Illumina TruPath Genome Pilepine

DRAGEN’s Germline pipeline integrates proximity mapped reads from the Illumina TruPath Genome prep to enhance genomic analysis using long-range information encoded on the flowcell. This proximity-aware workflow supports highly accurate read mapping, phasing, and variant detection, including structural variants, paralog‑resolved small variants, short tandem repeat (STR) genotyping, and colocation analysis. By modeling and applying read‑to‑read linkage probabilities, the pipeline enables more confident interpretation of complex and low‑mappability genomic regions using standard short‑read data.

Summary

Integrated TruPath proximity mapping: Enabling --enable-proximity=true activates proximity-aware modeling and analysis across the DRAGEN Germline pipeline, allowing reads that are spatially close on the flowcell to be probabilistically linked as originating from the same DNA template.
Proximity model-driven mapping and alignment: DRAGEN performs a preliminary mapping pass to collect high‑confidence alignments and fits a non‑linear proximity linking model that relates flowcell spatial distance and genomic distance to read‑to‑read linkage probability. The resulting Phred‑scaled linkage probability lookup table is applied during map/align to resolve ambiguous mappings and improve read placement accuracy in repetitive and complex genomic regions.
Enhanced phasing support: Proximity information strengthens read phasing by associating reads from the same original template molecule, enabling longer and more reliable phasing blocks that propagate into variant calling and assembly‑based analyses.
Structural variant calling: The Germline SV caller leverages proximity‑derived phasing to support phased assemblies, haplotype‑aware machine‑learning features, and haplotype‑resolved genotyping for single‑sample TruPath whole‑genome analyses.
Haplotype‑resolved small variant detection in paralogs: For clinically relevant paralogous regions, Multi‑Region Joint Detection (MRJD) estimates total copy number from read depth, reconstructs individual paralog copies using read sequences and proximity information, assigns each copy to a genomic region or haplotype, and calls small variants from the reconstructed copies.
STR genotyping with IRR recovery: Proximity linking enables recovery and placement of in‑repeat reads (IRRs) that would otherwise be unmapped, improving detection and sizing of large STR expansions and supporting phasing‑aware genotyping.
Colocation analysis and filtering: Colocation maps summarize long-range genomic interactions using proximity‑linked reads and are used to visualize structural features and filter SV breakends lacking proximity support.
Specialized outputs and reporting: The pipeline generates proximity‑aware BAM/CRAM files, VCFs, JSON summaries, cooler files, and TruPath‑specific DRAGEN Reports with dedicated QC metrics and visualizations.

Overview

Short‑read DNA sequencing typically captures genomic variation at high accuracy but lacks long-range context needed to confidently resolve complex regions such as repeats, paralogs, and structural variants. The Illumina TruPath Genome Prep encodes long-range molecular information directly on the flowcell by preserving spatial proximity between reads derived from the same original DNA molecule. When combined with DRAGEN’s proximity‑aware algorithms, this information enables long-range analysis that extends the power of standard short‑read data.

The DRAGEN Germline pipeline for Illumina TruPath Genome leverages this flowcell‑encoded proximity information through a probabilistic proximity linking model that assigns read‑to‑read linkage probabilities based on spatial and genomic distance. When proximity mode is enabled, DRAGEN automatically fits this model, generates Phred‑scaled proximity link probability distributions, and applies them across mapping, phasing, and variant calling workflows. These proximity linkage probabilities serve as a foundational signal reused throughout the pipeline—informing alignment scoring, phasing blocks, candidate assemblies, machine‑learning features, and variant filtering—to improve accuracy and confidence in repetitive and structurally complex genomic regions while remaining compatible with standard short‑read sequencing workflows and formats.

