Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.05.19.594885

Analyzing Performance of Twist Bioscience Exome Enrichment with Spike-in CNV Backbone Panels at Various Probe Densities Leveraging Golden Helix VS-CNV Analysis Software

Fortier, N.·Rudy, G.·Han, T.·Davassi, A.·Scherer, A.
View original source ↗Export metadata ↓

Abstract

Clinical Whole Exome Sequencing (WES) offers a high diagnostic yield test by detecting pathogenic variants in all coding genes of the human genome. WES is poised to consolidate multiple genetic tests by accurately identifying Copy Number Variation (CNV) events, typically necessitating microarray analysis. However, standard commercial exome kits are typically limited to targeting exon coding regions, leaving significant gaps in coverage between genes, which could hinder comprehensive CNV detection. To convert microarray CNV calling with NGS, advances in both assay design and computational methods are needed. Addressing the need for comprehensive coverage, Twist Bioscience has developed an enhanced Exome 2.0 Plus Comprehensive Exome Spike-in panel with added CNV "backbone" probes. These probes target common SNPs polymorphic in multiple populations and are evenly distributed in the intergenic and intronic regions, with three varying densities at 25 kb, 50 kb, and 100 kb intervals from highest to lowest resolution respectively. Concurrently, Golden Helix has developed a multi-modal CNV caller designed specifically for target-capture NGS data to detect single-exon to whole-chromosome aneuploidy CNV events. This study evaluates the combined efficacy of the backbone-probe enhanced exome capture kit and VS-CNV 2.6 in identifying known CNVs using the Coriell CNVPANEL01 reference set. The integration of the enhanced capture kit with VS-CNV 2.6 achieved a 100% sensitivity rate for the detection of known CNV events at all three probe densities. The application of best-practice quality metrics, annotations, and filters was shown to have a minimal impact on this high sensitivity. These findings underscore the potential of the augmented Twist Exome in tandem with the VS-CNV caller and VarSeqs annotation and filtering capabilities. This combination presents a promising alternative to conventional microarray assays, potentially consolidating WES and CNV into a single assay obviating the need for additional testing in clinical CNV detection. The studys results advocate for the implementation of this integrated approach as a more efficient and equally sensitive method for CNV analysis in a clinical setting.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fortier, N., Rudy, G., Han, T., Davassi, A., Scherer, A.. 2024-05-21. Analyzing Performance of Twist Bioscience Exome Enrichment with Spike-in CNV Backbone Panels at Various Probe Densities Leveraging Golden Helix VS-CNV Analysis Software. https://doi.org/10.1101/2024.05.19.594885

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