Biology subjects

Scherer, A.

Publications and source records attributed to Scherer, A..

15 recordsLinked to original sources

bioRxiv2025

VSPGx: A High-Accuracy Pharmacogenomics Interpretation Software Solution with Automated CPIC Guideline Integration

Accurate pharmacogenomic genotype determination and interpretation are essential for personalized medicine, yet existing bioinformatics tools face significant limitations in detecting named alleles, maintaining current allele definitions, and providing comprehensive clinical annotations. We present VSPGx, a pharmacogenomics interpretation software solution that identifies diplotypes from next-generation sequencing data and annotates them against Clinical Pharmacogenetics Implementation Consortium (CPIC) and FDA drug recommendations using automated curation of the latest allele definitions. We benchmarked VSPGx against established tools including Aldy, PharmCAT, and Stargazer using both synthetic datasets and real-world clinical samples. In a comprehensive synthetic benchmark spanning 3,655 CYP2C9 diplotype combinations, VSPGx achieved 99.97% concordance, matching PharmCATs performance and substantially outperforming Aldy (93.08%) and Stargazer (27.06%). Clinical validation using 11 TaqMan OpenArray samples demonstrated 88.2% allele concordance and 89.1% phenotype concordance across 110 gene-sample combinations, with all discrepancies attributed to the benchmark data utilizing outdated allele definitions rather than VSPGx errors. Our automated curation process ensures continuous alignment with current CPIC guidelines, addressing a critical gap in existing pharmacogenomic analysis tools. VSPGx provides a robust, clinically-validated solution for pharmacogenomic analysis that combines high-accuracy diplotype calling with up-to-date, evidence-based drug recommendations.

bioinformatics↗

bioRxiv2025

Analyzing the Performance of Deep Learning Splice Prediction Algorithms

SpliceAI has become the leading computational tool for predicting splice-altering variants, but restrictive licensing has limited its adoption by commercial clinical laboratories. While open-source reimplementations have emerged with author-reported comparisons, independent benchmarking across diverse datasets is needed to establish their practical equivalence. We compared the original SpliceAI algorithm against two open-source alternatives (OpenSpliceAI and CI-SpliceAI) across three independent benchmarks: a curated dataset of 1,316 functionally validated variants, 213 variants with experimental splice assay data, and 58,064 clinically classified variants from ClinVar. All deep-learning methods were also compared with a legacy ensemble of four traditional splice prediction algorithms (MaxEntScan, NNSplice, GeneSplicer, and PWM), enabling direct comparison between modern and conventional approaches. Across all benchmarks, the deep-learning models consistently outperformed the ensemble of traditional algorithms. We evaluated sensitivity, specificity, and balanced accuracy for each algorithm, and performed statistical testing to assess significance of performance differences. Additionally, we conducted a correlation analysis on 100,000 variants to quantify the concordance of splice-scores and the agreement on splice site positions between implementations. All three deep learning algorithms demonstrated comparable performance on the literature-curated benchmark (balanced accuracies: 89.5-90.7%) and the ClinVar dataset (88.9-89.5%). While both open-source solutions achieved a statistically significantly higher accuracy than SpliceAI on the ClinVar dataset, the magnitude of this improvement was small and unlikely to be of practical significance. On the functional splice assay dataset, the original SpliceAI achieved the highest accuracy (83.6%), while OpenSpliceAI showed significantly lower performance (74.6%, p = 0.019). Correlation analysis revealed that CI-SpliceAI maintained balanced concordance across splice event types ({rho} = 0.786-0.883), whereas OpenSpliceAI exhibited asymmetric performance with stronger correlation for loss events ({rho} = 0.924-0.940) than gain events ({rho} = 0.668-0.677). Both implementations demonstrated high spatial agreement with SpliceAI, with exact splice site position match rates exceeding 90% for all event types. Together, these results demonstrate that both open-source reimplementations of SpliceAI successfully reproduce the predictive behavior of the original algorithm across multiple evaluation contexts, while consistently outperforming traditional splice prediction methods.