Proximity Mode Analysis in DRAGEN

When proximity mode is enabled, DRAGEN automatically performs additional modeling and downstream analyses that integrate proximity information throughout the Germline pipeline. TruPath‑specific proximity analysis is activated by enabling proximity during a DRAGEN Germline run setting --enable-proximity=true. This proximity‑aware processing supports the following workflow and features:

High‑accuracy read mapping using linkage‑informed alignment scoring
Enhanced phasing via read‑to‑template association
Structural variant calling using phased assemblies and haplotype‑aware algorithms
Paralog‑resolved small variant detection with Multi‑Region Joint Detection (MRJD)
Improved STR genotyping through in‑repeat read (IRR) recovery
Long-range genomic interaction analysis and SV filtering using colocation maps

Key Benefits of TruPath Genome vs Standard Illumina SBS

When DRAGEN Germline with proximity mode is enabled for TruPath Genome data, improvements are observed relative to standard Illumina SBS inputs across multiple performance dimensions.

These include improved small variant calling accuracy, longer phasing blocks, a higher proportion of fully phased genes, and improved structural variant recall. The table below summarizes key performance metrics across TruPath and standard Illumina SBS datasets.

Benefit

TruPath, high molecular weight input DNA

TruPath, standard molecular weight input DNA

Standard Illumina SBS on DRAGEN 4.4

Best-in-class small variant calling performance

36,717 FP+FN

40,267 FP+FN

61,288

Multi-megabase phasing blocks

8.1 Mbp

649 kbp

Fully phased genes

98.4%

87.6%

Improved SV recall

94.0%

93.7%

80.7%

Phased, High-Quality Small Variant Calls in Clinically Relevant Gene Families

TruPath proximity-aware analysis enables haplotype‑resolved, copy‑number‑aware small variant calling in ten clinically relevant paralogous gene families using Multi-Region Joint Detection (MRJD), as shown in the table below. With TruPath data, MRJD produces phased variant calls across these supported paralogous regions without reliance on population haplotypes.

Supported Genes

Paralogous Gene

Disease Relevance

PMS2

Lynch Syndrome

SMN1–SMN2

Spinal Muscular Atrophy

NCF1

Chronic Granulomatous Disease

CYP21A2

Congenital Adrenal Hyperplasia

TNXB

Ehlers–Danlos syndrome

STRC

Recessive Nonsyndromic Hearing Loss

CYP2D6

Pharmacogenetics

CYP11B1–CYP11B2

Glucocorticoid-remediable Aldosteronism

CFHR1–CFHR2–CFHR3–CFHR4

Atypical Hemolytic Uremic Syndrome

USP18

Type I Interferonopathy

The figure below illustrates haplotype‑resolved variant calls generated by MRJD for PMS2 and PMS2CL, reported as separate copies for each locus, with long‑read data shown for comparison.

Improved STR Expansion Length and Classification Accuracy

TruPath analysis improves short tandem repeat (STR) expansion length estimation by recovering fragments composed entirely of STR sequence and by applying sequencing efficiency correction to account for locus‑specific coverage bias.

These improvements result in STR length estimates that more closely track expected repeat sizes and support more accurate expansion classification. The figure below compares STR expansion length estimates generated using standard Illumina sequencing and TruPath analysis across multiple loci.

Improved BND Filtering

TruPath proximity information enables more selective filtering of large (>200 kbp) inter‑ and intra‑chromosomal breakend (BND) calls produced by DRAGEN Structural-Variant (SV) Calling.

Incorporating colocation evidence reduces the number of reported large BND events while maintaining recall. This effect is observed for both intra‑chromosomal and inter‑chromosomal BNDs across evaluated samples.

Summarized below is BND recall and reduction in reported intra‑ and inter‑chromosomal BND calls for TruPath Coriell samples (n=45), with and without colocation filtering.

Proximity Linking Model

In Illumina TruPath Genome data, read pairs that are proximal on the flowcell have an increased likelihood of originating from the same original template molecule. To quantify this likelihood, DRAGEN uses a probabilistic proximity linking model that relates genomic distance and flowcell proximity to calculate the probability that two reads originate from the same input DNA molecule.

When DRAGEN is run with --enable-proximity=true, the mapper estimates the parameters of this proximity linking model and generates a link probability distribution for each TruPath FASTQ input. This process consists of three stages: sample collection, proximity analysis, and model fitting, followed by generation of a link probability lookup table.

