start_Datetime

Room

Tutorial VT1: Visualising and interpreting your -omics results using ggplot2 and R

This full-day tutorial introduces participants to the principles of impactful data visualisation and equips them with the skills to create publication-ready visualisations for -omics data. Designed for beginners with basic knowledge of the R programming language, the tutorial will guide attendees through creating and interpreting key visualisations such as volcano plots, box plots, heatmaps, dot plots, and network diagrams. Participants will work with real biological datasets in Quarto notebooks and learn how to use tools like ggplot2, ComplexHeatmap, igraph, ggraph, and ClusterProfiler. Through hands-on coding exercises and interactive lectures, attendees will develop an intuition for ggplot2’s grammar of graphics, best practices in data visualisation, and the application of functional enrichment methods to contextualise results. Whether you are a student or researcher wanting to improve your visualisation skills, or a computational biologist looking to enhance the presentation of your results, this tutorial will provide the tools and knowledge to produce professional-quality figures.

Tutorial VT6: Beyond Bioinformatics: Snakemake for Versatile Computational Workflows

Snakemake is a powerful, Python-based workflow management system that revolutionises how computational tasks are designed, executed and reproduced. By allowing researchers to define workflows as a series of interconnected rules, Snakemake simplifies complex computational pipelines and ensures reproducibility across diverse scientific domains. This intensive workshop introduces participants to the full potential of Snakemake, moving beyond traditional bioinformatics applications to demonstrate its versatility in machine learning and data analysis. Designed for researchers dealing with complex computational challenges, this tutorial will explore Snakemake’s capabilities through practical, hands-on examples that span multiple disciplines. Participants will learn how to create robust, scalable, and efficient workflows that can adapt to various research challenges. By the end of the workshop, attendees will have a comprehensive understanding of workflow management principles and the skills to implement sophisticated pipelines using Snakemake.

Tutorial VT5: Comprehensive Bioinformatics and Statistical Approaches for High-Throughput Sequencing Data Analysis, Including scRNA-seq, in Biomarker Discovery

With the significant advancements in genomic profiling technologies and the emergence of selective molecular targeted therapies, biomarkers have played an increasingly pivotal role in both the prognosis and treatment of various diseases, most notably cancer. This workshop is designed to begin with an introductory overview of basic concepts of biomarkers, the diverse categories of biomarkers, commonly employed biotechnologies for biomarker detection, with a special focus on gene mutation and gene expression using DNA-seq, RNA-seq, and scRNA-seq data. Furthermore, we will discuss processes of biomarker discovery and development, and outlining the key steps involved and the current analytical methodologies utilized. Following this, we will discuss the identification of driver gene mutations and altered gene expression, using The Cancer Genome Atlas (TCGA) lung cancer data and PBMC scRNA-seq data as illustrative examples with using R code as practical demonstrations to enhance understanding. In the latter part of this workshop, we will discuss commonly utilized biostatistics and bioinformatics tools, including data visualization, survival analysis and machine learning methods, which are employed to predict disease progression and patient survival outcomes based on these critical biomarkers. By the conclusion of this course, participants will have acquired a broad and fundamental understanding of biomarker discovery, particularly in cancer. This encompasses key concepts, data sources, data analysis techniques, and interpretation strategies. Such expertise will equip participants with the knowledge necessary in contributing to the development of precision medicine in cancer patient treatment.

Tutorial VT8: Generative AI for Single-Cell Perturbation Modeling: Theoretical and practical considerations

Single-cell perturbation modelling is revolutionising how we understand the effects of genetic interventions, drugs, and cellular stimulants on molecular and cellular physiology. This half-day virtual tutorial will introduce participants to highly performant Generative AI tools—scGEN and scPRAM—designed to simulate perturbations on single-cell datasets and extrapolate to unseen conditions. Through concise presentations and hands-on exercises, attendees will explore the theoretical underpinnings of these tools, preprocess single-cell data, train generative models, and interpret results using advanced metrics such as R², E-distance, and Maximum Mean Discrepancy as well as dimensionality reduction techniques. Special focus will be placed on challenging scenarios like extrapolating to unseen patient responses and cross-species predictions, leveraging benchmarking insights from the EU BH 2024 Perturb-Bench initiative. Participants will leave with actionable knowledge to implement, evaluate, and benchmark generative perturbation models, supported by practical resources hosted on Google Colab, Jupyter Book, and GitHub.

