公开(公告)号：US20230313271A1

公开(公告)日：2023-10-05

申请号：US18172821

申请日：2023-02-22

申请人： Illumina, Inc. ,  Illumina Cambridge Limited

发明人： Steven Norberg ,  Luis Fernando Camarillo Guerrero ,  Colin Brown ,  Andrea Manzo ,  Sarah E. Shultzaberger ,  Michael Eberle ,  Sepideh Almasi ,  Suzanne Rohrback ,  Pascale Mathonet ,  Egor Dolzhenko

IPC分类号： C12Q1/6809 ,  G16C20/70

CPC分类号： C12Q1/6809 ,  G16C20/70

摘要： This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning to determine factors or scores indicating an error level with which a given methylation assay detects methylation of cytosine bases. For instance, the disclosed systems use a machine-learning model to generate a bias score indicating a degree to which a given methylation assay errs in detecting cytosine methylation when specific sequence contexts surround such cytosines compared to other sequence contexts. The machine-learning model may take various forms of models, including a decision-tree model, a neural network, or a combination of a decision-tree model and a neural network. In some cases, the disclosed system combines or uses bias scores from multiple machine-learning models to generate a consensus bias score.

公开(公告)号：US20230340571A1

公开(公告)日：2023-10-26

申请号：US18307482

申请日：2023-04-26

申请人： Illumina, Inc. ,  Illumina Software, Inc.

发明人： Sepideh Almasi ,  Yong Li ,  Anindita Dutta ,  Eric Vermaas ,  Rigoberto Pantoja

IPC分类号： C12Q1/6811 ,  C12Q1/6874 ,  G16B25/20

CPC分类号： C12Q1/6811 ,  C12Q1/6874 ,  G16B25/20 ,  C12Q2600/156

摘要： This disclosure describes methods, non-transitory computer readable media, and systems that can use a machine-learning model to classify or predict a probability of an oligonucleotide probe yielding an accurate genotype call or hybridizing with a target oligonucleotide—based on the oligonucleotide probe's nucleotide-sequence composition. To intelligently identify oligonucleotide probes that are more likely to yield accurate downstream genotyping—or more likely to successfully hybridize with target oligonucleotides—some embodiments of the disclosed machine-learning model include customized layers trained to detect motifs or other nucleotide-sequence patterns that correlate with favorable or unfavorable probe accuracy. By intelligently processing the nucleotide sequences of candidate oligonucleotide probes before implementing a microarray for a particular target oligonucleotide, the disclosed system can identify oligonucleotide probes with better genotyping accuracy (or better binding accuracy) than existing microarray systems for use in a microarray.