Biology subjects

Menschaert, G.

Publications and source records attributed to Menschaert, G..

18 recordsLinked to original sources

bioRxiv2025

Translatome and translation dynamics analysis of a RiboCancer cell line panel reveals that leukemia-associated Rps15 mutations rewire translation through codon-specific tRNA accommodation defects.

Deletions and point mutations targeting ribosomal proteins (RPs) have been identified in cancer. Yet, their role in translational dysregulation remains poorly understood. We performed an integrated genome-wide translatome analysis (proteome, Ribo-seq and total RNA-seq) as well as RiboMethSeq on an isogenic cell line library modeling the most recurrent RP defects in cancer (Rpl5+/-, Rpl11+/-, Rpl22+/-, Rpl22-/-, Rpl10 R98S, Rps15 P131S and Rps15 H137Y). RP knock-out had minimal effects on translation, whereas RP point mutations induced a significant number of translation efficiency changes, affecting up to 10% of expressed genes in Rps15 mutants associated with Chronic Lymphocytic Leukemia (CLL). Cryo-electron microscopy and biochemical analyses revealed that the Rps15 mutations destabilize the C-terminal Rps15 domain, affecting the translation elongation cycle dynamics, and deregulating accommodation of aminoacylated tRNAs at the ribosomal A-site. Using Ribo-seq and translation reporter assays, we show that this accommodation defect shows codon specificity, explaining the reduced translation efficiency of genes enriched for these codons in Rps15 mutant cells, such as histones. Notably, genes with reduced translation efficiency in Rps15 mutated cells were enriched for transcriptional regulators such as transcription factor Runx3, resulting in downregulation of Runx3 target genes involved in immune regulation. Altogether, this study provides a comparative map of the translational rewiring driven by the most frequent somatic RP mutations. We provide unprecedented mechanistic insights in the translation defects induced by CLL-associated Rps15 mutations, and reveal an unappreciated cross-talk between translational and transcriptional dysregulation in these RP mutant cells. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/687986v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@cd2846org.highwire.dtl.DTLVardef@10f3c49org.highwire.dtl.DTLVardef@13f1595org.highwire.dtl.DTLVardef@a212df_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

An expanded reference catalog of translated open reading frames for biomedical research

Non-canonical (i.e., unannotated) open reading frames (ncORFs) have until recently been omitted from reference genome annotations, despite evidence of their translation, limiting their incorporation into biomedical research. To address this, in 2022, we initiated the TransCODE consortium and built the first community-driven consensus catalog of human ncORFs, which was openly distributed to the research community via Ensembl-GENCODE. While this catalog represented a starting point for reference ncORF annotation, major technical and scientific issues remained. In particular, this initial catalogue had no standardized framework to judge the evidence of translation for individual ncORFs. Here, we present an expanded and refined catalog of the human reference annotation of ncORFs. By incorporating more datasets and by lifting constraints on ORF length and start-codon, we define a comprehensive set of 28,359 ncORFs that is nearly four times the size of the previous catalog. Furthermore, to aid users who wish to work with ncORFs with the strongest and most reproducible signals of translation, we utilized a data-driven framework (i.e. translation signature scores) to assess the accumulated evidence for any individual ncORF. Using this approach, we derive a subset of 7,888 ncORFs with translation evidence on par with canonical protein-coding genes, which we refer to as the Primary set. This set can serve as a reliable reference for downstream analyses and validation, with a particular emphasis on high quality. Overall, this update reflects continual community-driven efforts to make ncORFs accessible and actionable to the broader research public and further iterations of the catalog will continue to expand and refine this resource.

genomics↗

bioRxiv2025

A systematic assessment of single-cell language model configurations

Transformers pre-trained on single-cell transcriptomic data have recently been applied to a series of tasks, earning them the title of foundation models. As all currently published models in this class employ vastly different pre-training strategies, it is impossible to determine which practices drive their success (or failure). Here, we present a large-scale study of pre-training components for single-cell transcriptomic transformers: bento-sc (BENchmarking Transformer-Obtained Single-Cell representations). By isolating (and tuning) parts of the pre-training scheme one by one, we define best practices for single-cell language model (scLM) construction. While comparisons with baselines indicate that scLMs do not yet offer the generational leap in prediction performances promised by many foundation models, we identify key design choices leading to their improved performance. Namely, the best scLMs are obtained by: (1) minimally processing counts at the input level, (2) using reconstruction losses that exploit known count distributions, (3) masking (up to high rates), and (4) combining different pre-training tasks/losses. All code supporting this study is distributed on PyPI and is packaged under: https://github.com/gdewael/bento-sc.

bioinformatics↗

bioRxiv2024

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

molecular biology↗

bioRxiv2024

Deep learning to decode sites of RNA translation in normal and cancerous tissues

The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Yet, accurately delineating the variation in RNA translation represents a significant challenge. Here, we develop RiboTIE, a transformer model-based approach to map global RNA translation. We find that RiboTIE offers unparalleled precision and sensitivity for ribosome profiling data. Application of RiboTIE to normal brain and medulloblastoma cancer samples enables high-resolution insights into disease regulation of RNA translation.

