Abstract: Methods and systems disclosed herein can improve analysis capabilities of genomic materials. The methods provided herein may examine transcription factor binding site accessibility to diagnose a disease or monitor progression of a disease in a subject.

Abstract: Methods and systems disclosed herein can improve inference of gene expression using cell-free DNA fragments. In an aspect, the present disclosure provides a computer-implemented method for inferring gene expression, the method comprising: obtaining a biological sample from a subject; extracting cell-free deoxyribonucleic acid (cfDNA) from the biological sample, wherein the cfDNA comprises a plurality of cfDNA fragments; performing a sequencing assay on the plurality of cfDNA fragments to generate a plurality of cfDNA sequencing fragments; computer processing the plurality of cfDNA sequencing fragments; and calculating, based at least in part on the computer processing, a gene expression score for a gene in a plurality of genes, wherein the gene expression score indicates a probability of expression or non-expression of the gene in the plurality of genes.

Abstract: The present disclosure provides methods and systems for screening or detecting a tumor or following disease progression that may be applied to cell-free nucleic acids, such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, detecting proliferative cell disorder-associated signals in the sequencing information, and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer, or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or cancer development.

Abstract: The present disclosure provides methods and systems for screening or detecting a tumor or following disease progression that may be applied to cell-free nucleic acids, such as cell-free DNA. The method may use detection of methylation signals within a single sequencing read in identified genomic regions as input features to train a machine learning model and generate a classifier useful for stratifying populations of individuals. The method may comprise extracting DNA from a cell-free sample obtained from a subject, converting the DNA for methylation sequencing, generating sequencing reads, detecting proliferative cell disorder-associated signals in the sequencing information, and training a machine learning model to provide a discriminator capable of distinguishing groups in a subject population such as healthy, cancer, or distinguishing disease subtype or stage. The method may be used for, e.g., predicting, prognosticating, and/or monitoring response to treatment, tumor load, relapse, or cancer development.

Abstract: Methods and systems disclosed herein can improve analysis capabilities of genomic materials. The methods provided herein may examine transcription factor binding site accessibility to diagnose a disease or monitor progression of a disease in a subject.