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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2024

Detecting Amino Acid Variants Using Next-Generation Protein Sequencing (NGPS)

Next-Generation Protein Sequencing (NGPS) is a single-molecule approach for characterizing protein variants, offering detailed insight into proteoforms and amino acid substitutions not easily discerned by mass spectrometry. The novel data type produced by NGPS, which is based on binding of N-terminal amino acids by fluorescently tagged recognizer proteins, requires the development of new data analysis methods and bioinformatic tools. Here, we present ProteoVue, a comprehensive bioinformatics pipeline for Single Amino Acid Variant (SAAV) detection and quantification using the Quantum-Si Platinum(R) NGPS platform. ProteoVue integrates multiple analytical components, including robust pulse-calling, recognition segment detection, fluorescence dye classification, and a neural network-driven kinetic signature database for pulse duration prediction. These components feed into a scoring-based alignment and clustering framework that enables accurate variant calling within binary peptide mixtures. We demonstrate that ProteoVue recovers expected variant ratios across diverse substitution types including residues that lack direct amino acid recognizers. While some extreme cases remain challenging, the pipeline consistently captures the key kinetic features required for variant discrimination, underscoring its potential as a versatile and powerful tool for proteomic studies. As NGPS technology matures and recognizer libraries expand, ProteoVue provides a foundation for increasingly refined variant analysis in basic research, biomarker discovery, and clinical applications.

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bioRxiv2024

Recovery of highly contiguous genomes from complex terrestrial habitats reveals over 15,000 novel prokaryotic species and expands characterization of soil and sediment microbial communities

Genomes are fundamental to understanding microbial ecology and evolution. The emergence of high-throughput, long-read DNA sequencing has enabled recovery of microbial genomes from environmental samples at scale. However, expanding the microbial genome catalogue of soils and sediments has been challenging due to the enormous complexity of these environments. Here, we performed deep, long-read Nanopore sequencing of 154 soil and sediment samples collected across Denmark and through an optimised bioinformatics pipeline, we recovered genomes of 15,314 novel microbial species, including 4,757 high-quality genomes. The recovered microbial genomes span 1,086 novel genera and provide the first high-quality reference genomes for 612 previously known genera, expanding the phylogenetic diversity of the prokaryotic tree of life by 8 %. The long-read assemblies also enabled the recovery of thousands of complete rRNA operons, biosynthetic gene clusters and CRISPR-Cas systems, all of which were underrepresented and highly fragmented in previous terrestrial genome catalogues. Furthermore, the incorporation of the recovered MAGs into public genome databases significantly improved species-level classification rates for soil and sediment metagenomic datasets, thereby enhancing terrestrial microbiome characterization. With this study, we demonstrate that long-read sequencing and optimised bioinformatics, allows cost-effective recovery of high-quality microbial genomes from highly complex ecosystems, which remain the largest untapped source of biodiversity for expanding genome databases and filling in the gaps of the tree of life.

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bioRxiv2025

Long-Term Effects of Radiation Therapy on Cerebral Microvessel Proteome: A Six-Month Post-Exposure Analysis

BackgroundRadiation therapy (RT) treats primary and metastatic brain tumors, with about one million Americans surviving beyond six months post-treatment. However, up to 90% of survivors experience RT-induced cognitive impairment. Emerging evidence links cognitive decline to RT-induced endothelial dysfunction in brain microvessels, yet in vivo studies of endothelial injury remain limited. Investigating the molecular and cellular pathways connecting RT, endothelial dysfunction, and cognitive impairment is vital for developing targeted interventions. This study examines proteomic changes in cerebral microvessels following RT. MethodsWe conducted a comprehensive quantitative analysis comparing the proteome in cerebral microvessels from five control mice and five irradiated mice (12 Gy) 6 months after RT. Bioinformatics analyses included gene ontology (GO) enrichment, Mitocarta analysis, Ingenuity Pathway Analysis (IPA), and iPathwayGuide. Predictions from the analyses were validated by western blotting. ResultsOur data identified significant dysregulation of 414 proteins following RT, with 157 upregulated and 257 downregulated. Gene ontology analysis indicated that the majority of the dysregulated proteins were part of various metabolic pathways. Cross referencing with Mitocarta revealed a significant presence of mitochondrial proteins among the dysregulated proteins, indicating potential mitochondrial metabolic dysfunction. Further investigation with IPA analysis uncovered 76 enriched canonical pathways, 34 transcription regulators, 6 nuclear receptors, and 5 growth factors involved in RT-induced damage responses in cerebral microvessels. IPA canonical pathway analysis predicted mitochondrial dysfunction due to inhibition of various metabolic pathways in the irradiated group. Validation with western blotting confirmed the bioinformatics predictions from the proteomic dataset. ConclusionsOur data show significant proteomic changes in cerebral microvessels 6 months post-radiation, including oxidative phosphorylation, the TCA cycle, and glycolysis, suggesting metabolic mechanisms of RT-induced microvascular dysfunction.