Sample Collection

To fit the proximity linking model, DRAGEN first collects a representative subset of preliminary alignments from the input data. During an initial mapping pass, alignments are generated in flowcell‑tile-sized batches and reads meeting suitability requirements are retained for proximity analysis.

Eligible preliminary alignments must satisfy the following criteria:

Mapped with MAPQ ≥ 60
Primary alignments
Non‑duplicate reads
For paired‑end data, first‑in‑pair with a mapped mate and proper pairing

DRAGEN continues sampling until one million qualifying preliminary alignments have been collected or until the entire FASTQ input has been processed. If fewer than one million alignments are collected, processing continues with a warning indicating a potentially insufficient sample. If no suitable alignments are found, DRAGEN exits with an error.

Proximity Analysis

Once a sufficient set of preliminary alignments has been collected, DRAGEN analyzes read pairs that are both spatially proximal on the flowcell and genomically proximal on the reference genome. Read pairs meeting both criteria have a high likelihood of originating from the same template molecule.

Each alignment is associated with a mapped genomic position and a flowcell coordinate (X, Y). For candidate read pairs, DRAGEN computes:

Spatial displacement on the flowcell, represented as (XD, YD) in nanometers
Genomic displacement, represented as GDIST in base pairs and rounded to the nearest 1,000 bp

Read pairs whose spatial and genomic displacements fall within configured proximity thresholds are considered likely linked. For these pairs, counts are aggregated across combinations of XD, YD, and GDIST. These aggregated counts form the empirical input to the model fitting stage.

A second set of counts is also collected using read pairs that are spatially proximal but genomically distant. These pairs are assumed to represent chance colocation and are used to model background noise.

Before proceeding, DRAGEN evaluates both sets of counts to ensure the observed trends are consistent with TruPath data. If the data fails validation, DRAGEN exits with an error.

Model Fitting

The proximity linking model is non‑linear and includes approximately 20 parameters that predict the expected number of linked read pairs (N) as a function of XD, YD, and GDIST. The aggregated counts from proximity analysis are submitted to a non‑linear least‑squares solver to estimate these parameters.

If the solver fails to converge, DRAGEN exits with an error. When fitting succeeds, the model enables calculation of the expected number of linked read pairs, $\mu(\text{XD}, \text{YD}, \text{GDIST})$ , which provides a smoothed estimate relative to the empirical counts.

A separate background model estimates the expected number of proximal read pairs due to chance, $(\mu_\text{chance}(\text{XD}, \text{YD}, \text{GDIST})$ . The link probability is then computed as:

$1 - \frac{\mu_\text{chance}(\text{XD}, \text{YD}, \text{GDIST})}{\mu(\text{XD}, \text{YD}, \text{GDIST})}$

This probability is typically expressed on a Phred scale as:

$-10 \log_{10} \left(\frac{\mu_\text{chance}(\text{XD}, \text{YD}, \text{GDIST})}{\mu(\text{XD}, \text{YD}, \text{GDIST})}\right)$

Higher values indicate a stronger likelihood that two reads originated from the same template molecule.

Link Probability Distribution Generation

After successful model fitting, DRAGEN evaluates the fitted model across the practical range of spatial and genomic displacements and stores the resulting link probabilities in a lookup table. The table is generated continuously until link probabilities fall below a minimum threshold.

This lookup table represents the primary output of the TruPath proximity linking model and is used downstream by the DRAGEN Germline pipeline to incorporate proximity information during mapping, template tagging, and variant calling.

In rare cases where the fitted model fails to produce meaningful link probabilities above the minimum threshold, an empty lookup table is generated and DRAGEN exits with an error.

Map/Align

The proximity linking model is used during mapping to improve read alignment accuracy for TruPath samples. In regions of high sequence homology, standard Illumina sequencing reads may align equally well, or nearly so, to multiple genomic locations, resulting in ambiguous mappings. With TruPath data, proximity‑linked read pairs can provide additional context that enables both reads in a pair to be mapped uniquely.