Tutorial VT2: OmicsViz: Interactive Visualization and ML for Omics Data

Data Science and Machine Learning are intricately connected, particularly in computational biology. In a time when biological data is being produced on an unprecedented scale — encompassing genomic sequences, protein interactions, and metabolic pathways- meeting the demand has never been more crucial. Data visualization plays a crucial role in biological data sciences since it allows the transformation of complex, often incomprehensible raw data into visual formats that are easier to understand and interpret. This allows biologists to recognize patterns, anomalies, and correlations that would otherwise be lost in the sheer volume of data. In addition, machine learning (ML) has brought about a revolution in the analysis of biological data. Exploiting extensive datasets, ML provides tools to model complex systems and generate predictions. Indeed, ML algorithms excel at uncovering subtle patterns in data, contributing to tasks like predicting protein structures, comprehending genetic variations and their implications for diseases, and even facilitating drug discovery by predicting molecular interactions. The integration of data visualization and machine learning is particularly powerful. In particular, visualization may aid in interpreting machine learning models, allowing biologists to understand and trust their predictions. It could also help fine-tune these models by identifying outliers or anomalies in the data. Due to its remarkable capability, there has been a surge in the development and application of tools that combine data visualization and machine learning in biology. Platforms that integrate these technologies enable biologists to conduct comprehensive analyses without needing deep expertise in computer science. Assuredly, this democratization of data science and ML has empowered more and more biologists to engage in sophisticated, data-driven research.

Tutorial VT3: Computational approaches for deciphering cell-cell communication from single-cell transcriptomics and spatial transcriptomics data

Tissues and organs are complex and highly-organized systems composed of diverse cells that work together to maintain homeostasis, drive development and mediate complex disease progression as Myocardial Infarction (Kuppe et al. 2022). A key focus of modern biology is understanding how heterogeneous populations of cells coexist and communicate with each other (intercellular signaling), how they properly respond (intracellular signaling) within a tissue and organ system and how these processes vary across different experimental conditions (comparative analysis). Recently, a rapid expansion of computational tools exploring the expression of ligand and receptor has enabled the systematic inference of cell-cell communication from single-cell transcriptomics and spatial transcriptomics data (Armingol et al. 2021; Armingol, Baghdassarian, and Lewis 2024). These are crucial in unravelling the complex landscape of biological systems. This tutorial aims to provide a comprehensive introduction to computational approaches for cell-cell communication inference using high throughput transcriptomics data. It covers the fundamental concepts of cellular communication and assumptions underlying analysis focusing on the main computational methods used in the field. This includes computational approaches for inter-cellular communication inference (CellphoneDB (Efremova et al. 2020); LIANA (Dimitrov et al. 2022, 2024)) and for intra-cellular signals communication (scSeqComm (Baruzzo, Cesaro, and Di Camillo 2022); NicheNet (Browaeys, Saelens, and Saeys 2019)). Next, we will describe approaches for comparative analysis of cell-cell networks in distinct biological conditions (CrossTalkeR (Nagai et al. 2021)) and methods for spatially resolved cell-cell communications (Ischia (Regan-KomitoDaniel 2024); DeepCOLOR (Kojima et al. 2024)). In the first part of the tutorial, participants will be introduced to the theoretical basis of state-of-the-art computational approaches and will learn how to use representative tools for inferring intercellular signaling and intracellular signaling pathways. In the second part, we will focus on the comparative analyses, i.e. changes of cell-cell communication in two conditions, and subsequently highlighting the unique insights spatial transcriptomics data can provide for understanding tissue architecture and cellular communication. Both sections will be followed by a hands-on component based on the analysis of single cell and spatial transcriptomics data from the myocardial infarction atlas (Kuppe et al. 2022). To promote transparency, all the codes, software tools and the datasets used throughout the tutorial will be available and accessible through open-access repositories (e.g. GitHub repositories or Zenodo platforms).

