Biology subjects

Wissel, D.

Publications and source records attributed to Wissel, D..

7 recordsLinked to original sources

bioRxiv2026

Building computational benchmarks: an Omnibenchmark reimplementation of a single-cell preprocessing pipeline evaluation

In the past few years, we have seen a veritable surge in single-cell (e.g., RNA sequencing) techniques and datasets, enabling increasingly detailed characterization of cellular heterogeneity across tissues and conditions. This surge in single-cell techniques has been complemented by a large number of analysis frameworks and pipelines, and a large parameter space and researcher degrees of freedom to use them. Many neutral benchmarks have been presented for various computational tasks, but most make design decisions that render them incompatible with each other, e.g., different datasets and metrics, or parameter sets used. In this work, we showcase a recently developed framework, Omnibenchmark, to build reproducible, extensible and standardized method comparisons. This not only facilitates the broad investigation of pipelines used in single-cell data analysis, but also highlights how the process of building benchmarks can be streamlined and unified. We do this as an initial proof-of-principle for an arms-length benchmark that evaluates five single-cell RNA sequencing pipelines (filtering to normalization to dimensionality reduction to clustering) on three datasets. This standardization enables benchmarks to be easily extended in several directions, including broader parameter sweeps, comparisons across software versions and architectures, isolation of pipeline steps, and integration of additional pipelines, datasets, and metrics.

bioinformatics↗

bioRxiv2025

Sample-specific haplotype-resolved protein isoform characterization via long-read RNA-seq-based proteogenomics

Protein isoform inference from bottom-up mass spectrometry (MS) relies on database search strategies that assume the reference protein database accurately reflects the full repertoire of genetic and transcriptomic states present in the sample being analyzed. Long-read RNA sequencing (lrRNA-seq) now enables simultaneous recovery of complete transcript (splice) structures and the genetic variants present on each molecule, offering a direct route to allele-specific isoforms. Yet, this capability has not been fully leveraged to improve MS-based proteogenomics workflows. Here, we develop an end-to-end workflow for constructing and searching haplotype-resolved, sample-specific proteomes using matched lrRNA-seq and MS data. We benchmark phasing algorithms on PacBio lrRNA-seq from Genome-in-a-Bottle samples and identify methods that achieve high phasing accuracy and completeness. Our Snakemake pipeline leverages existing methods to perform variant calling, read-based phasing, transcript discovery, haplotype-resolved proteome construction, MS search, and downstream annotation. To demonstrate its utility, we apply the workflow to an induced pluripotent stem cell line (WTC11) and to an osteoblast differentiation time course. We show that sample-specific haplotype-resolved databases enable the detection of variant and splice peptides, allele-specific protein isoforms, and linked variants not detectable with reference proteomes. Together, our results demonstrate that lrRNA-seq-based phasing is feasible and effective for proteogenomics and provide a practical framework for allele-resolved proteome characterization in dynamic or disease-relevant settings.

bioinformatics↗

bioRxiv2025

Quantitative isoform profiling using deep coverage long-read RNA sequencing across early endothelial differentiation

PacBio long-read RNA sequencing resolves transcripts with greater clarity than short-read technologies, yet its quantitative performance remains under-evaluated at scale. Here, we benchmark the high-throughput PacBio Kinnex platform against Illumina short-read RNA-seq using matched, deeply sequenced datasets across a time course of endothelial cell differentiation. Compared to Illumina, Kin-nex achieved comparable gene-level quantification and more accurate transcript discovery and transcript quantification. While Illumina detected more transcripts overall, many reflected potentially unstable or ambiguous estimates in complex genes. Kinnex largely avoids these issues, producing more reliable differential transcript expression (DTE) calls, despite a mild bias against short transcripts (shorter than 1.25 kb). When correcting Illumina for inferential variability, Kinnex and Illumina quantifications were highly concordant, demonstrating equivalent performance. We also benchmarked long-read tools, nominating Oarfish as the most efficient for our Kinnex data. Together, our results establish Kinnex as a reliable platform for full-length transcript quantification.

bioinformatics↗

bioRxiv2024

CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer

We present CancerFoundation, a novel single-cell RNA-seq foundation model (scFM) trained exclusively on malignant cells. Despite being trained on only one million total cells, a fraction of the data used by existing models, CancerFoundation outperforms other scFMs in key tasks such as zero-shot batch integration and drug response prediction. During training, we employ tissue and technologyaware oversampling and domain-invariant training to enhance performance on underrepresented cancer types and sequencing technologies. We propose survival prediction as a new downstream task to evaluate the generalizability of single-cell foundation models to bulk RNA data and their applicability to patient stratification. CancerFoundation demonstrates superior batch integration performance and shows significant improvements in predicting drug responses for both unseen cell lines and drugs. These results highlight the potential of focused, smaller foundation models in advancing drug discovery and our understanding of cancer biology. Our code is available here1.