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bioRxiv2025

Parapipe: a Pipeline for Handling Parasite NGS Datasets and its Application to Cryptosporidium

0.0Cryptosporidium, a protozoan parasite of significant public health concern, is responsible for severe diarrheal diseases, particularly in immunocompromised individuals and young children in resource-limited settings. Analysis of whole genome next generation sequencing (NGS) data is a critical next step in improving our understanding of Cryptosporidium epidemiology, transmission dynamics, and genetic diversity. However, effective analysis of NGS data in a public health context necessitates the development of robust, validated bioinformatics tools. Here, we present Parapipe, a modular ISO accreditable bioinformatics pipeline designed for high-throughput processing and analysis of Cryptosporidium NGS datasets. Built using Nextflow DSL2 and containerized with Singularity, Parapipe is portable, scalable, and capable of end-to-end analyses, including quality control, variant calling, multiplicity of infection (MOI) investigations and phylogenomic clustering analysis. Using both simulated and real-world datasets, we demonstrate Parapipes ability to resolve genetic heterogeneity, identify mixed infections, and generate high-resolution phylogenomic insights. Here, we use it to carry out a comparison between whole genome single nucleotide polymorphism (wgSNP) typing and the conventionally used gp60 molecular typing scheme. Compared to existing pipelines, Parapipe uniquely integrates MOI analysis, enabling the differentiation of mixed infections and supporting epidemiological investigations. Parapipes design facilitates integration with geographic, demographic, epidemiological and environmental data, enhancing its utility for tracking transmission pathways and outbreak sources. Parapipe represents a significant advance in utilising genomics for public health surveillance of Cryptosporidium, offering a streamlined and reproducible framework for analysis with potential application to other pathogenic protozoa. By automating complex workflows and enabling detailed genomic characterization, Parapipe provides a valuable tool for public health agencies and researchers, supporting efforts to mitigate the global burden of cryptosporidiosis.

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bioRxiv2025

Genomic reconstruction of Bacillus anthracis from complex environmental samples enables high throughput identification and lineage assignment in Pakistan

Bacillus anthracis, the causative agent of anthrax, is a highly virulent zoonotic pathogen primarily affecting domesticated and wild herbivores. Human exposure to B. anthracis is primarily through contact with infected animals or contaminated animal products. In Pakistan, where livestock vaccines are largely unavailable and infected carcasses are often disposed of improperly, the risk to humans, wildlife and livestock is significant. Currently, diagnosis of anthrax infections and outbreak tracing necessitates the isolation and culturing of B. anthracis, a process that requires BSL-3 facilities. In this study, we show that positive identification, genome reconstruction and lineage assignment can be accomplished using bioinformatic analysis of DNA extracted directly from environmental samples that would otherwise provide the starting material for isolation and culturing. This approach does not require laboratory target enrichment as is necessary for other pathogens, due in part to the extremely high bacterial load in the bloodstream in the deceased animals. Using these methods, we greatly expand the knowledge of endemic B. anthracis in Pakistan. We provide the first reference B. anthracis genomes from Pakistan since the 1970s and identify A.Br.014 Aust94 as a minor circulating sublineage alongside dominant A.Br.047 Vollum. Future work will focus on limits of detection and will determine if this bioinformatic method can be expanded more broadly for B. anthracis or other pathogens to replace typical culture-based methods.