Read pairs originating from a region of interest on the flowcell are processed through the standard mapping workflow. Multiple candidate alignments are generated and scored, and key attributes—including alignment score, genomic position, and flowcell position—are stored in an indexed data structure.

For each read pair X that may benefit from proximity information, the mapper revisits the candidate alignments and searches the data structure for other read pairs Y whose alignment and flowcell positions suggest a shared template of origin. The proximity linking model quantifies the likelihood that X and Y originated from the same original DNA molecule. A Phred‑scaled score derived from this likelihood is incorporated into the corresponding joint alignment hypothesis.

Template Tagging

During alignment, the mapper assigns each read a set of link probability scores that estimate the likelihood of links between the read and other nearby reads on the flowcell. Template tagging uses these scores to reconstruct the original template DNA molecules from which paired reads originated.

Template tagging begins by grouping reads into fragments, where each fragment consists of a paired‑end read pair. For each fragment, outgoing link probability scores are collected from the constituent reads. Links with Phred‑scaled quality below the threshold specified by --proximity-min-linkq-threshold (default: 10) are discarded.

The remaining high‑quality links are used to connect fragments into templates. Each connected set of fragments represents a reconstructed template molecule. All reads assigned to the same template are annotated with a shared template identifier in the BAM file (BX:Z), allowing reads originating from the same original DNA molecule to be identified downstream.

Outputs

Template tagging generates a set of metrics reports that describe characteristics of all discovered templates and links identified during the DRAGEN run. Reports are produced for whole‑genome data and for any specified QC regions.

A template or link is included in QC region metrics if any portion of its genomic span overlaps the QC region.

Template Metrics

Template Subpair Count Report

The template subpair count report,<prefix>.<qc-region>_template_subpairs.csv, summarizes the distribution of discovered templates by the number of fragments (subpairs) they contain. A subpair refers to a read‑pair fragment within a template.

Each record in the report describes the number of templates observed with a given fragment count and the corresponding percentage of all templates. Summary statistics, including the mean and selected percentiles of subpair counts, are also reported. Example summary statistics include the mean subpair count and the 25th, 50th, 75th, and 95th percentile subpair counts across all templates.

Template Genomic Distance Report

The template genomic distance report, <prefix>.<qc-region>_template_gdist.csv, describes the distribution of template genomic lengths from the 0th to the 100th percentile.

Template genomic length is defined as the genomic distance between the smallest and largest mapped genomic positions represented in the template, corresponding to the span from the start of the first fragment to the end of the last fragment.

Percentile values are interpolated from the distribution of all discovered template lengths and may therefore be non‑integer base‑pair values.

Template Spatial Distance Reports

Template spatial distance reports describe the distribution of template spatial extents in flowcell units (FCU) from the 0th to the 100th percentile. Two reports are generated:

<prefix>.<qc-region>_template_xdist.csv, describing spatial extent along the flowcell X axis
<prefix>.<qc-region>_template_ydist.csv, describing spatial extent along the flowcell Y axis

Template spatial length is defined as the distance between the smallest and largest flowcell coordinates represented in the template along the corresponding axis. As with genomic distances, percentile values are interpolated from the observed distribution and may be non‑integer FCU values.

Template Length Thresholds Report

The template length thresholds report,<prefix>.<qc-region>_template_thresholds.csv, summarizes the count and proportion of discovered templates whose genomic lengths exceed specified thresholds.

Template genomic length is defined as the span between the smallest and largest mapped genomic positions within a template.

Thresholds reported in this file are defined using the --template-gdist-thresholds option (default: 10000, 20000, 60000). Each record reports the threshold value, the number of templates meeting or exceeding that threshold, and the corresponding proportion of all discovered templates.

Link Metrics

Link metrics are generated for each Phred‑scaled link quality threshold specified at runtime. These thresholds control which links are considered when computing proximity‑based metrics.

The following options determine link metric generation:

--proximity-min-linkq-threshold
- Specifies the primary link quality threshold used to accept or reject link hypotheses during template tagging (default: 10).
--proximity-additional-linkq-thresholds
- Specifies up to two additional link quality thresholds at which link metrics are computed (default: 25).