bioinformatics↗

bioRxiv2022

SurvBoard: Standardised Benchmarking for Multi-omics Cancer Survival Models

Multi-omics data, which include genomic, transcriptomic, epigenetic, and proteomic data, are gaining increasing importance for determining the clinical outcomes of cancer patients. Several recent studies have evaluated various multi-modal integration strategies for cancer survival prediction, highlighting the need for standardizing model performance results. Addressing this issue, we introduce SurvBoard, a benchmark framework that standardizes key experimental design choices. SurvBoard enables comparisons between single-cancer and pan-cancer data models and assesses the benefits of using patient data with missing modalities. We also address common pitfalls in preprocessing and validating multi-omics cancer survival models. We apply SurvBoard to several exemplary use cases, further confirming that statistical models tend to outperform deep learning methods, especially for metrics measuring survival function calibration. Moreover, most models exhibit better performance when trained in a pan-cancer context and can benefit from leveraging samples for which data of some omics modalities are missing. We provide a web service for model evaluation and to make our benchmark results easily accessible and viewable: https://www.survboard.science/. All code is available on GitHub: https://github.com/BoevaLab/survboard/. All benchmark outputs are available on Zenodo: https://zenodo.org/records/11066227. O_TEXTBOXKey MessagesO_LIWe introduce SurvBoard, a comprehensive benchmarking framework for the standardized evaluation of multi-omics cancer survival models. SurvBoard provides an easily accessible platform for the reproducible comparison of models trained on single-cancer and pan-cancer datasets. The platform addresses issues such as the impact of missing modalities and variability in experimental setups. SurvBoard integrates data from four major cancer programs-TCGA, ICGC, TARGET, and METABRIC-to ensure a comprehensive evaluation across diverse types of cancer and research centers. C_LIO_LISurvBoard results confirm that statistical models generally outperform deep learning models in survival function calibration. We also find that pan-cancer training enhances model performance and that models benefit from incorporating data with missing modalities. C_LIO_LISurvBoard includes a web service that allows researchers to submit models for benchmarking and evaluation. A leaderboard is accessible via https://survboard.science/ to promote transparency and the continuous assessment of models performance. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2022

Clinically driven knowledge distillation for sparsifying high-dimensional multi-omics survival models

Recently, various methods have been proposed to integrate different heterogeneous high-dimensional genomic data sources to predict cancer survival, often in addition to widely available and highly predictive clinical data. Although clinical applications of survival models have high sparsity requirements, most state-of-the-art models do not naturally exhibit this sparsity, as they are based on random forests or deep learning. We propose to use[l] 1-penalized linear student models within a knowledge distillation framework to sparsify underlying multi-omics black-box teachers. We show that by excluding clinical variables from our[l] 1 penalty, we can effectively guide the knowledge distillation, reaching virtually identical discriminative performance to the teachers while using on average 140 features or less across the 17 cancer datasets from The Cancer Genome Atlas (TCGA) considered in our study.

bioinformatics↗

bioRxiv2021

Supervised Hierarchical Autoencoders for Multi-Omics Integration in Cancer Survival Models

With decreasing costs of high-throughput sequencing, more and more datasets providing omics profiles of cancer patients become available. Thus, novel survival analysis approaches integrating these differently sized and heterogeneous molecular and clinical groups of variables start being developed. Due to the difficulty of the task of multi-omics data integration, the Cox Proportional-Hazards (PH) model using clinical data has remained one of the best-performing techniques, barely outperformed by models using molecular data modalities. There is therefore a need for methods that can successfully perform multi-omics integration in survival analysis and outperform the clinical Cox PH model. Moreover, while certain deep learning methods have been shown to provide state-of-the-art accuracy of cancer survival prediction, most of them show no benefit or even decay in performance when integrating a larger number of modalities, further motivating a need to investigate how modality-specific representations should be integrated when using neural networks for multi-omics integration. We benchmarked multiple integration techniques for a neural network architecture, revealing that hierarchical autoencoder-based integration of modality-specific representations outperformed other methods such as max-pooling and was comparable with state-of-the-art statistical approaches for multi-omics integration. Further, we showed that the hierarchical autoencoder-based integration of modality-specific representations achieved increased performance through a soft modality selection mechanism, focusing on the most informative modalities for each cancer. We thus framed multiomics integration as a partial group-wise feature selection problem, highlighting that only those models performed well that could adequately weight important modalities in the presence of the high noise imposed by less important modalities.

bioinformatics↗