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bioRxiv2025

Fast and Scalable Parallel External-Memory Construction of Colored Compacted de Bruijn Graphs with Cuttlefish 3

The exponential growth of genomic data has created an urgent need for scalable sequence analysis algorithms. De Bruijn graphs--along with their colored and compacted variants--have become essential tools in modern bioinformatics pipelines. Colored compacted de Bruijn graphs condense repetitive sequence information, significantly reducing the data burden on downstream analyses like genome assembly, metagenomic clustering, and pan-genomics. Since constructing uncompacted graphs becomes computationally prohibitive at scale, direct construction methods for colored compacted de Bruijn graphs are essential for the scalability of downstream analyses. We present Cuttlefish 3, a parallel external-memory algorithm that delivers state-of-the-art performance for constructing colored compacted de Bruijn graphs. Our approach introduces three algorithmic innovations that enable efficient scaling to massive datasets while maintaining high performance. First, we develop an optimized technique for accelerating local subgraph contractions. Second, we design a deterministic parallel algorithm based on list-ranking to efficiently merge local solutions. Third, we introduce a novel combinable hash-based method for identifying and tracking color-changing nodes, enabling rapid color-set extraction. We evaluate Cuttlefish 3 on diverse large-scale genomic datasets. In our benchmarks, Cuttlefish 3 achieves 3.29-4.09x speedup over GGCAT, the current state-of-the-art tool, while maintaining comparable memory usage. These performance gains make Cuttlefish 3 a practical solution for representing and analyzing the growing volumes of genomic data in modern bioinformatics workflows. Cuttlefish 3 is implemented in C++17 and is available at https://github.com/COMBINE-lab/cuttlefish.

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bioRxiv2025

High Dimensional Proteomic Multiplex Imaging of the Central Nervous System Using the COMET(TM) System

Sequential multiplex methodologies such as Akoya CODEX, Miltenyi MACSima, Rarecyte Orion, and others require modification of the antibodies by conjugation to an oligo or a specific fluorophore which means the use of off-the-shelf reagents is not possible. Modifications of these antibodies are typically performed via reduction chemistry and thus require verification and validation post-modification. Fixed panels are therefore developed due to various limitations including spectral overlap that creates spectral unmixing issues, steric hindrance, harsh antibody removal, and tissue degradation throughout the labeling. As such, a complex interrogation evaluating multiple study hypotheses and/or endpoints requires the development of sequential panels, reconstruction, and realignment of the tissue that necessitate a z-stack strategy. Standardized antibody panels are typically fixed and require substantial validation efforts to modify a single target and thus do not evolve with the pace of research interests. To increase the throughput of profiling cells within the human central nervous system (CNS), we developed and validated a CNS-specific library with an associated analysis platform using the newly developed Lunaphore COMETTM platform. The COMETTM is an automated staining/imaging instrument integrating a reagent deck for staining buffers and off-the-shelf label-free primary antibodies and fluorophore-labeled secondary antibodies, which feed into a circular plate holding up to 4 slides that are automatically imaged in microscope-operated control software. For this study, standard formalin fixed paraffin embedded histology slides are used. However, the COMET is capable of imaging fresh-frozen samples using specialized settings. Our methodologies address an unmet need in the neuroscience field while leveraging prior developmental efforts in the domain of immunology spatial profiling. Cataloging and validating a large series of antibodies on the COMET along with developing CNS autofluorescence management strategies while optimizing standard operating procedures have allowed for the visualization at the subcellular level. Forty analytes can be used to analyze one specimen which has clinical utility in cases in which the CNS can only be sampled by biopsy. CNS biopsies, depending on the anatomical location, can have limited available volume to a degree that requires prioritization and restriction to select analysis. In-depth bioinformatic imaging analysis can be done using standard bioinformatic tools and software such as Visiopharm(R). These results establish a general framework for imaging and quantifying cell populations and networks within the CNS while providing the scientific community with standard operating procedures.

