DNA Analysis Methods

DNA alignment and error correction involves aligning sequencing reads derived from DNA libraries to a reference genome and correcting errors in the sequencing reads prior to variant calling.

DRAGEN unique molecular identifier (UMI) error correction comprises three main steps:

1.

DRAGEN UMI uses its HW accelerated mapper (based on a hash table implementation) to align DNA sequences in FASTQ files to the hg19 reference genome. These alignments are not written to a BAM.

2.

The raw alignments are processed to remove errors, including errors introduced during FFPE preservation, PCR amplification, and sequencing. Reads from the same original DNA molecule are tagged with the same UMI during library preparation. The UMI allows DRAGEN to compare related reads, remove outlier signals, and collapse multiple reads into a single high-quality sequence. Read collapsing adds the following BAM tags:

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RX/XU—UMI.

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XV—Number of reads in the family.

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XW—Number of reads in the duplex-family or 0 if not a duplex family.

3.

DRAGEN performs a final alignment step on the UMI-collapsed reads. These final alignments are then written to a BAM file and a corresponding BAM index file is created.

DRAGEN continues to use these final alignments as input for gene amplification (copy number) calling, small variant calling (SNV, indel, MNV, delin), microsatellite instability (MSI) status determination, and DNA library quality control.

DRAGEN supports calling SNVs, indels, MNVs, and delins in tumor-only samples by using mapped and aligned DNA reads from a tumor sample as input. Variants are detected via both column wise pileup analysis and local de novo assembly of haplotypes. The de novo haplotypes allow the detection of much larger insertions and deletions than possible through column wise pileup analysis only. DRAGEN insertions and deletions are validated with lengths of at least 0–25 bp and more than 25 bp can be supported. In addition, DRAGEN also uses the de novo assembly to detect SNVs, insertions, and deletions that are co-phased and part of the same haplotypes. Any such co-phased variants that are within a window of 15 bp can then be reassembled into complex variants (MNVs and delins). The tumor-only pipeline produces a VCF file containing both germline and somatic variants that can be further analyzed to identify tumor mutations. The pipeline makes no ploidy assumptions, enabling detection of low-frequency alleles.

DRAGEN small variant calling includes the following steps:

1.

Detects regions with sufficient read coverage (callable regions).

2.

Detects regions where the reads deviate from the reference and there is a possibility of a germline or somatic call (active regions).

3.

Assembles de novograph haplotypes are assembled from reads (haplotype assembly).

4.

Extracts possible somatic or germline calls (events) from column wise pileup analysis.

5.

Calibrates read base qualities to account for FFPE noise.

6.

Computes read likelihoods for each read/haplotype pair.

7.

Performs mutation calling by summing the genotype probabilities across all reads/haplotype pairs.

8.

Performs additional filtering to improve variant calling accuracy, including using a systematic noise file. The systematic noise file indicates the statistical probability of noise at specific positions in the genome. This noise file is constructed using clean (normal) samples. Regions where noise is common (eg, difficult to map regions) have higher noise values. The small variant caller penalizes those regions to reduce the probability of making false positive calls.

The DRAGEN copy number variant caller performs amplification, reference, and deletion calling for CNV targets within the assay. It counts the coverage of each target interval on the panel, uses a preprocessed panel of normal samples to normalize target counts, corrects for GC coverage bias, and calculates scores of a CNV event from observed coverage and makes copy number calls.

The BRCA large rearrangement step generates segmentation of the BRCA1 and BRCA2 genes for exon-level CNV detection from the BAM file. Using the same method as CNV calling, the large rearrangement component counts coverage of each target interval of the panel, performs normalization, and calculates the fold change values for each probe across the BRCA genes. Normalization includes GC bias correction, sequencing depth, and probe efficiency using a collection of normal FFPE and genomic DNA samples. Initial segmentation is performed for each gene with circular binary segmentation. The merging of segments is then determined by amplitude, noise, and variance at adjacent segments using thresholds established with in silico data. A large rearrangement is reported for genes with more than one segment. Coordinates of the exon-level CNV and the log2 mean fold change for each of the BRCA gene segments are found in the *_DragenExonCNV.json file.