Tutorial VT9: Biomedical text mining for knowledge extraction

Modern bioinformatics analyses rely heavily on the existing knowledge of the role of genes and mutations in different diseases, as well as the complex interactions between genes, proteins and drugs. However, access to this information is often limited for many biomedical problems, especially niche areas, due to lack of knowledge bases and large curation costs. The information is often locked in the text of the original research papers. Machine learning methods, particular natural language processing techniques, offer an automated approach to extracting knowledge from the research literature to build bespoke knowledge bases for scientists’ needs. This tutorial will provide a hands-on introduction to the core tasks in biomedical natural language processing (BioNLP). These include identifying mentions of important concepts (e.g. phenotypes, cell-lines, etc) and extracting nuanced relationships between them. Finally, it will show how large language models have changed how information can be quickly extracted, but also highlight their challenges.

Tutorial VT4: An applied genomics approach to crop breeding: A suite of tools for exploring natural and artificial diversity

The urgent need for crop improvement is hindered by the lack of precision in crop breeding. Although significant progress has been made in genomics, many causal genes of important agronomic and nutritional traits remain unknown. This is due to the inefficient identification of causal genetic features in candidate genes. With the growing body of sequencing data and phenotype information, current advances in genomics and the development of bioinformatics tools offer improvements in candidate gene selection. In this workshop, SoyHUB, the suite of tools, strategies, and solutions for soybean applied genomics, will be presented along with its extension to other crops at KBCommons. Our methodology for data integration, curation, reuse, and leveraging will be highlighted. Practical utilization of integrated data with the tools will be demonstrated. We will focus on the selection of best-performing markers, identification of causal genes, and exploration of alleles and genomic variation. We will cover simple Mendelian traits, showing how to analyze variation in protein-coding regions and promoters and touch on copy number variation. Solutions for complex cases, such as multiple independent alleles in a single gene or quantitative traits, will also be introduced. This workshop will highlight recent achievements in leveraging big data to improve precision in GWAS-driven discoveries and, therefore, accelerate the breeding of soybean and other crops.

Tutorial VT7: Assessing and Enhancing Digital Accessibility of Biological Data and Visualizations

As computational biologists, we produce biological datasets, visualizations, and computational tools. Our shared goal is to make our data and tools widely usable and accessible. However, we often fail to meet the needs of certain groups of people. Our recent comprehensive evaluation [1] (https://inscidar.org) shows that biological data resources are largely inaccessible to people with disabilities, with severe accessibility issues in almost 75% of all 3,112 data portals included in the study. To address the critical accessibility barriers, it is important to increase awareness of accessibility in the community and teach the workforce practical ways to enhance the accessibility of biological data resources. While there are existing resources and training opportunities that focus on content that includes no or little data, there is a lack of solutions and resources that provide insights into how to make data-intensive content accessible. Our tutorial is designed to help participants understand the importance of digital accessibility in computational biology and practice various approaches to test and implement digital accessibility of biological data and visualizations. We will demonstrate our evaluation results [1] to help participants understand the critical barriers in biological research and education for people with disabilities, such as those involving vision, cognitive, and physical function. We will use hands-on examples that are familiar to and widely used by computational biologists, such as computational notebooks, genome browsers, and other visualizations. Our tutorial will conclude by introducing open problems and recent innovations, such as the accessibility of interactive genomics data visualizations [2]. We will ensure that our tutorial and all the materials are accessible.

SCS: Student Council Symposium

SCS: Student Council Symposium

Tutorial IP1: Machine Learning for Omics: Best practices and Real-Life Insights with TidyModels

Omics data analysis presents unique challenges due to its high dimensionality and complexity. Supervised machine learning (ML) offers powerful tools for gaining insights from these data but currently faces a crisis of reproducibility due to poor adherence to best practices when undertaking feature selection, model evaluation, and needs for further interpretability. This full-day tutorial introduces participants to the common pitfalls and best practices of applying ML to omics research. It exemplifies good practice through example using the Tidymodels framework for ML workflows in R, tailored to omics applications. The course will feature a mixture of lectures, quizzes, real-life coding tutorials and hands-on practicals with 1-1 support. Example applications will illustrate regression analysis with methylation clocks, gene prioritisation and classification with cancer biomarker discovery. Special attention will be paid to challenges in working with highly multivariate data and integrating various data types as well as providing tips to extract meaningful insights from complex data. Beginner-level R skills are required, and attendees will leave with practical skills to apply Tidymodels to their own datasets.