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bioRxiv2025

Direct construction of sparse suffix arrays with Libsais

Pattern matching is a fundamental challenge in bioinformatics, especially in the fields of genomics, transcriptomics and proteomics. Efficient indexing structures, such as suffix arrays, are critical for searching large datasets. While sparse suffix arrays offer significant memory savings compared to full suffix arrays, they typically still require the construction of a full suffix array prior to a sampling step, resulting in substantial memory overhead during the construction phase. We present an alternative method to directly construct the sparse suffix array using a simple, yet powerful text encoding, in combination with the widely used Libsais library. This approach bypasses the need for constructing a full suffix array, reducing memory usage by 63% and construction time by 55% when building a sparse suffix array with sparseness factor 3 for the entire UniProt knowledgebase. The method is particularly effective for applications with small alphabets, such as a nucleotide or amino acid alphabet. An open-source implementation of this method is available on GitHub, enabling easy adoption for large-scale bioinformatics applications.

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bioRxiv2025

VueGen: Automating the generation of scientific reports

MotivationThe analysis of omics data typically involves multiple bioinformatics tools and methods, each producing distinct output files. However, compiling these results into comprehensive reports often requires additional effort and technical skills. This creates a barrier for non-bioinformaticians, limiting their ability to produce reports from their findings. Moreover, the lack of streamlined reporting workflows impacts reproducibility and transparency, making it difficult to communicate results and track analytical processes. ResultsWe present VueGen, a tool that automates the creation of reports from bioinformatics outputs, allowing researchers with minimal coding experience to communicate their results effectively. With VueGen, users can produce reports by simply specifying a directory containing output files, such as plots, tables, networks, Markdown text, and HTML components, along with the report format. Supported formats include documents (PDF, HTML, DOCX, ODT), presentations (PPTX, Reveal.js), Jupyter notebooks, and Streamlit web applications. To showcase VueGens functionality, we present two case studies and provide detailed documentation to help users generate customized reports. Availability and implementationVueGen is distributed as a Python package, Docker image, nf-core module, and desktop application. The source code is freely available on https://github.com/Multiomics-Analytics-Group/vuegen under the MIT license. Documentation is provided at https://vuegen.readthedocs.io/.

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bioRxiv2025

High-throughput RNA and Single-cell RNA Sequencing to Identify Sepsis associated Molecular Mechanisms and the Blood Immune Cell Landscape

Sepsis, a leading cause of death in intensive care units (ICUs), is a complex systemic inflammatory response to infection with high morbidity and mortality. Its pathogenesis involves dysregulated inflammation, immune dysfunction, and metabolic alterations, particularly in lactate metabolism. This study employed bioinformatics analyses to explore sepsis mechanisms and identify potential therapeutic targets. We analyzed two GEO datasets and found the lactate metabolism pathway significantly enriched in sepsis patients. Seventeen key genes were identified and used to classify sepsis into two subtypes via WGCNA and consensus clustering. These subtypes exhibited distinct clinical and immune profiles. Seven hub genes (BPI, HGF, HP, LCN2, LTF, MMP8, RETN) showed differential expression between subtypes and may serve as diagnostic biomarkers. MMP8 was identified as a critical regulator in lactate metabolism, with associated miRNAs and transcription factors predicted. Single-cell analysis revealed altered immune cell compositions and interactions in sepsis patients. Our findings offer novel insights into sepsis pathogenesis and potential therapeutic strategies targeting lactate metabolism and immune regulation. Keywords: sepsis; lactate metabolism; bioinformatics; immune microenvironment

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bioRxiv2025

SeroBA(v2.0) and SeroBAnk: a robust genome-based serotyping scheme and comprehensive atlas of capsular diversity in Streptococcus pneumoniae