bioinformatics↗

bioRxiv2024

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

For the last decade, matrix-assisted laser desportion/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been the reference method for species identification in clinical microbiology. Hampered by a historical lack of open data, machine learning research towards models specifically adapted to MALDI-TOF MS remains in its infancy. Given the growing complexity of available datasets (such as large-scale antimicrobial resistance prediction), a need for models that (1) are specifically designed for MALDI-TOF MS data, and (2) have high representational capacity, presents itself. Here, we introduce Maldi Transformer, an adaptation of the state-of-the-art transformer architecture to the MALDI-TOF mass spectral domain. We propose the first self-supervised pre-training technique specifically designed for mass spectra. The technique is based on shuffling peaks across spectra, and pre-training the transformer as a peak discriminator. Extensive benchmarks confirm the efficacy of this novel design. The final result is a model exhibiting state-of-the-art (or competitive) performance on downstream prediction tasks. In addition, we show that Maldi Transformers identification of noisy spectra may be leveraged towards higher predictive performance. All code supporting this study is distributed on PyPI and is packaged under: https://github.com/gdewael/maldi-nn

bioinformatics↗

bioRxiv2023

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavours to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged under: https://github.com/gdewael/maldi-nn

bioinformatics↗

bioRxiv2023

RIBO-former: leveraging ribosome profiling information to improve the detection of translated open reading frames.

AO_SCPLOWBSTRACTC_SCPLOWRibosome profiling is a deep sequencing technique used to chart translation by means of mRNA ribosome occupancy. It has been instrumental in the detection of non-canonical coding sequences. Because of the complex nature of next-generation sequencing data, existing solutions that seek to identify translated open reading frames from the data are still not perfect. We propose RIBO-former, a new approach featuring several innovations for the de novo annotation of translated coding sequences. RIBO-former is built using recent transformer models that have achieved considerable advancements in the field of natural language processing. The presented deep learning approach allows to omit several pre-processing steps as features are automatically extracted from the data. We discuss various steps that improve the detection of coding sequences and show that read length information of all mapped reads can be leveraged to improve the predictive performance of the tool. Our results show RIBO-former to outperform previous methodologies. Additionally, through our study we find support for the existence of translated non-canonical ORFs, present along existing coding sequences or on long non-coding RNAs. Furthermore, several polycistronic mRNAs with multiple translated coding regions were detected.

bioinformatics↗

bioRxiv2021

TIS transformer: Re-annotation of the human proteome using deep learning

AO_SCPLOWBSTRACTC_SCPLOWThe correct mapping of the proteome is an important step towards advancing our understanding of biological systems and cellular mechanisms. Methods that provide better mappings can fuel important processes such as drug discovery and disease understanding. Currently, true determination of translation initiation sites is primarily achieved by in vivo experiments. Here we propose TIS Transformer, a deep learning model for the determination of translation start sites solely utilizing the information embedded in the transcript nucleotide sequence. The method is built upon deep learning techniques first designed for natural language processing. We prove this approach to be best suited for learning the semantics of translation, outperforming previous approaches by a large margin. We demonstrate that limitations in the model performance is primarily due to the presence of low quality annotations against which the model is evaluated against. Advantages of the method are its ability to detect key features of the translation process and multiple coding sequences on a transcript. These include micropeptides encoded by short Open Reading Frames, either alongside a canonical coding sequence or within long non-coding RNAs. To demonstrate the use of our methods, we applied TIS Transformer to remap the full human proteome.

bioinformatics↗

bioRxiv2021

A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational vocabulary of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

genomics↗

bioRxiv2021

CpG Transformer for imputation of single-cell methylomes

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSThe adoption of current single-cell DNA methylation sequencing protocols is hindered by incomplete coverage, outlining the need for effective imputation techniques. The task of imputing single-cell (methylation) data requires models to build an understanding of underlying biological processes. ResultsWe adapt the transformer neural network architecture to operate on methylation matrices through combining axial attention with sliding window self-attention. The obtained CpG Transformer displays state-of-the-art performances on a wide range of scBS-seq and scRRBS-seq datasets. Further-more, we demonstrate the interpretability of CpG Transformer and illustrate its rapid transfer learning properties, allowing practitioners to train models on new datasets with a limited computational and time budget. Availability and ImplementationCpG Transformer is freely available at https://github.com/gdewael/cpg-transformer.

bioinformatics↗

bioRxiv2020

An in-depth evaluation of annotated transcription start sites in E. coli using deep learning

The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally comprises the automatic selection of relevant nucleotide motifs from DNA sequences. To benefit from automated learning methods, new strategies are required that unveil the decision-making process of trained models. In this paper, we present several methods that can be used to gather insights on biological processes that drive any genome annotation task. This work builds upon a transformer-based neural network framework designed for prokaryotic genome annotation purposes. We find that the majority of sub-units (attention heads) of the model are specialized towards identifying DNA binding sites. Working with a neural network trained to detect transcription start sites in E. coli, we successfully characterize both locations and consensus sequences of transcription factor binding sites, including both well-known and potentially novel elements involved in the initiation of the transcription process.