Tutorial IP2: Massively parallel reporter assays in functional regulatory genomics and as part of the IGVF data resource

This tutorial is designed to empower bioinformatics researchers with the knowledge and skills to effectively utilize Massively Parallel Reporter Assays (MPRAs) data in their work. MPRAs are gaining wider applications across the functional genomics community and are used as part of the Impact of Genomic Variation on Function (IGVF) Consortium. IGVF is a collaborative research initiative funded by the NHGRI that aims to systematically study how genomic variations affect genome function and, consequently, phenotypes. By integrating experimental and computational approaches, IGVF seeks to map and predict the functional impacts of genetic variants, providing a comprehensive catalog of these effects. This tutorial provides a thorough introduction in MPRAs and IGVF data resources, practical training on MPRA data, and insights into advanced analysis methods for such data. Participants will gain an understanding of MPRA experiments, including their various experimental designs and the rationale for using them in functional genomics. This will involve learning the process of associating tags/barcodes with sequences incorporated in the reporter constructs from raw sequencing reads and counting barcodes from DNA sequencing and RNA expression. The tutorial will guide participants through data processing using MPRAsnakeflow, a streamlined snakemake workflow developed with IGVF for efficient MPRA data handling and QC reporting. Statistical analysis for sequence-level and variant-level effect testing of MPRA count data will be introduced using BCalm, a barcode-level MPRA analysis package developed as part of our IGVF efforts. Further, the tutorial will provide a starting point for training (deep learning) sequence models on MPRA data and related functional genomics datasets. Participants will learn how to extract meaningful insights from their datasets by investigating the sequence activity relationship and extracting important sequence motifs. By integrating these topics and methods, participants will leave the tutorial equipped with both theoretical knowledge and practical skills necessary for analyzing and using MPRA data effectively.

Tutorial IP3: Genomic Variant Interpretation & prioritisation for clinical research

The interpretation of genetic variation is important for understanding human health and disease. Increased knowledge leads to societal benefits including faster disease diagnosis, a better understanding of disease progression, more efficient identification and prioritisation of drug targets for testing, resulting in overall better health outcomes for a population. Whilst the speed and cost of sequencing has reduced, the complexity of variant interpretation remains a bottleneck for understanding. This tutorial will explore the variety of annotations and techniques available to assess human variation and the implications of variant effects on human health and disease.

Tutorial IP4: Quantum Machine Learning for multi-omics analysis

Single-cell and population-level multi-omics analyses have greatly enhanced our understanding of biological complexity. By integrating various types of biological data—such as genomics, proteomics, and transcriptomics, collectively known as multi-omics—these approaches have provided deep insights into the molecular mechanisms underlying complex diseases, both at the cellular level and across patient populations. As the size and complexity of multi-omics data continues to grow, the need to leverage emerging technologies such as artificial intelligence (AI) and quantum computing (QC) also grows. Recently, advances in QC have shown promise in solving real-world problems in machine learning and optimization in biomedicine, drug discovery, biomarker discovery, clinical trials, among other healthcare and life sciences objectives [1,2,3,4,5]. In this tutorial, participants will learn the fundamental concepts of QC, engage in hands-on experiments that apply classical machine learning (ML) techniques. They will also learn best practices for pre-processing multi-omics data in preparation for quantum machine learning (QML) tasks. Through a systematic evaluation of various data complexity measures and their impact on the performance of different ML and QML models, participants will gain insights into when to effectively utilize QML models. Additionally, they will explore quantum-classical hybrid workflows for ML, with a focus in biomedical data analysis.

Tutorial IP5: Introduction to Causal Analysis using Mendelian Randomisation

Mendelian randomisation (MR) is a method that uses genetic variation associated with an exposure (e.g., behaviours, biomarkers) to infer its causal effect on an outcome (e.g. health status). In statistical terms, it functions as an "instrumental variable" approach. By mimicking the design of a randomised controlled trial through genetic inheritance, MR provides a framework for addressing confounding and reverse causation, making it a valuable tool in epidemiological and biomedical research. This workshop offers a beginner-friendly introduction to the key concepts and assumptions underlying MR, such as the use of genome-wide association study (GWAS) data and the three key assumptions for valid instrumental variables: relevance, independence, and exclusion restriction. Participants will explore common challenges in MR analysis, including pleiotropy, population stratification, and measurement error while learning strategies to overcome these using advanced methods. The workshop also includes a two-hour hands-on session in which attendees will work with real-world data to conduct MR analyses using R. By the end of the session, participants will have a clear understanding of MR principles, the ability to critically evaluate MR studies, and practical skills to apply MR methods in their own research.