Link Genomic Distance Report

The link genomic distance report,<prefix>.<qc-region>_proximity_gdist.csv, describes the distribution of genomic distances for links that meet or exceed a specified link quality threshold.

Link genomic length is defined as the genomic distance between the two fragments connected by the link. Distances are reported from the 0th to the 100th percentile.

Percentile values are interpolated from the distribution of all discovered link lengths and may therefore be non‑integer base‑pair values.

Link Spatial Distance Reports

Link spatial distance reports describe the spatial extent of links in flowcell units (FCU) from the 0th to the 100th percentile. Two reports are generated for each link quality threshold:

<prefix>.<qc-region>_proximity_xdist.csv, reporting spatial extent along the flowcell X axis
<prefix>.<qc-region>_proximity_ydist.csv, reporting spatial extent along the flowcell Y axis

Link spatial length is defined as the distance between the flowcell coordinates of the two fragments connected by the link along the corresponding axis.

As with genomic distance metrics, percentile values are interpolated from the observed distribution and may be non‑integer flowcell unit values.

Phasing

When TruPath data is used, DRAGEN performs read phasing upstream of variant calling and uses haplotype‑phased reads to generate phased variant calls. Phasing is informed by both long‑range proximity linking information provided by the TruPath library preparation and inference of the sample's ancestral haplotypes, which enables robust phasing across long genomic distances.

DRAGEN personalization provides the ancestral component of phasing information by inferring the sample’s ancestral haplotypes, such that phasing is typically inferred to be consistent with that observed in the ancestral haplotypes. As in the standard personalization workflow, DRAGEN also uses variants imputed from the haplotype database to inform prior probabilities for variants in the sample, providing a boost to variant calling performance.

Phasing Model Overview

DRAGEN performs phasing at the level of small, contiguous genomic bins, typically 4,096 bp in length. Within each bin, haplotypes are inferred using the haplotype database in the reference hash table, and reads are assigned accordingly. Proximity linking information is used to propagate phasing information across bins.

Bins are grouped into larger, non‑overlapping phase blocks when there is sufficient evidence of co‑phasing. Each bin is phased in the context of ancestral haplotypes inferred from neighboring bins and from linked reads elsewhere in the genome.

Phasing Options

Phasing is enabled automatically when proximity mode is enabled using --enable-proximity=true. No additional arguments are required. Default settings are recommended, but phasing behavior can be adjusted using the following options:

--personalization-phase-block-threshold
- Controls the amount of evidence required to group adjacent bins into a single phase block (default: 20).
--read-phasing-gene-list
- Specifies an optional GTF file used to compute gene‑based phasing metrics for genes fully contained within phase blocks.

Lowering the phase-block threshold parameter will reduce the amount of co-phasing evidence required to group adjacent personalization bins into a single phase block, and vice versa.

Output Files

BAM/CRAM Output

The phased reads in the map/align output file are annotated with the following tags:

Tag

Description

Values

pp

Phasing probability in Phred-scale log odds: $10 * \log_{10}(P(H_1) / P(H_2))$

$[-127, 127]$

HP

Haplotype tag for all reads where $\|{pp}\| \geq 10$

$1,2$

PS

Phase block tag

$[0,2^{32})$

Personalized Haplotypes

Personalized haplotypes for each phased bin are output in tab-delimited format (TSV). A summary of the phase blocks defined in the TSV file is also written in GTF format.

TSV (`<sample_id>.personal_haplotypes.tsv.gz`)

The personalized haplotypes TSV file contains the following columns:

Column

Description

CHROM

Chromosome name

START

Start position of the phased bin (0-based)

END

End position of the phased bin (1-based)

PHASE_BLOCK

Phase block ID for the bins. Bins with the same IDs are confidently co-phased.

PHASING_CONFIDENCE

Phasing confidence for the bin. Lower confidence values indicate a higher likelihood of haplotype switching.

