee: Illumina, Inc.

G06N3/08G06N3/082G06N3/084G06N20/20G16B40/00G06N3/044G06N3/045

Abstract

Claims (58)

1 . A system comprising:

at least one processor; and

a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:

access a nucleotide sequence corresponding to a promoter region and comprising nucleotides at target base positions and nucleotide bases at flanking base positions that flank each target base position;

generate, from the nucleotide sequence corresponding to the promoter region, alternative nucleotide sequences comprising nucleotide variants substituted for the nucleotides at the target base positions;

provide, as an input to one or more layers of a trained pathogenicity classifier, encoded data representing the alternative nucleotide sequences corresponding to the promoter region;

iteratively process, utilizing one or more blocks of the trained pathogenicity classifier in a single inference invocation, the encoded data representing the alternative nucleotide sequences comprising the nucleotide variants at the target base positions of the nucleotide sequence;

generate, for the single inference invocation by the trained pathogenicity classifier, predictions that the nucleotide variants are benign or pathogenic at each target position of the target base positions of the nucleotide sequence based on the nucleotide bases at the flanking base positions; and

classify, based on the predictions, a nucleotide variant as pathogenic.

2 . The system of claim 1 , wherein the trained pathogenicity classifier comprises a neural network.

3 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, by the trained pathogenicity classifier, the predictions by generating a pathogenicity likelihood score that each of three nucleotide variants at the target base positions of the alternative nucleotide sequences is benign or pathogenic.

4 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to:

access, for the target base positions of the nucleotide sequence corresponding to the promoter region, one or more of a protein binding affinity score, signal for deoxyribonucleic acid (DNA) methylation changes, a signal for histone modifications, a signal for noncoding ribonucleic acid (ncRNA) expression, a signal for chromatin structural changes, a signal for deoxyribonuclease (DNase), or a signal for histone 3 lysine 27 acetylation (H3K27ac); and

generate, by the trained pathogenicity classifier, the predictions that the nucleotide variants are benign or pathogenic at each target base position of the nucleotide sequence based further on one or more of the protein binding affinity score, the signal for DNA methylation changes, the signal for histone modifications, the signal for histone modifications, the signal for ncRNA expression, the signal for chromatin structural changes, the signal for DNase, or the signal for H3K27ac.

5 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the nucleotide sequence corresponding to the promoter region as the input to the trained pathogenicity classifier by providing an encoded representation of the nucleotide sequence as input to the trained pathogenicity classifier.

6 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, by the trained pathogenicity classifier, the predictions by:

determining a sequence motif within the nucleotide bases at the flanking base positions; and

generate the predictions based on the sequence motif within the nucleotide bases at the flanking base positions.

7 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, by the trained pathogenicity classifier, the predictions by:

determining a trinucleotide context for the nucleotide variants within the nucleotide bases at the flanking base positions; and

generate the predictions based on the trinucleotide context within the nucleotide bases at the flanking base positions.

8 . The system of claim 1 , further comprising instructions that, when executed by the at least one processor, cause the system to:

access the nucleotide sequence corresponding to the promoter region comprising single nucleotide variant (SNVs) at the target base positions; and

generate, by the trained pathogenicity classifier, the predictions that the SNVs are benign or pathogenic at the target base positions.

9 . The system of claim 1 , wherein the nucleotide bases at the flanking base positions comprise reference bases from a reference genome.

10 . The system of claim 1 , wherein the trained pathogenicity classifier has been trained utilizing input promoter training sequences comprising one or more observed pSNVs sampled from pSNVs identified within a sample genomic dataset and one or more unobserved pSNVs sampled from a pool of substitutionally generated pSNVs at base positions for which pSNVs are not identified within the sample genomic dataset.

