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OwnerPaul M. Lizardiinventor
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Methods for genome-wide DNA methylation analysis at single-cell resolution are provided. In some forms, biological samples to be analyzed contain multiple cell types, and individual cells are present in multiple regulatory states. The methods obtain single-cell epigenetic information including repetitive DNA elements, and provide full sets of 46 chromosomes corresponding to specific cell regulatory states. The methods include grouping assembly performed by alignment of long DNA sequences containing linearly contiguous DNA methylation information for entire chromosomes, guided by DNA methylation sequence information present in single-cell Methyl-seq data. Grouping assembly generates a multiplicity of distinct full sets of chromosome complements. The methods generate improved single-cell Methyl-seq data sets, augmented by additional information that accurately maps the DNA methylation states of all repetitive elements. The methods include data-acquisition and AI-based transformation of the acquired data.
A method comprising mapping single-cell-resolved DNA methylation sequence data to a set of epi-Haplotype Chromosomes (eHCs) to group the different eHCs in the set of eHCs into single-cell-resolved subsets of eHCs (epi-Haplotype Genomes), wherein each epi-Haplotype Genome (eHG) corresponds to the full chromosome complement of respective different single cells represented in the single-cell-resolved DNA methylation sequence data, wherein the single-cell-resolved DNA methylation sequence data are divided into sets of reads, wherein the reads in each set of reads correspond to respective different single cells, cell types, and/or cell regulatory states represented in the single-cell-resolved DNA methylation sequence data,
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A method comprising analyzing a test omics data set derived from a test sample using a deep learning (DL) model, wherein the DL model has been trained on one or more first omics data set(s) from one or more first sample(s), and one or more second omics data set(s) from one or more second sample(s),
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