The unprecedented number of Streptococcus pneumoniae (the pneumococcus) genomes sequenced in recent years has accelerated the discovery of novel serotypes and highlighted the genetic diversity both between and within each serotype. A novel serotype should demonstrate a distinct cps locus, capsular structure, and serological profile. In only the past four years, nine new serotypes have been identified. Accurate and timely serotyping of pneumococcal isolates is key to understanding its global distribution, evolution, and the response of the bacterial population to vaccination. However, current bioinformatics serotyping tools are infrequently updated, and struggle to accommodate the rapid discovery of new serotypes in a timely manner. To address these limitations, we built a comprehensive and curated library (SeroBAnk) encompassing all known pneumococcal serotypes; this resource is presented as an atlas on a dedicated publicly accessible webpage (https://www.pneumogen.net/gps/#/serobank). Building upon this resource, we developed SeroBA(v2.0), a tool with an easy-to-update database that can accurately identify 102 of 107 known pneumococcal serotypes (except for serotypes 24B, 24C, 24F, 7D and 6H) and 18 genetic subtypes within serotypes 6A, 6B, 11A, 19A, 19F and 33F. We validated SeroBA(v2.0) on 26,306 genomes from the Global Pneumococcal Sequencing project, reference isolates and simulated reads derived from the reference genetic sequences of capsular polysaccharide biosynthetic (cps) locus and showed that SeroBA(v2.0) can reliably detect the nine recently discovered serotypes. Additionally, we show that in silico serotypes inferred by SeroBA(v2.0) had high concordance with phenotypic serotypes determined by either Quellung or latex agglutination at the serotype level (88.9%; 15,945/17,933), and at the serogroup level (91.9%; 16,480/17,933). Finally, we propose a community-contribution based approach to ensure that SeroBA(v2.0) is maintained and updated as novel serotypes continue to be discovered. The global community can submit putative novel serotypes through our public repository on GitHub (https://github.com/GlobalPneumoSeq/seroba/issues). The submitted putative novel serotypes will be curated based on the genetic sequence of cps region, capsular structure and serological profile by people of relevant expertise in the field. SeroBA(v2.0) can be accessed at https://github.com/GlobalPneumoSeq/seroba. Data summaryGenome sequences are available in the European Nucleotide Archive (ENA) and are also available alongside metadata on the Monocle Database available at https://data.monocle.sanger.ac.uk/. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Impact StatementThe polysaccharide capsule has been an effective vaccine antigen against diseases caused by Streptococcus pneumoniae (the pneumococcus). The pneumococcal conjugate vaccine has been estimated to have halved pneumococcal-related childhood mortality over 15 years (2000-2015). We collated the genetic locus and capsular structure of each known capsule type (serotype), alongside with pneumococcal vaccine formulation and licensure history, into a single webpage (SeroBAnk), providing a valuable resource for basic research and vaccine development. With increasing use of whole genome sequencing in clinical and public health laboratories, we also provided a fast and accurate bioinformatics tool, SeroBA(v2.0), to identify 102 pneumococcal serotypes, alongside a proposed system to expand SeroBA(v2.0) to include new serotypes as they are discovered, ensuring that the tool remains valuable to the global research community in the long-term.

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bioRxiv2025

Benchmarking circRNA Detection Tools from Long-Read Sequencing Using Data-Driven and Flexible Simulation Framework

AO_SCPLOWBSTRACTC_SCPLOWCircular RNAs (circRNAs) are unique non-coding RNAs with covalently closed loop structures formed through backsplicing events. Their stability, tissue-specific expression patterns, and potential as disease biomarkers have garnered increasing attention. However, their circular structure and diverse size range pose challenges for conventional sequencing technologies. Long-read Oxford Nanopore (ONT) sequencing offers promising capabilities for capturing entire circRNA molecules without fragmentation, yet the effectiveness of bioinformatic tools for analyzing this data remains understudied. This study presents the first comprehensive benchmark comparison of three specialized tools for circRNA detection from ONT long-read data: CIRI-long (Zhang et al., 2021), IsoCIRC (Xin et al., 2021), and circNICK-Irs (Rahimi et al., 2021). To address the lack of standardized evaluation frameworks, we developed a novel computational pipeline, open-source and freely available, to generate realistic simulated circRNA ONT long-read datasets. Our pipeline integrates several molecular features of circRNAs extracted from established databases and real datasets into NanoSim tool (Hafezqorani et al., 2020) and outputs FASTQ reads reflecting therefore biological diversity and technical properties. We systematically assessed tool performance across key metrics, including precision, recall, specificity, accuracy, and F1 score. Our analysis revealed distinct performance profiles: while all tools exhibited high specificity, they varied in precision and their ability to detect different circRNA subtypes, often showing limited sensitivity and precision. Notably, the overlap in detected circRNAs among tools was relatively low. Additionally, computational efficiency varied significantly across the tools. This suggests that relying on a single tool might not be ideal, and combining tools or improving algorithms could be necessary for more accurate circRNA detection from ONT data. This benchmark provides valuable insights for researchers selecting appropriate tools for circRNA studies using ONT sequencing. Furthermore, our customizable simulation framework, offering a resource to optimize detection approaches and advance bioinformatic tool development for circRNA research is freely available at: https://gitlab.com/bioinfog/circall/nano-circ.