Tutorial IP6: Hello Nextflow: Getting started with workflows for bioinformatics

Nextflow is a powerful and flexible open-source workflow management system that simplifies the development, execution, and scalability of data-driven computational pipelines. It is widely used in bioinformatics and other scientific fields to automate complex analyses, making it easier to manage and reproduce large-scale data analysis workflows. This training workshop is intended as a “getting started” course for students and early-career researchers who are completely new to Nextflow. It aims to equip participants with foundational knowledge and skills in three key areas: (1) understanding the logic of how data analysis workflows are constructed, (2) Nextflow language proficiency and (3) command-line interface (CLI) execution. Participants will be guided through hands-on, goal-oriented exercises that will allow them to practice the following skills: Use core components of the Nextflow language to construct simple multi-step workflows effectively. Launch Nextflow workflows locally, navigate output directories to access results, interpret log outputs for insights into workflow execution, and troubleshoot basic issues that may arise during workflow execution. By the end of the workshop, participants will be well-prepared for tackling the next steps in their journey to develop and apply reproducible workflows for their scientific computing needs. Additional study-at-home materials will be provided for them to continue learning and developing their skills further.

Youth Bioinformatics Symposium

SCS: Student Council Symposium

Nutri-omics: how omics investigation can help designing personalized nutrition research

Federica Bergamo, Pedro Mena, Davide Martorana, Daniele Del Rio, Giovanni Malerba, Valeria Barili, Riccardo Bonadonna, Alessandra Dei Cas, Marco Ventura, Francesca Turroni, Letizia Bresciani, Mirko Treccani, Cristiano Negro, Alice Rosi, Cristina Del Burgo-Gutiérrez, Maria Sole Morandini, Nicola Luigi Bragazzi, Claudia Favari, José Fernando Rinaldi de Alvarenga, Lucia Ghiretti, Cristiana Mignogna

(Poly)phenols (PPs) are a group of bioactive compounds found in plant-based food, widely consumed within diet. Several studies have reported the beneficial effects of PPs in preventing chronic diseases through a myriad of mechanisms of action. However, the bioavailability and effects of these compounds greatly differ across individuals, causing uneven physiological responses. To understand their inter-individual variability, we present a multi-omics investigation comprising genomics, metagenomics and metabolomics. We recruited 300 healthy individuals and collected biological samples (blood, urine, and faeces), anthropometric measurements, health status and lifestyle/dietary information. After identification by UPLC-IMS-HRMS and quantification by UPLC-QqQ-MS/MS, the large set of phenolic metabolites underwent dimensionality reduction and clustering to identify individuals with similar metabolic profiles (metabotypes), identifying high and low PP producers. Then, genomics and metagenomics investigations were performed to gain insights on inter-individual differences and unravel the potential pathophysiological impact of these molecules, with particular regards to cardiometabolic diseases. In details, genome-wide association studies followed by computational functional analyses on genetic variants, and taxonomic and functional investigations of gut microbiome were performed, showing hints for associations in genes and microbial species related to PP metabolism, together with unprecedented genetic associations. Genomics were further investigated in terms of gene networks and computational functional analyses, identifying differentially expressed genes, gene sets enrichments, candidate regulatory regions, and interacting loci and chromatin states, and associations with metabolic traits and diseases. Overall, we demonstrated the benefits of omics research in nutrition, advancing the field of personalised nutrition and health.

