Methods for identifying chromosomal spatial instability such as homologous repair deficiency in low coverage next-generation sequencing data

A computer-based method of determining a homologous recombination deficiency (HRD) status of a subject DNA sample, the method comprising:

• obtaining a set of sequencing reads of the whole genome of the subject DNA sample to be analyzed,
• aligning the set of sequencing reads of the subject DNA sample to a reference genome, wherein the reference genome is divided into a plurality of bins, each bin belonging to the same genomic region from a chromosome arm in the whole genome chromosomes to be analyzed,
• counting and normalizing the number of aligned reads in each bin along each chromosome arm to obtain a coverage signal on the chromosome arm,
• arranging the coverage signals of the chromosome arms into a coverage data signal array for the subject DNA sample,
• inputting the coverage data signal array to a trained machine learning model, wherein the model has been trained using a set of samples of known homologous recombination deficiency status to distinguish between the coverage data signal array from samples with a positive homologous recombination deficiency status and the coverage data signal array from samples with a negative homologous recombination deficiency status,
• thereby determining a homologous recombination deficiency score (HRD score) of the subject DNA sample, and
• determining a negative, a positive or an uncertain homologous recombination deficiency (HRD) status of the subject DNA sample according to the HRD score out of the trained machine learning model.