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bioRxiv2025

mimicDetector: a pipeline for protein motif mimicry detection in host-pathogen systems

MotivationMolecular mimicry is a widespread strategy used by pathogens to evade the host immune system and manipulate other host cellular processes. Detecting these events--where pathogen proteins resemble host molecules--is challenging due to limitations in the sensitivity, specificity, and scalability of current bioinformatics tools. The challenges are pronounced when identifying subtle similarities in short protein fragments. ResultsWe present mimicDetector, an optimized bioinformatic pipeline for systematically identifying protein-level molecular mimicry between pathogens and their hosts. mimicDetector builds on existing k-mer-based approaches with three key improvements: (i) improved sensitivity for short-sequence alignments using the PAM30 substitution matrix and tuned BLASTP parameters; (ii) a revised k-mer filtering strategy based on bitscore differences rather than percent identity; (iii) the removal of overly conservative homologue exclusion steps. Applied to 17 globally important pathogens, mimicDetector identified a broad and biologically plausible set of mimicry candidates, including helminth proteins mimicking components of the human complement system and a Leishmania infantum mimic of Reticulon-4, a regulator of immune cell recruitment. Availability and implementationmimicDetector is freely available at https://github.com/Kayleerich/mimicDetector/, implemented in Python, and compatible with Unix-based systems.

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bioRxiv2025

Dotplotic: a lightweight visualization tool for BLAST+ alignments and genomic annotations

With the development of sequencing technologies, chromosome-level genome assemblies have become increasingly common across various organisms, including non-model species. BLAST+ is one of the most widely used bioinformatics tools for computing sequence alignments, offering numerous optimizations for speed and scalability. Dot plots, which visualize the similarity between two sequences, are widely used in biological research. However, while many dot plot-generating programs exist, most rely on their own alignment algorithms, and it is uncommon to visualize external BLAST results directly. Here, we present Dotplotic, a lightweight Perl program that generates dot plot-like visualizations based on BLAST output in tabular format. Dotplotic visualizes each alignment as a line connecting the start and end points of the query and subject sequences, with a gradient color indicating sequence identity. It allows users to overlay annotation data from external files onto the plot. Although command-line-based, Dotplotic requires only core Perl modules and is implemented as a single script, making it easy to install and run across platforms. The program supports standard input for both BLAST results and annotation files, enabling flexible visualization under various conditions, such as filtering specific alignments or displaying selected genomic features like genes or repeats. Dotplotic is an efficient, portable, and easy-to-use visualization tool that enhances the exploration of sequence alignments and serves as a valuable resource for both bioinformatics and broader biological research.

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bioRxiv2025

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform

Isobaric mass tags, such as iTRAQ and TMT, are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: an E. coli dataset with 13 spike-in proteins, the clear cell renal cell carcinoma (ccRCC) whole proteome and phosphopeptide-enriched datasets from the Clinical Proteomic Tumor Analysis Consortium, and two human cell lysate datasets showcasing the latest advances with the Astral instrument and TMT 35-plex reagents. Integrated into the widely used FragPipe computational platform (https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluate the FragPipe/TMT-Integrator analysis pipelines performance against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe/TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, identifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and delivers overall more robust quantification performance compared to other tools.

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bioRxiv2025

A simple workflow to identify novel Small Linear Motif (SLiM)-mediated interactions with AlphaFold

