Biology subjects

Hitz, M.-P.

Publications and source records attributed to Hitz, M.-P..

7 recordsLinked to original sources

bioRxiv2026

hvantk: an agentic multi-omics framework for variant annotation and analysis

Large-scale genetic variant analysis involves multiple computational steps, data formats, gene and variant annotation sources and compute requirements. Some of the steps like joint calling for large cohorts are run on large compute clusters, while gene/variant classification algorithms can be run in a desktop computer. At the same time, variant annotations such as gene expression and gene constraints metrics from gnomAD are provided in different formats, by resources that frequently change their versions. Here, we present hvantk, a framework that provides a common data model for large-scale annotation and analysis of genetic variants, by extending popular data formats such as Hail MatrixTable and AnnData. hvantk agentically integrates 21 public multi-omic data providers as plugins that a developer, bioinformatician or an AI agent can author without modifying the framework's core, drives analysis workflows using agent skills, and scales from single desktop computers to high-performance computing clusters. We used hvantk for reanalysing three case-control cohorts, comprising congenital heart disease (CHD), developmental and epileptic encephalopathy (Epi25) and schizophrenia (SCHEMA). Re-ranking of gene-based burden testing results using multi-omic annotations improved the recovery of curated disease genes from 26% to 49% in CHD, 40% to 48% in Epi25 and 51% to 54% in SCHEMA, among the top decile re-ranked genes. Expression at bulk and single-cell levels were the features contributing the most over a baseline of burden, gene constraint and publication count.

bioinformatics↗

bioRxiv2025

ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a proteins absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://github.com/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpys capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4,921 samples and 5,766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse.

bioinformatics↗

bioRxiv2024

TAK1 operates at the primary cilium in non-canonical TGFB/BMP signaling to control heart development

Transforming Growth Factor-Beta-Activated Kinase 1 (TAK1/MAP3K7), along with its upstream regulators TAK1-Binding Protein 2 (TAB2) and the catalytic alpha-subunit of Protein Kinase A (PKA-C/PRKACA), has been identified as a pivotal player in regulation of developmental processes. Haploinsufficiency of TAB2 causes Congenital Heart Disease (CHD) and rare variants in PKA-C and TAK1 cause cardioacrofacial dysplasia (CAFD), and Frontometaphyseal Dysplasia (FMD) and cardiospondylocarpofacial syndrome (CSCFS), respectively, rare multisystem syndromes, where CHD may appear in the clinical spectrum. We hypothesized that TAK1 plays a significant role in heart development and CHD and addressed this by genetic analysis in CHD patient cohorts and experiments in cell and animal models. Exome sequencing data from 1,471 CHD patients with extracardiac anomalies (syndromic CHD, sCHD), 2,405 patients with nonsyndromic CHD (nsCHD) and 45,082 controls showed increased burden of rare TAB2 and TAK1 variants in sCHD, but not in nsCHD. Detailed characterization of tak1-/-and tab2-/- zebrafish mutants revealed cardiac defects (dilated atrium, trabeculation defects, tachycardia and reduced contractility) as well as extracardiac developmental anomalies. RNA sequencing of tak1-/- mutant hearts showed downregulation of genes encoding core cardiac transcription factors, sarcomeric proteins and extracellular matrix proteins. Experiments with cell cultures and analysis of zebrafish larvae and gastruloids indicated that TAK1 via TAB2 and PKA-C is activated at the primary cilium during cardiomyogenesis and that TAK1 activation at this site is enhanced by cardiomyogenic signaling molecules, including ligands of the TGFB/BMP superfamily. Consistent with these findings, CRISPR/Cas9-mediated editing of TAK1 or administration of small molecule inhibitors targeting TAK1 inhibited ciliary signaling and cardiomyocyte differentiation in vitro, while FMD-causing mutations in TAK1 reduced its ciliary localization. In conclusion, our data establishes a central role for TAK1 and its upstream regulators in cardiac development and syndromic CHD, coordinated via the primary cilium.