SCS: Student Council Symposium

Nocardia Genomes are a Large Reservoir of Diverse Gene Content, Biosynthetic Gene Clusters, and Species-specific Genes

Nocardia, an opportunistic pathogenic bacterial genus, remains underexplored in terms of biosynthetic potential, gene content, and evolutionary history. By analyzing 263 genomes across 88 species, we found that Nocardia varies greatly in genome size and gene content. It exhibits an open pangenome, with a small core genome (< 900 genes), and high genomic fluidity (0.76), indicating high gene turnover. A large proportion (75%) of its genes are species-specific, indicating its high genomic plasticity and dynamic evolutionary adaptation. Average Nucleotide Identity (ANI) analysis confirmed taxonomic relationships among Nocardia species, with most exhibiting high between-species ANI values (80-85%). N. globerula showed a high ANI of ~84% with Rhodococcus erythropolis, strongly supporting its reclassification under Rhodococcus. The biosynthetic capabilities of the Nocardia genus are striking with the presence of >8,000 biosynthetic gene clusters (BGCs), dominated by type 1 polyketide synthase, terpenes, and non-ribosomal polypeptide synthetases. This establishes Nocardia as the Actinomycetota genus that has the largest biosynthetic repertoire. Our study is the first to identify a prodigiosin BGC in Nocardia. Network analysis revealed complex evolutionary connections between Nocardia’s gene cluster families (GCFs) and MIBiG reference BGCs, suggesting evolutionary changes, including gene gains and losses, that may have influenced the genus’s BGC diversity and composition. Synteny analysis uncovered conserved and unique gene arrangements across Nocardia and related genera, mostly with core genes conserved in Actinomycetota. The findings from our study contribute to advancing microbial genomics, evolution, and biotechnology by uncovering the potential of Nocardia to address challenges in infectious diseases and natural product discovery.

SCS: Student Council Symposium

Lifting the veil on Challenging Medically Relevant Genes

Victor Grentzinger, Leonor Palmeira, Keith Durkin, Maria Artesi, Vincent Bours

While the cost of DNA sequencing has never been cheaper, a number of genetic diseases remain difficult to diagnose. Nearly 400 medically relevant genes are still challenging to characterize due to the complex nature of their sequence. This complexity can arise from a variety of factors, such as the existence of pseudogene, large Short Tandem Repeat region or Variable Number Tandem Repeat region. As such, the access to reliable and cost-effective genetic tests is limited. To resolve this issue, we decided to focus on improving the characterization of the following genes by using long-read sequencing: PKD1/PKD2, responsible for Autosomal Dominant Polycystic Kidney Disease (ADPKD), and FLG, involved in Atopic Dermatitis. For PKD genes, we amplified their sequence by long-range PCR before sequencing the products by Oxford Nanopore Sequencing. We were able to retrieve all variants previously confirmed by Sanger sequencing on 34 samples with ADPKD. For FLG, while investigating the 23 publicly available PacBio HiFi data of the 1000 Genome project, we identified new undescribed alleles in African samples. To determine if these variations are population specific, we analyzed 1111 additional public samples with long-read data. We discovered 5 novel alleles mostly from Sub-Saharan populations. We also investigated, in our cohort of public data, the MUC1 and SMN1/SMN2 genes, responsible respectively for Autosomal Dominant Tubulointerstitial Kidney Disease and Spinal Muscular Atrophy. Our next goal is to design cost efficient techniques to improve the sequencing of these challenging medically relevant genes in a clinical setting.

SCS: Student Council Symposium

AccuRate: A Tool Supporting Genotype–Phenotype Analysis and Causal Mutation Discovery in Soybean

Soybean is one of the world’s most significant crops, serving as an indispensable source of high-quality plant protein and oil for both human and livestock consumption. Advances in soybean research support genomics-assisted breeding, guiding the development of more resilient, nutritious, and high-yielding varieties. Soybean also possesses an extensive collection of genomic and phenotypic data, including a large database of phenotypic traits. This enables the creation of new strategies for analysing genotype-phenotype associations. While association studies are important for identifying genomic loci linked to phenotypic traits, pinpointing causal mutations remains a challenge due to many factors. Building on these resources, this study presents new algorithms for analysing, visualizing, and automatically categorizing quantitative and categorical phenotypes. Given that most functional mutations are biallelic, and that quantitative traits often arise from the combined effects of multiple genes, phenotype binarization provides a practical basis for further analysis. Since many traits exist on a spectrum, various categorization methods are applied to transform them into binary form. This step is essential for calculating an accuracy parameter that quantifies genotype-phenotype correlation and facilitates the identification of causal mutations. The algorithm AccuRate was tested on well-characterized genes influencing protein and oil content in soybean. Results confirmed its ability to identify genotype-phenotype correlations. Additionally, two candidate genes were analysed, and a causal mutation was confirmed in one of them (Glyma.06G205800), linked to flowering and maturation time. AccuRate is a promising tool for uncovering genotype-phenotype relationships in soybean and, after optimizing for high-throughput testing, may be extended to other crops.