GTF (`<sample_id>.phase_blocks.gtf.gz`)

Regions covered by the phase blocks, as defined in the personalized TSV file's PHASE_BLOCK column, are also output in a GTF file with the following fields:

Column

Description

seqname

Chromosome name

source

Always 'dragen'

feature

Always 'phaseblock'

start

Start position of the phase block (1-based)

end

End position of the phase block (1-based)

score

Unused ('.')

strand

Unused ('.')

frame

Unused ('.')

attribute

Always 'phase_block n'

Imputed Variants

Imputed variants for each phased bin are output in a VCF file. This VCF contains only variants imputed from the haplotype database in the reference hash table. It does not include novel variants observed in the sample, and multi‑allelic variants are split into separate records.

VCF (`<sample_id>.personal.vcf.gz`)

The VCF follows the 4.2 standard, below is the description of relevant fields:

Tag

Description

QUAL

Phred-scale score for the marginal probability of ALT. For example, for a diploid variant: $-10*log_{10}(P(\text{GT='0}\vert\text{0'}))$

INFO:HAPS

Two best haplotype pairs for the bin the variant belongs to

INFO:PGP

Marginal probability for $P(\text{GT='0}\vert\text{0'}),P(\text{GT='1}\vert\text{0'}) + P(\text{GT='0}\vert\text{1'}),P(\text{GT='1}\vert\text{1'})$

FORMAT:PS

Phase block ID for the bin the variant belongs to

Phasing Metrics

DRAGEN reports a set of phasing metrics for each TruPath analysis and writes them to a summary CSV file. Reported metrics include phase block length statistics (N50, L50, NG50,LG50), cumulative phase block lengths, counts of fully phased genomic windows, and counts of fully phased genes. Gene‑based metrics are reported only when a gene list is provided using --read-phasing-gene-list.

CSV (`<sample_id>.phasing_summary_stats.csv`)

Metric

Description

Phasing chromosomes

A list of the chromosomes used to calculate the metrics. Only autosomes with phased reads are considered.

N50

The length of the shortest phase block where all phase blocks of at least that length account for ≥50% of the cumulative phase block length.

L50

The smallest number of phase blocks that account for 50% of the cumulative phase block length.

NG50

The length of the shortest phase block where all phase blocks of at least that length account for ≥50% of the cumulative length of the phasing chromosome set.

LG50

The smallest number of phase blocks that account for 50% of the cumulative length of the phasing chromosome set.

Total phase block length for L50/N50

The cumulative length of the phase-block assembly.

Total phase block length for LG50/NG50

The cumulative length of the chromosome set.

Number of fully phased 300 kbp windows

After partitioning each chromosome into 300 kbp windows, the number of such windows that are each fully contained within a single phase block.

Number of fully phased genes

The number of genes that are each fully contained within a single phase block.

Gene list

The filename of the gene list used to calculate the number of fully phased genes

Structural Variant Calling

TruPath‑specific structural variant (SV) calling is supported only in single‑sample whole‑genome germline SV discovery mode. DRAGEN‑SV leverages proximity information indirectly through phasing information encoded in the reads, rather than using proximity links directly during SV detection.

This approach provides several key advantages. Candidate regions are assembled separately by haplotype, which reduces assembly graph complexity and produces higher‑quality contigs. Features used by the machine‑learning (ML) model are also segregated by haplotype, enabling improved training and inference. As a result, heterozygous SVs can be distinguished and assigned to specific local haplotypes.

Leveraging TruPath Proximity-Linked Features

DRAGEN‑SV currently incorporates proximity information indirectly by using phasing information during candidate assembly and ML‑based filtering. For best accuracy, ML filtering should remain enabled.

Phased Assembly

Reads collected for candidate assembly are partitioned into two haplotypes based on available phasing information. Each haplotype is assembled independently, resulting in at most one contig per haplotype. Up to two contigs per candidate are propagated through downstream stages of the pipeline.

