modality XPLR

Accelerate discovery with efficient, insightful exploration of 6-base sequencing data.

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Transforming 6-base data into multiomic insights

Accelerate your journey from multiomic data to biological discovery with modality XPLR – a high‑performance, low‑code analysis software for analysing 6‑base data.

Move from raw sequencing counts to multiomic model-ready features with intuitive biological QC, differential methylation analysis, and feature extraction tools — all in a single, scalable workflow, on a standard laptop.

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Multiomic discovery made simple

modality XPLR is an accessible and scalable software tool designed for scientists and translational researchers to uncover disease-relevant signatures, link methylation to gene regulation, and support biomarker discovery—empowering you to identify, classify, and monitor disease.

With a low-code, command-line interface and comprehensive documentation, modality XPLR provides multiomic analyses and advanced publication-ready visualisations.

Introduced with modality XPLR v1.1.0, is a new interactive visualisation tool for navigation of complex multiomic datasets. Find key results faster with intuitive file discovery, comparative analysis, customisable plots and export tools. No additional installation necessary – the Viewer runs locally through any web browser for seamless integration on a standard laptop.

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Move beyond technical QC and unlock biological understanding

With modality XPLR, you can go further than basic sequencing checks to understand your datasets in biological context. Biological QC tools empower you to identify patterns and relationships in multiomic data, using intuitive correlation matrices and PCA plots. Instantly visualise sample similarities, spot outliers, differentiate subtypes and reveal underlying biological signals—so you can explore, interpret, and act on your results with confidence.

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Correlation of hmC fractions across gene bodies (hg38), between tissue samples. These plots can be used to observe sample relationships by modification type, and are included in the modality XPLR Biological QC analysis.
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Principal Component Analysis (PCA) for 5mC between tissue samples. These plots can be used to observe sample relationships for modification types, and are included in the modality XPLR Biological QC analysis.

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Discover meaningful epigenetic changes with differential analysis

modality XPLR makes it easy to identify 5mC and 5hmC differentially methylated regions (DMRs) across the genome. Flexible region definitions via BED file input, robust statistical testing, and advanced correction methods help you pinpoint significant differences between groups—revealing disease signatures, treatment responses, and better biomarkers.

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5mC DMRs for promoter regions (1000bp upstream of transcription start sites) between Healthy Control and CRC cfDNA from early to late-stage disease. Stage IV samples show a high density of 5mC hypomethylation (negative modification difference when compared to controls), which is typically associated with gene repression. Overdispersion correction applied.
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5hmC DMRs for gene body regions between Healthy Control and CRC cfDNA from early to late-stage disease. Stage II samples show significant hypermethylated 5hmC DMRs compared to controls, and help to explain the late-stage hypomethylation in 5hmC (this figure) and 5mC in promoter regions (above). Overdispersion correction applied.

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Best-in-class performance and DMR accuracy

We’ve validated modality XPLR DMR calling across a range of analytical parameters, using a truth set of simulated DMRs spiked into real cfDNA data. The effect of region size, CpG coverage and sample size are shown with respect to DMR sensitivity and false discovery. Next, using dynamic, genome-wide pre-segmentation method guided by the distribution of CpGs, we demonstrate the DMR sensitivity and false discovery rate according to DMR effect size. The effect of region size, CpG coverage and sample size are shown below with respect to DMR sensitivity.
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modality XPLR 5mC DMR calling sensitivity and FDR, for region sizes of 1Kb, 2Kb, and 5Kb, by (A) mean strand-merged CpG coverage with 8 samples per group and (B) sample size with up to 32 samples per group (64 total) and a mean strand-merged CpG coverage of 21x.

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Sensitivity and FDR for 5mC DMR calling for pre-segmented regions by mean methylation difference (effect size).

Map discoveries into biological context

Move beyond summary statistics with interactive gene track plots. Overlay 5mC and 5hmC signals on annotated genes, promoters, and regulatory elements to interpret epigenetic change where it matters most. Track plots provide an additional layer of validation—confirming significance, uncovering functional relevance, and accelerating confident biological discovery.

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Methylation fraction by genomic position (points) and smoothed (line) at CpG contexts for Control (dark teal) and Test (light teal) groups.
Methylation difference at CpG contexts (smoothed line) between the two groups. Positive values indicate hypermethylation and negative values indicate hypomethylation relative to the Control group.
Magnitude of modification difference for defined DMR regions (teal block). Positive values indicate hypermethylation and negative values indicate hypomethylation relative to the Control group. Significance level indicated by the number of asterisks upon cursor hover-over.
Annotation of genomic features by genome reference selection, showing genes and exons (blue boxes).
Tracks plot for the ENGASE gene, showing a hypomethylated 5mC DMR between Healthy Control and Stage IV CRC cfDNA. The four tracks are methylation fraction, methylation difference, DMR magnitude and genome annotation. The DMR significance (p-value) is indicated by the number of asterisks (≤0.001), the difference between 5mC fractions is highlighted by divergence of the mean methylation between the two groups (methylation fraction track), immediately prior to the ENGASE TSS (annotation track).
"Data processing can often be a roadblock to progressing data analysis—particularly when working with complex methylation datasets derived from circulating tumour DNA. modality XPLR's efficient methods and objects accelerate the transition from raw data to clean, analysable outputs, which is essential when tracking tumour evolution and therapeutic response over time. The comprehensive documentation also provides a high degree of malleability, allowing me to tailor workflows to the unique demands of liquid biopsy data across different sample types and timepoints."
Max BladenSJ Dawson Laboratory, Peter MacCallum Cancer Centre

Power your models with feature extraction

modality XPLR is designed to sit upstream of your modelling workflows through exploration of feature statistics over genome annotations, DMRs or custom defined regions. Once key features are identified, efficiently extract and export them in community-standard formats (BED, TSV, CX Report, bedmethyl, bedgraph and Bismark) for use in custom pipelines, translational modelling environments and classifier development.
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Violin plot showing the distribution of 5mC fractions over gene bodies by tissue type, indicating tissue-specific differences in methylation profiles

Benchmarking the DMR caller in modality XPLR against methylKit

To assess real-world usability, we tested modality XPLR and methlyKit (an alternate community tool) using an 8-sample pilot dataset on a. standard laptop. Using modality XPLR, DMR calling over 19,382 promoter regions completed in under 7 minutes. However, methylKit failed to load the input files due to memory constraints. This comparison highlights modality XPLR’s scalability and efficiency, making it a practical solution for large-scale epigenomic studies.
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Memory usage over time for genome-wide DMR calling on 19,382 promoters with 8 samples on a standard laptop (4 cores, 16 GB RAM). Whilst modality XPLR efficiently completes the analysis in a round 7 minutes, methylKit cannot complete the operation due to memory exhaustion.

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