Short linear motifs (SLiMs) are highly compact interaction modules embedded within disordered protein regions and are increasingly recognized for their central role in maintaining cellular homeostasis. Due to their small size, degeneracy and transient binding, SLiMs remain difficult to detect both experimentally and computationally. Here, we show that AlphaFold, used via ColabFold, offers a practical and accessible alternative for in-silico SLiM discovery. Unlike previous studies focused on structural accuracy, we evaluated AlphaFolds capacity to reveal SLiMs independently of model quality. To this end, we benchmarked several scoring metrics and showed that AlphaFold2 combined with MiniPAE yields the best performance, outperforming AlphaFold3 in this context. Building on these findings, we also provide a streamlined and cost-effective workflow for SLiM prediction requiring no installation or local computation. To overcome challenges associated with SLiM validation, we also introduce a highly sensitive detection method based on proximity labeling in living cells. This workflow was used to predict the occurrence of SLiMs that mediate binding to ribosomal protein S6 kinase A3 (RPS6KA3 or RSK2). By leveraging Colabfold and MiniPAE available through Colab notebooks, our approach provides a scalable and widely accessible strategy for identifying functional SLiMs in proteins of interest. MiniPAE can be accessed at https://github.com/martinovein/MiniPAE Short descriptionMartin Veinstein is a PhD student in Biomedical Sciences at the de Duve Institute, UCLouvain, Belgium. He specializes in Small Linear Motifs (SLiMs) in the context of host-virus interactions and has developed strong expertise in bioinformatics, structural biology, and predictive modeling. Victor J is a unfergradiate student at the ECAM Brussels Engineering School, Haute Ecole "ICHEC-ECAM-ISFSC", Brussels, Belgium. His activities span form September to November 2023. B.I. Iorga is a CNRS Research Director at the Institut de Chimie des Substances Naturelles in Gif-sur-Yvette, France. His research focuses among others on methodological developments in molecular modeling and the in-silico prediction of antibiotic resistance using machine learning and deep learning approaches. Raphael Helaers is a Senior Investigator and leads bioinformatics infrastructure at the de Duve Institute, UCLouvain, Belgium. He has developed strong expertise in next-generation sequencing and software development, along with a deep interest in biology, genetics, and evolution. Thomas Michiels is a Full Professor and researcher at the de Duve Institute, UCLouvain, Belgium. His research focuses on virus-mediated subversion of the innate immune response. Frederic Sorgeloos is an adjunct Professor at the INRS, Laval, Canada. He currently focuses on the subversion of cellular homeostasis through small linear peptides encoded by viral and bacterial pathogens. Short abstractVarious AlphaFold2/3 scoring metrics were systematically benchmarked for their ability to detect Small Linear Motifs (SLiMs) Based on this evaluation, a user-friendly and cost-effective in-silico workflow is proposed to identify novel SLiMs-targeting proteins The utility of this workflow is demonstrated through the prediction of previously uncharacterized SLiMs interacting with RSK kinases. A sensitive in-vitro assay is proposed to streamline the validation of low-affinity SLiM-target interactions. Together, our workflow and associated validation assay offer an integrated pipeline for the discovery and validation of SLiM-mediated protein-protein interactions.

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bioRxiv2025

The AlphaFold Database Ages

The AlphaFold database provides 200M protein structures predicted by AlphaFold2 and released in 2022 from sequences in UniProt in April 2021. However, of the 20,504 full-length human structures in the AlphaFold database, 631 entries conflict with UniProt (version 2025_03 i.e. release 3 of 2025 published Jun 18, 2025); and there is a similar discrepancy for other species. This highlights how bioinformatics resources, as exemplified by the AlphaFold database can rapidly age. Time flies in bioinformatics.

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bioRxiv2025

ALPAR: Automated Learning Pipeline for Antimicrobial Resistance

The field of machine learning in antimicrobial resistance (AMR) research has experienced rapid growth, fueled by advancements in high-throughput genome sequencing and growing capacity of computational resources. However, the complexity and lack of standardized data preparation and bioinformatic analyses present significant challenges, especially to newcomers to the domain. In response to these challenges, we introduce ALPAR (Automated Learning Pipeline for Antimicrobial Resistance), a comprehensive AMR data analysis tool covering the entire process from processing of raw genomic data to training machine learning models to interpretation of results. Our method relies on a reproducible pipeline that integrates widely used bioinformatics tools, presenting a simplified, automatic workflow specifically tailored for single-reference AMR analysis. Accepting genomic data in the form of FASTA files as input, ALPAR facilitates generation of machine learning-ready data tables and both training of machine learning and execution of genome-wide association studies (GWAS) experiments. Additionally, our tool offers supplementary functionalities such as phylogeny-based analysis of distribution of mutations, enhancing its utility for researchers. Our tool is accessible through the ALPAR GitHub page (https://github.com/kalininalab/ALPAR) and installable via conda (https://anaconda.org/kalininalab/ALPAR).

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