11 . A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause a system to:

access a nucleotide sequence corresponding to a promoter region and comprising nucleotides at target base positions and nucleotide bases at flanking base positions that flank each target base position;

generate, from the nucleotide sequence corresponding to the promoter region, alternative nucleotide sequences comprising nucleotide variants substituted for the nucleotides at the target base positions;

provide, as an input to one or more layers of a trained pathogenicity classifier, encoded data representing the alternative nucleotide sequences corresponding to the promoter region;

iteratively process, utilizing one or more blocks of the trained pathogenicity classifier in a single inference invocation, the encoded data representing the alternative nucleotide sequences comprising the nucleotide variants at the target base positions of the nucleotide sequence;

generate, for the single inference invocation by the trained pathogenicity classifier, predictions that the nucleotide variants are benign or pathogenic at each target position of the target base positions of the nucleotide sequence based on the nucleotide bases at the flanking base positions; and

classify, based on the predictions, a nucleotide variant as pathogenic.

12 . The non-transitory computer readable medium of claim 11 , wherein the trained pathogenicity classifier comprises a neural network.

13 . The non-transitory computer readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, by the trained pathogenicity classifier, the predictions by generating a pathogenicity likelihood score that each of three nucleotide variants at the target base positions of the alternative nucleotide sequences is benign or pathogenic.

14 . The non-transitory computer readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the system to:

access, for the target base positions of the nucleotide sequence corresponding to the promoter region, one or more of a protein binding affinity score, signal for deoxyribonucleic acid (DNA) methylation changes, a signal for histone modifications, a signal for noncoding ribonucleic acid (ncRNA) expression, a signal for chromatin structural changes, a signal for deoxyribonuclease (DNase), or a signal for histone 3 lysine 27 acetylation (H3K27ac); and

generate, by the trained pathogenicity classifier, the predictions that the nucleotide variants are benign or pathogenic at the each target base position of the nucleotide sequence based further on one or more of the protein binding affinity score, the signal for DNA methylation changes, the signal for histone modifications, the signal for histone modifications, the signal for ncRNA expression, the signal for chromatin structural changes, the signal for DNase, or the signal for H3K27ac.

15 . The non-transitory computer readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the nucleotide sequence corresponding to the promoter region as the input to the trained pathogenicity classifier by providing an encoded representation of the nucleotide sequence as input to the trained pathogenicity classifier.

16 . The non-transitory computer readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, by the trained pathogenicity classifier, the predictions by:

determining a sequence motif within the nucleotide bases at the flanking base positions; and

generate the predictions based on the sequence motif within the nucleotide bases at the flanking base positions.

17 . The non-transitory computer readable medium of claim 11 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, by the trained pathogenicity classifier, the predictions by:

determining a sequence motif within the nucleotide bases at the flanking base positions; and

generate the predictions based on the sequence motif within the nucleotide bases at the flanking base positions.

18 . A computer-implemented method comprising:

accessing a nucleotide sequence corresponding to a promoter region and comprising nucleotides at target base positions and nucleotide bases at flanking base positions that flank each target base position;

generating, from the nucleotide sequence corresponding to the promoter region, alternative nucleotide sequences comprising nucleotide variants substituted for the nucleotides at the target base positions;

providing, as an input to one or more layers of a trained pathogenicity classifier, encoded data representing the alternative nucleotide sequences corresponding to the promoter region;

iteratively processing, utilizing one or more blocks of the trained pathogenicity classifier in a single inference invocation, the encoded data representing the alternative nucleotide sequences comprising the nucleotide variants at the target base positions of the nucleotide sequence;

generating, for the single inference invocation by the trained pathogenicity classifier, predictions that the nucleotide variants are benign or pathogenic at each target position of the target base positions of the nucleotide sequence based on the nucleotide bases at the flanking base positions; and

classifying, based on the predictions, a nucleotide variant as pathogenic.

19 . The computer-implemented method of claim 18 , wherein generating, by the trained pathogenicity classifier, the predictions comprises:

determining a trinucleotide context for the nucleotide variants within the nucleotide bases at the flanking base positions; and

generate the predictions based on the trinucleotide context within the nucleotide bases at the flanking base positions.

20 . The computer-implemented method of claim 18 , further comprising:

accessing the nucleotide sequence corresponding to the promoter region comprising single nucleotide variants (SNVs) at the target base positions; and

generating, by the trained pathogenicity classifier, the predictions that the SNVs are benign or pathogenic at the target base positions.

Assignments (1)

Continuity (8)

References Cited (308)