ML Processing

When run with TruPath data, DRAGEN‑SV uses an ML model trained on TruPath‑derived features that depend on read‑level phasing, in addition to features used with standard Illumina sequencing data. Enabling ML processing is critical for achieving optimal SV calling accuracy.

Collapsing, Deduplication, Regenotyping

Structural variants of certain types, including insertions and deletions, may be produced from multiple phased assembly rounds. These SVs are collapsed and deduplicated when they are inferred to represent the same event before being written to the VCF output. SV type, length, genomic location, genotype scores, and haplotype of origin are used to determine equivalence.

During this process, genotypes may be updated. For example, if a heterozygous SV is produced only from reads phased to the first haplotype, the genotype GT field is set to 1/0. If two SVs originating from different haplotypes are collapsed into a single event, the resulting SV is re‑genotyped as 1/1.

SV VCF Outputs

The following VCF fields are added for TruPath

INFO Fields

Description

PHASEDASM

Haplotype of the reads used for the assembly yielding the SV (only with --enable-proximity=true)

ML_UPDATED

The FILTER status has changed from PASS to non-PASS or non-PASS to PASS after QUAL being recalibrated by ML

FORMAT Fields

Description

MLQS

ML recalibrated QUAL for indels

FILTER Fields

Level

Description

MLFail

Record

Prob(TP) is less than SV_ML_MIN_PASS_DEL_PROB for deletions or Prob(TP) is less than SV_ML_MIN_PASS_INS_PROB (default values).

Multi-Region Joint Detection

DRAGEN Multi-Region Joint Detection (MRJD) is a germline small variant caller for paralogous regions. When used with TruPath data, MRJD produces haplotype‑resolved variant calls by leveraging proximity linking information enabled by TruPath. This approach does not rely on known population haplotypes.

With TruPath data, MRJD currently supports nine sets of paralogous regions encompassing 15 clinically relevant genes. Table 1 lists the hg38 genomic coordinates covered by MRJD. MRJD is compatible only with the hg38 reference genome.

Chromosome

Start

End

Region name

Paralog set name

Paralog type

chr1

196786972

196827189

CFHR3-CFHR1

CFHR3-CFHR1-CFHR4-CFHR2

Non-tandem

chr1

196911497

196951222

CFHR4-CFHR2

CFHR3-CFHR1-CFHR4-CFHR2

Non-tandem

chr5

70924941

70966375

SMN1

SMN1-SMN2

Non-tandem

chr5

70049523

70090528

SMN2

SMN1-SMN2

Non-tandem

chr6

32037415

32045473

CYP21A2-TNXB

CYP21A2

Tandem

chr6

32004679

32012619

CYP21A1P-TNXA

CYP21A2

Tandem

chr7

5969485

5987844

PMS2

PMS2-PMS2CL

Non-tandem

chr7

6736851

6755308

PMS2CL

PMS2-PMS2CL

Non-tandem

chr7

74771000

74791999

NCF1

NCF1-NCF1B-NCF1C

Non-tandem

chr7

73217606

73238630

NCF1B

NCF1-NCF1B-NCF1C

Non-tandem

chr7

75153934

75174978

NCF1C

NCF1-NCF1B-NCF1C

Non-tandem

chr8

142873164

142879856

CYP11B1

CYP11B1-CYP11B2

Tandem

chr8

142910764

142917883

CYP11B2

CYP11B1-CYP11B2

Tandem

chr15

43599563

43618800

STRC

STRC-STRCP1

Tandem

chr15

43699418

43718260

STRCP1

STRC-STRCP1

Tandem

chr22

18159724

18174315

USP18

USP18-USP41P

Non-tandem

chr22

20362649

20377695

USP41P

USP18-USP41P

Non-tandem

chr22

42123192

42132193

CYP2D6

CYP2D6-CYP2D7

Tandem

chr22

42135344

42145873

CYP2D7

CYP2D6-CYP2D7

Tandem

Table 1. Paralogous regions covered by MRJD.

Method

MRJD begins by collecting all primary alignments within the paralogous regions of interest, regardless of mapping quality. For each paralogous region set (for example, SMN1–SMN2), MRJD estimates the total copy number by leveraging read depth across the regions of interest and a set of pre‑selected stable regions elsewhere in the genome.