SCS: Student Council Symposium

Early colorectal cancer detection with deep learning on ultra-shallow whole genome sequencing of cell-free DNA

Early detection of cancer can mitigate adverse patient outcomes by reducing the time to intervention and treatment. Cell-free DNA (cfDNA) circulating the bloodstream contains signatures of cancer which can be obtained and sequenced through liquid biopsy. Given a large collection of sequencing reads, features can be extracted and used to develop predictive models for patient cancer classification. However, current techniques for early cancer detection rely on tens of millions of sequencing reads, which can increase the cost of diagnosis. In our work, using whole genome sequencing data obtained from the Sequence Read Archive (SRA), we adapted convolutional neural networks to predict colorectal cancer. We found that the number of reads used by the model can be scaled down from approximately 60 million reads to 1 million reads. Our model achieved a classification performance of 0.902 AUC. This result suggests that the blood sample size required for liquid biopsy could be significantly reduced, thereby reducing the cost of diagnosis. Furthermore, through an ablation study, we showed that the fragment end distribution by itself produced a classification performance of 0.904 AUC. Meanwhile, relying only on fragment length distribution and end motif distribution produced 0.771 and 0.790 AUC, respectively. This suggests that fragment end distribution is a much more predictive feature for classification. In future work, we intend to incorporate fragment end features into transformer-based models to improve classification performance.

SCS: Student Council Symposium

DNA-DistilBERT: A small language model for non-coding variant effect prediction from human DNA sequences

Genetic variants have been associated with changes in disease risk. Historically, research has focused on coding variants; however, emerging research shows that non-coding variants also have strong links to disease causality, via transcription and gene regulation. Next-generation sequencing has exponentially increased genomic data availability, necessitating scalable computational approaches for accurate variant effect prediction. Transformer-based LLMs, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved good results on coding variants, however, results on non-coding variants remain inconsistent. Moreover, the quadratic computational complexity of attention mechanisms with sequence length imposes substantial resource demands, restricting innovation in this area to a few institutions with high-end infrastructure. Arguably, BERT is the most successful of such architectures as it excels in context-aware modelling of genomic sequences due to its bidirectional nature. However, to substantially decrease computational costs, it is proposed to exploit DistilBERT, which uses knowledge distillation during pretraining to reduce the number of model parameters. While small language models (SLMs) such as DistilBERT are established in natural language processing, they remain underexplored in genomics. Experiments show that, when pretrained on human reference genome sequences, and fine-tuned for variant effect prediction, the SLM approach can match state-of-the-art LLMs such as DNABERT-2 in accuracy, while significantly reducing resource requirements. This innovative, energy-efficient approach not only makes variant effect prediction more scalable but also advances equitable research by enabling training on a single GPU, eliminating the need for high-performance computing.

SCS: Student Council Symposium

Generative AI for Childhood and Adult Cancer Research

Cancer is one of the most common causes of death worldwide, and its complexity makes it especially challenging to study. Despite ongoing progress in cancer research, a significant challenge is the scarcity of detailed data on disease subgroups and stages. To overcome this problem, Generative AI techniques and, specifically, the Variational Autoencoder (VAE), have been widely used to handle high-dimensional data. We propose a robust, explainable Synthetic Data Generation (SDG) pipeline based on the VAE using cancer transcriptomics data. Here, two main scenarios are presented, where we use our SDG pipeline to study different cancer types, addressing data scarcity limitations effectively. First, we present the case of Medulloblastoma, a rare, childhood brain tumor traditionally classified into four molecular subgroups, where we provide evidence supporting the existence of an additional subgroup with distinct molecular features. Additionally, we apply explainability techniques to the VAE, uncovering key relationships between gene expression and disease subgroups. Second, we tackle cancer's dynamic nature to link the most similar patients and leverage our SDG pipeline to direct the process of data generation along a trajectory between patients at different stages of the disease. Our pipeline generates stage-separable patients, revealing actionable molecular insights at intermediate reconstructed steps. These studies demonstrate the potential of synthetic data generation in highly specific contexts, shed light on the temporal aspects of cancer, and advance our understanding of the underlying biological mechanisms.