developmental biology↗

bioRxiv2024

Transcriptional dynamics of the murine heart during perinatal development at single-cell resolution

Heart maturation and remodelling during the foetal and early postnatal period are critical for proper survival and growth of the foetus, yet our knowledge of the molecular processes involved are lacking for many cardiac cell types. To gain a deeper understanding of the transcriptional dynamics of the heart during the perinatal period, we performed single-cell RNA-seq on E14.5, E16.5, E18.5, P0, P4 and P7 mouse hearts to establish a catalogue of 49,769 cells. Gene regulatory network and pathway activity analyses underscored that heart maturation is strongly associated with regulation of cell growth and proliferation via pathways such as TGF{beta}. We additionally identified a common, cell type-independent signature for imprinted genes over time. Surprisingly, bioinformatics analyses and confirmation with RNAscope confirmed that while lncRNA H19 expression decreased over time in multiple cardiac cell types, it remained stably expressed in endocardial cells between E14.5 and P7. This suggests a differential requirement for H19 in the endocardium, and points towards an endocardium-specific maturation process when compared to other cardiac cell types. We envision this dataset to serve as a resource for better understanding perinatal heart maturation at the transcriptomic level, and to help bridge the gap between early developmental and adult heart stages for single-cell transcriptomics.

developmental biology↗

bioRxiv2020

The Omics Discovery REST interface

The Omics Discovery Index is an open source platform that can be used to access, discover and disseminate omics datasets. OmicsDI integrates proteomics, genomics, metabolomics, models and transcriptomics datasets. Using an efficient indexing system, OmicsDI integrates different biological entities including genes, transcripts, proteins, metabolites and the corresponding publications from PubMed. In addition, it implements a group of pipelines to estimate the impact of each dataset by tracing the number of citations, reanalysis and biological entities reported by each dataset. Here, we present the OmicsDI REST interface to enable programmatic access to any dataset in OmicsDI or all the datasets for a specific provider (database). Clients can perform queries on the API using different metadata information such as sample details (species, tissues, etc), instrumentation (mass spectrometer, sequencer), keywords and other provided annotations. In addition, we present two different libraries in R and Python to facilitate the development of tools that can programmatically interact with the OmicsDI REST interface.

bioinformatics↗

bioRxiv2019

Systems genetics analysis identify calcium signalling defects as novel cause of congenital heart disease

BackgroundCongenital heart disease (CHD) occurs in almost 1% of newborn children and is considered a multifactorial disorder. CHD may segregate in families due to significant contribution of genetic factors in the disease aetiology. The aim of the study was to identify pathophysiological mechanisms in families segregating CHD. MethodsWe used whole exome sequencing to identify rare genetic variants in ninety consenting participants from 32 Danish families with recurrent CHD. We applied a systems biology approach to identify developmental mechanisms influenced by accumulation of rare variants. We used an independent cohort of 714 CHD cases and 4922 controls for replication and performed functional investigations using zebrafish as in vivo model. ResultsWe identified 1,785 genes, in which rare alleles were shared between affected individuals within a family. These genes were enriched for known cardiac developmental genes and 218 of the genes were mutated in more than one family. Our analysis revealed a functional cluster, enriched for proteins with a known participation in calcium signalling. Replication confirmed increased mutation burden of calcium-signalling genes in CHD patients. Functional investigation of zebrafish orthologues of ITPR1, PLCB2 and ADCY2 verified a role in cardiac development and suggests a combinatorial effect of inactivation of these genes. ConclusionsThe study identifies abnormal calcium signalling as a novel pathophysiological mechanism in human CHD and confirms the complex genetic architecture underlying CHD.

genetics↗

bioRxiv2017

Accurate And Fast Feature Selection Workflow For High-Dimensional Omics Data

We are moving into the age of Big Data in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S x F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical bioinformatics problem (e.g. classification) includes redundant and irrelevant features that can result, in the worst-case scenario, in false positive results. Then, Feature Selection (FS) constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a grey zone. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domains problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process.

bioinformatics↗