bioinformatics↗

bioRxiv2024

Comprehensive multi-omics profiling of a healthy human cohort

Multi-omics approaches can offer powerful insights into personalized biomarker profiles relevant for disease diagnosis, prognosis, and therapeutics. However, separating meaningful biological variability from technical noise remains a major challenge. The EATRIS-Plus consortium analyzed blood samples from 127 healthy adults across six omics layers using twelve platforms, resulting in one of the most comprehensive multi-omics profiling datasets of healthy individuals available to date. We applied reproducible workflows to analyze and integrate these data, revealing several key findings. Sex significantly influenced all omics layers, emphasizing the importance of sex-balanced study designs. Age could be accurately predicted using epigenetic clocks, achieving high performance with our high-resolution enzymatic methylation sequencing data (R2 = 0.90), whereas candidate aging biomarkers were identified across all omics layers. The resulting dataset provides reference ranges in healthy individuals for abundance and variability of omics features, enabling robust power analyses, sample size estimations, and benchmarking of multi-omics integration methods. This resource can guide future biomarker discovery and personalized health research and was made FAIR-compliant and publicly available via the ClinData Portal (https://clindata.imtm.cz) and a Zenodo repository (https://doi.org/10.5281/zenodo.17514796).

bioinformatics↗

bioRxiv2024

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

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.

bioinformatics↗

bioRxiv2024

Vimentin regulates mitochondrial ROS production and inflammatory responses of neutrophils

The intermediate filament vimentin is present in immune cells and is implicated in proinflammatory immune responses. Whether and how it supports antimicrobial activities of neutrophils is not well established. Here, we developed an immortalized neutrophil model to examine the requirement of vimentin. We demonstrate that vimentin restricts the production of proinflammatory cytokines and reactive oxygen species (ROS), but enhances phagocytosis and swarming. We observe that vimentin is dispensable for neutrophil extracellular trap (NET) formation, degranulation, and inflammasome activation. Moreover, gene expression analysis demonstrated that the presence of vimentin was associated with changes in expression of multiple genes required for mitochondrial function and ROS overproduction. Treatment of wild-type cells with rotenone, an inhibitor for complex I of the electron transport chain, increases the ROS levels. Likewise, treatment with mitoTEMPO, a SOD mimetic, rescues the ROS production in cells lacking vimentin. Together, these data show vimentin regulates neutrophil antimicrobial functions and alters ROS levels through regulation of mitochondrial activity.

immunology↗

bioRxiv2023

Multi-omics Quality Assessment in Personalized Medicine through EATRIS

Molecular characterization of a biological sample, e.g., with omics approaches, is fundamental for the development and implementation of personalized and precision medicine approaches. In this context, quality assessment is one of the most critical aspects. Accurate performance and interpretation of omics techniques is based on consensus, harmonization, and standardization of protocols, procedures, data analysis and reference values and materials. EATRIS, the European Infrastructure for Translational Medicine (www.EATRIS.eu), brings together resources and services to support researchers in developing their biomedical discoveries into novel translational tools and interventions for better health outcomes. Here we describe activities of member facilities of EATRIS towards quality assessment of pre-clinical sample processing, clinical omics data generation, multi-omics data integration, and dissemination of the resources in a Multi-Omics Toolbox, the principal deliverable of the EATRIS Plus project for the consolidation of EATRIS towards translational Medicine.

molecular biology↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗

bioRxiv2022

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The implementation of quality control for multiomic data requires the widespread use of well-characterized reference materials, reference datasets, and related resources. The Quartet Data Portal was built to facilitate community access to such rich resources established in the Quartet Project. A convenient platform is provided for users to request the DNA, RNA, protein, and metabolite reference materials, as well as multi-level datasets generated across omics, platforms, labs, protocols, and batches. Interactive visualization tools are offered to assist users to gain a quick understanding of the reference datasets. Crucially, the Quartet Data Portal continuously collects, evaluates, and integrates the community-generated data of the distributed Quartet multiomic reference materials. In addition, the portal provides analysis pipelines to assess the quality of user-submitted multiomic data. Furthermore, the reference datasets, performance metrics, and analysis pipelines will be improved through periodic review and integration of multiomic data submitted by the community. Effective integration of the evolving technologies via active interactions with the community will help ensure the reliability of multiomics-based biological discoveries. The Quartet Data Portal is accessible at https://chinese-quartet.org. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

systems biology↗

bioRxiv2022

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

bioinformatics↗

bioRxiv2022

Quartet RNA reference materials and ratio-based reference datasets for reliable transcriptomic profiling