The Illumina Annotation Engine performs annotation of small variants, CNVs, and exon-level CNVs. The inputs are gVCF files and the outputs are annotated JSON files.

The Illumina Annotation Engine processes each variant entry and annotates with available information from databases such as dbSNP, gnomAD genome and exome, 1000 genomes, ClinVar, COSMIC, RefSeq, and Ensembl. The header includes version information and general details. Each annotated variant is included as a nested dictionary structure in separate lines following the header.

DRAGEN is used to compute tumor mutational burden (TMB) in coding regions where there is sufficient coverage.

The following variants are excluded from the TMB calculation:

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Non-PASS variants.

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Mitochondrial variants.

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MNVs.

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Variants that do not meet a minimum depth threshold.

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Variants that do not meet the minimum variant allele threshold.

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Variants that fall outside the eligible regions.

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Tumor driver mutations. Variants with a population allele count ≥ 50 are treated as tumor driver mutations.

Germline variants are not counted towards TMB. Variants are determined as germline based on a database and a proxy filter.

Variants with a population allele count ≥ 10 that are observed in either the 1000 Genomes or gnomAD databases are marked as germline. MNVs, which do not count towards TMB, may be marked as germline when all their component small variants are marked as germline. The proxy filter scans the variants surrounding a specific variant and identifies those variants with similar variant allele frequencies (VAF). If the majority of surrounding variants of similar VAF are germline, then the variant is also marked as germline.

The formula for TMB calculation is:

Outputs are captured in a *_TMB_Trace.tsv file that contains information on variants used in the TMB calculation and a *.tmb.json file that contains the TMB score calculation and configuration details.

DRAGEN can determine the MSI status of a sample. It uses a normal reference file, which was created from a set of normal samples. During sequencing, normal reference files are generated by tabulating read counts for each microsatellite site. The normal file contains the read count distribution for each microsatellite.

MSI calling for a tumor-only sample is performed by first tabulating tumor counts from the read alignments for each microsatellite site. Then, the Jensen-Shannon distance (JSD) is calculated between each pair of tumor and normal baseline samples. DRAGEN determines unstable sites by performing Chi-square testing of tumor JSD and normal JSD distributions. Unstable sites are called if the mean distance difference of the two JSD distributions is ≥to the distance threshold and Chi-square p-value is ≤ to the p-value threshold. Lastly, DRAGEN produces an MSI status given assessed site count, unstable site count, the percentage of unstable sites in all assessed sites, and the sum of the Jensen-Shannon distance of all the unstable sites.

Genomic instability score (GIS) is a whole genome signature for homologous recombination deficiency. The GIS is composed of the sum of three components: loss of heterozygosity, telomeric allele imbalance, and large-scale state transition. These components are estimated using the GIS algorithm contracted from Myriad Genetics, which uses an input of the b-allele frequency and coverage across a genome-wide single nucleotide panel. A panel of normal samples is used for both bias reduction and normalization prior to GIS estimation. Final GIS results can be found in the *.gis.json file.

The contamination analysis step detects foreign human DNA contamination using the SNP error file and pileup file that are generated during the small variant calling and the TMB trace file. The software determines whether a sample has foreign DNA using the contamination score. In contaminated samples, the variant allele frequencies in SNPs shift from the expected values of 0%, 50%, or 100%. The algorithm collects all positions that overlap with common SNPs that have variant allele frequencies of < 25% or > 75%. Then, the algorithm computes the likelihood that the positions are an error or a real mutation. The contamination score is the sum of all the log likelihood scores across the predefined SNP positions with minor allele frequency < 25% in the sample and are not likely due to CNV events.

The larger the contamination score, the more likely there is foreign DNA contamination. A sample is considered to be contaminated if the contamination score is above predefined quality threshold. The contamination score was found to be high in samples with highly rearranged genomes or HRD samples. 1% of HRD samples found to be above the threshold with no evidence for actual contamination.