genomics↗

bioRxiv2020

BRAWNIN: A sORF-encoded Peptide Essential for Vertebrate Mitochondrial Complex III Assembly

The emergence of small open reading frame (sORF)-encoded peptides (SEPs) is rapidly expanding the known proteome at the lower end of the size distribution1,2. Here, we show that the mitochondria proteome is enriched for proteins smaller than 100 a.a. (defined as SEPs). Using a mitochondrial prediction and validation pipeline for small open-reading-frame (sORF)-encoded peptides (SEPs), we report the discovery of 16 endogenous mitochondrial SEPs (mito-SEPs) associated with oxidative phosphorylation (OXPHOS). Through functional prediction, proteomics, metabolomics and metabolic flux modeling, we demonstrate that BRAWNIN (BR), a 71 amino acid peptide encoded by the C12orf73 gene, is essential for respiratory chain complex III (CIII) assembly. In human cells, BR is induced by the energy-sensing AMPK pathway, and its depletion impairs mitochondrial ATP production. In vivo, BR is enriched in muscle tissues and its maternal zygotic deletion in zebrafish causes complete CIII loss, resulting in severe growth retardation, lactic acidosis and early death. Our findings demonstrate that BR is essential for oxidative phosphorylation across vertebrate species. We propose that mito-SEPs are an untapped resource for essential regulators of oxidative metabolism.

cell biology↗

bioRxiv2019

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff.

bioinformatics↗

bioRxiv2019

A combined strategy of neuropeptide predictions and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone Nematostella vectensis

Neuropeptides are a class of bioactive peptides and are responsible for various physiological processes including metabolism, development and reproduction. Although accumulated genome and transcriptome data have reported a number of neuropeptide candidates, it still remains difficult to obtain a comprehensive view of neuropeptide repertoires due to their small and variable nature. Neuropeptide prediction tools usually work only for those peptides for which sequentially related homologs have previously been identified. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, obtaining reliable identifications of endogenous peptides is still difficult using a conventional tandem mass spectrometry-based peptide identification approach using protein database because a large search space has to be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of the prediction of in silico cleaved endogenous neuropeptides followed by peptide-spectrum matching enabling highly sensitive and reliable neuropeptide identification. This approach effectively reduces the search space of peptide-spectrum matching, and thus increases search sensitivity. To identify neuropeptides in Nematostella vectensis, a basal eumetazoan having one of the most primitive nervous systems, we scanned the Nematostella protein database for sequences displaying structural hallmarks of metazoan neuropeptides, including C/N-terminal structures and modifications. Peptide-spectrum matching was performed against the in silico cleaved peptides and successfully identified dozens of neuropeptides at high confidence. The identification of Nematostella neuropeptides structurally related the tachykinin, GnRH/AKH, neuromedin-U/pyrokinin peptide families indicate that these peptides already originated in the eumetazoan ancestor of all animal species, most likely concomitantly with the development of a nervous system.

biochemistry↗

bioRxiv2018

DeepRibo: precise gene annotation of prokaryotes using deep learning and ribosome profiling data

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network applying ribosome profiling data that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and Shine-Dalgarno region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, and is therefore evaluated on independent datasets. Through extensive validation of the model, including the use of multiple species sequence similarity and mass spectrometry, the effectiveness of the model is highlighted.

bioinformatics↗

bioRxiv2017

The proBAM and proBed standard formats: enabling a seamless integration of genomics and proteomics data.

On behalf of The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI), we are here introducing two novel standard data formats, proBAM and proBed, that have been developed to address the current challenges of integrating mass spectrometry based proteomics data with genomics and transcriptomics information in proteogenomics studies. proBAM and proBed are adaptations from the well-defined, widely used file formats SAM/BAM and BED respectively, and both have been extended to meet specific requirements entailed by proteomics data. Therefore, existing popular genomics tools such as SAMtools and Bedtools, and several very popular genome browsers, can be used to manipulate and visualize these formats already out-of-the-box. We also highlight that a number of specific additional software tools, properly supporting the proteomics information available in these formats, are now available providing functionalities such as file generation, file conversion, and data analysis. All the related documentation to the formats, including the detailed file format specifications, and example files are accessible at http://www.psidev.info/probam and http://www.psidev.info/probed.

bioinformatics↗

bioRxiv2017

REPARATION: Ribosome Profiling Assisted (Re-)Annotation of Bacterial genomes.

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence context and often underestimate the complexity of the proteome. We developed REPARATION (RibosomeE Profiling Assisted (Re-)AnnotaTION), a de novo algorithm that takes advantage of experimental protein translation evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation. REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs. Our predictions were supported by matching mass spectrometry (MS) proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to perform de novo ORF delineation in bacterial genomes irrespective of the sequence context of the reading frame.

microbiology↗