As an indispensable tool for transcriptome-wide analysis of differential gene expression, RNA sequencing (RNAseq) has demonstrated great potential in clinical applications. However, the lack of multi-group RNA reference materials of biological relevance and the corresponding reference datasets for assessing the reliability of RNAseq hampers its wide clinical applications wherein the underlying biological differences among study groups are often small. As part of the Quartet Project for quality control and data integration of multiomic profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets using multi-batch RNAseq datasets, providing "ground truth" for benchmarking. Moreover, Quartet-sample-based quality metrics were developed for assessing reliability of RNAseq technology in terms of intra-batch proficiency and cross-batch reproducibility. The small intrinsic biological differences among the Quartet samples enable sensitive assessment of performance of transcriptomic measurements. The Quartet RNA reference materials combined with the reference datasets can be served as unique resources for assessing data quality and improving reliability of transcriptomic profiling.

genetics↗

bioRxiv2021

Whole Genome and Exome Sequencing Reference Datasets from A Multi-center and Cross-platform Benchmark Study

With the rapid advancement of sequencing technologies in the past decade, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from errors or artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically benchmarking sequencing platforms, library protocols, bioinformatics pipelines and for measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples, a human triple-negative breast cancer cell line and a matched normal cell line derived from B lymphocytes. We generated whole-genome (WGS) and whole-exome sequencing (WES) data using 16 NGS library preparation protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the paired reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

bioinformatics↗

bioRxiv2020

Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

Since the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year. SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

bioinformatics↗

bioRxiv2020

The SEQC2 Epigenomics Quality Control (EpiQC) Study: Comprehensive Characterization of Epigenetic Methods, Reproducibility, and Quantification

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and a global resource for epigenetics research from the FDAs Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus was on 5-methylcytosine modifications. Each sample was processed in two or more technical replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), one enzymatic deamination method (EMseq), targeted methylation sequencing (Illumina Methyl Capture EPIC), and single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, BWAMeth, and GemBS), we found overall high concordance between assays (R=0.87-R0.93), differences in efficency of read mapping and CpG capture and coverage, and platform performance. The data provided herein can guide continued used of these reference materials in epigenomics assays, as well as provide best practices for epigenomics research and experimental design in future studies.

genomics↗

bioRxiv2020

Profiling the Intratumoral Immune Landscapes of Primary and Syngeneic Kras-driven Sarcoma Mouse Models

ABSTRACTThe introduction of immunotherapy has revolutionized cancer treatment and fueled interest in the immune cell composition of preclinical tumor models. Both genetically-engineered mouse models and syngeneic cell transplant approaches use immunocompetent mice to study mechanisms driving immunotherapy response and resistance. Due to their rapid and reproducible nature, there has been an expanded interest in developing new syngeneic tools from established primary tumor models. However, there are few analyses directly comparing the immune profiles of primary models with their derived syngeneic counterparts. Here we report comprehensive immunophenotyping of primary tumors from two well-established sarcoma models and four syngeneic allografts derived from these models. We observed that cell lines derived from the same type of genetically engineered primary tumor form allografts with significantly different levels of immune infiltration and intratumoral immune cell composition. Most notable are the differences in the T cell compartment of the syngeneic models, including CD4+ T cell, CD8+ T cell, and regulatory T cell populations – each of which have well-documented roles in tumor response to immunotherapy. Our findings highlight the importance of thorough characterization of syngeneic models prior to their use in preclinical studies in order to maintain scientific rigor and to increase the translatability of findings from bench to bedside.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗

bioRxiv2019

Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

We characterized two reference samples for NGS technologies: a human triple-negative breast cancer cell line and a matched normal cell line. Leveraging several whole-genome sequencing (WGS) platforms, multiple sequencing replicates, and orthogonal mutation detection bioinformatics pipelines, we minimized the potential biases from sequencing technologies, assays, and informatics. Thus, our \"truth sets\" were defined using evidence from 21 repeats of WGS runs with coverages ranging from 50X to 100X (a total of 140 billion reads). These \"truth sets\" present many relevant variants/mutations including 193 COSMIC mutations and 9,016 germline variants from the ClinVar database, nonsense mutations in BRCA1/2 and missense mutations in TP53 and FGFR1. Independent validation in three orthogonal experiments demonstrated a successful stress test of the truth set. We expect these reference materials and \"truth sets\" to facilitate assay development, qualification, validation, and proficiency testing. In addition, our methods can be extended to establish new fully characterized reference samples for the community.

bioinformatics↗