Using the estimated total copy number, read sequences, and proximity linking information, MRJD constructs the corresponding number of copies for each paralogous region set. For non‑tandem paralogous regions, proximity information is used to assign each constructed copy to the genomic region from which it most likely originated (for example, PMS2 versus PMS2CL). For tandem paralogous regions, proximity information is instead used to assign each copy to the maternal or paternal haplotype.

Finally, MRJD calls small variants based on the constructed copies and reports variant calls together with their assigned genomic regions or haplotypes.

The figure below provides an overview of the MRJD Workflow using TruPath data.

Outputs

Upon analysis completion, DRAGEN produces the following MRJD output files in the directory specified by --output-directory, using the prefix defined by --output-file-prefix:

<prefix>.mrjd.hard-filtered.vcf.gz
- VCF file containing small variants called by MRJD in paralogous regions.
<prefix>.mrjd.json
- JSON file containing MRJD results, including copy number estimates, region or haplotype assignments for each copy, and run status for each paralogous region.
<prefix>.mrjd.phased.bam
- BAM file containing phased read alignments within paralogous regions.
mrjd_supporting_files/
- A directory containing additional files that support MRJD visualization, including:
  - <prefix>.mrjd.<paralog_name>.vcf.gz
    Multi‑column VCF file containing MRJD variant calls for each paralogous region (one column per copy). One file is generated for each paralogous region set.
  - <prefix>.mrjd.reference_region_alignments.sam
    SAM file containing reference region alignments used by MRJD.

MRJD VCF Output

The MRJD caller generates a gzip‑compressed VCFv4.2 file, <prefix>.mrjd.hard-filtered.vcf.gz, containing small variants derived from the inferred genotypes.

For a given set of paralogous regions, all copies are reported under each region. Each copy is annotated with its assigned genomic region or haplotype in the FORMAT fields, depending on the paralog structure.

For non‑tandem paralogous regions, the REGION_PLACEMENT field in the FORMAT column indicates the genomic region assignment for each copy, following the order of entries in the genotype field. Values indicate assignment to the current region, assignment to an alternate region, or an unplaced copy.

#CHROM

POS

REF

ALT

QUAL

FILTER

INFO

FORMAT

chr5

70052190

500

regionGroupName=SMN1-SMN2;REF_DIFF_SITE

GT:REGION_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS

1|0|0|0:A,A,I,I:.:500:90,30:0.250:120:70052190

chr5

70052613

500

regionGroupName=SMN1-SMN2

GT:REGION_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS

1|0|0|0:A,A,I,I:.:500:86,35:0.289:121:70052190

chr5

70052881

CAAAAA

500

regionGroupName=SMN1-SMN2;REF_DIFF_SITE

GT:REGION_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS

1|0|0|0:A,A,I,I:.:500:93,28:0.231:121:70052190

chr5

70053733

500

regionGroupName=SMN1-SMN2

GT:REGION_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS

0|1|0|0:A,A,I,I:.:500:85,32:0.274:117:70052190

chr5

70053985

500

regionGroupName=SMN1-SMN2

GT:REGION_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS

0|1|0|1:A,A,I,I:.:500:67,65:0.492:132:70052190

chr5

70054456

500

regionGroupName=SMN1-SMN2

GT:REGION_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS

0|1|1|1:A,A,I,I:.:500:22,105:0.827:127:70052190

For tandem paralogous regions, the PSL field in the FORMAT column indicates haplotype assignment for each copy, again following the order of entries in the genotype field. hap1 and hap2 correspond to assignment to the first and second haplotypes, respectively. Because tandem copies cannot be assigned to specific genomic regions, the REGION_PLACEMENT field is not applicable and is populated with U (unplaced) for all copies.

#CHROM

POS

REF

ALT

QUAL

FILTER

INFO

FORMAT

chr6

32004754

63.01

regionGroupName=CYP21A2;REF_DIFF_SITE