SCS: Student Council Symposium

AutoPeptideML 2: An open source library for democratizing machine learning for peptide bioactivity prediction

Raúl Fernández-Díaz, Thanh Lam Hoang, Vanessa Lopez, Denis Shields

Peptides are a rapidly growing drug modality with diverse bioactivities and accessible synthesis, particularly for canonical peptides composed of the 20 standard amino acids. However, enhancing their pharmacological properties often requires chemical modifications, increasing synthesis cost and complexity. Consequently, most existing data and predictive models focus on canonical peptides. To accelerate the development of peptide drugs, there is a need for models that generalize from canonical to non-canonical peptides. We present AutoPeptideML, an open-source, user-friendly machine learning platform designed to bridge this gap. It empowers experimental scientists to build custom predictive models without specialized computational knowledge, enabling active learning workflows that optimize experimental design and reduce sample requirements. AutoPeptideML introduces key innovations: (1) preprocessing pipelines for harmonizing diverse peptide formats (e.g., sequences, SMILES); (2) automated sampling of negative peptides with matched physicochemical properties; (3) robust test set selection with multiple similarity functions (via the Hestia-GOOD framework); (4) flexible model building with multiple representation and algorithm choices; (5) thorough model evaluation for unseen data at multiple similarity levels; and (6) FAIR-compliant, interpretable outputs to support reuse and sharing. A webserver with GUI enhances accessibility and interoperability. We validated AutoPeptideML on 18 peptide bioactivity datasets and found that automated negative sampling and rigorous evaluation reduce overestimation of model performance, promoting user trust. A follow-up investigation also highlighted the current limitations in extrapolating from canonical to non-canonical peptides using existing representation methods. AutoPeptideML is a powerful, platform for democratizing machine learning in peptide research, facilitating integration with experimental workflows across academia and industry.

Tutorial IP1: Machine Learning for Omics: Best practices and Real-Life Insights with TidyModels

Omics data analysis presents unique challenges due to its high dimensionality and complexity. Supervised machine learning (ML) offers powerful tools for gaining insights from these data but currently faces a crisis of reproducibility due to poor adherence to best practices when undertaking feature selection, model evaluation, and needs for further interpretability. This full-day tutorial introduces participants to the common pitfalls and best practices of applying ML to omics research. It exemplifies good practice through example using the Tidymodels framework for ML workflows in R, tailored to omics applications. The course will feature a mixture of lectures, quizzes, real-life coding tutorials and hands-on practicals with 1-1 support. Example applications will illustrate regression analysis with methylation clocks, gene prioritisation and classification with cancer biomarker discovery. Special attention will be paid to challenges in working with highly multivariate data and integrating various data types as well as providing tips to extract meaningful insights from complex data. Beginner-level R skills are required, and attendees will leave with practical skills to apply Tidymodels to their own datasets.

Tutorial IP2: Massively parallel reporter assays in functional regulatory genomics and as part of the IGVF data resource

This tutorial is designed to empower bioinformatics researchers with the knowledge and skills to effectively utilize Massively Parallel Reporter Assays (MPRAs) data in their work. MPRAs are gaining wider applications across the functional genomics community and are used as part of the Impact of Genomic Variation on Function (IGVF) Consortium. IGVF is a collaborative research initiative funded by the NHGRI that aims to systematically study how genomic variations affect genome function and, consequently, phenotypes. By integrating experimental and computational approaches, IGVF seeks to map and predict the functional impacts of genetic variants, providing a comprehensive catalog of these effects. This tutorial provides a thorough introduction in MPRAs and IGVF data resources, practical training on MPRA data, and insights into advanced analysis methods for such data. Participants will gain an understanding of MPRA experiments, including their various experimental designs and the rationale for using them in functional genomics. This will involve learning the process of associating tags/barcodes with sequences incorporated in the reporter constructs from raw sequencing reads and counting barcodes from DNA sequencing and RNA expression. The tutorial will guide participants through data processing using MPRAsnakeflow, a streamlined snakemake workflow developed with IGVF for efficient MPRA data handling and QC reporting. Statistical analysis for sequence-level and variant-level effect testing of MPRA count data will be introduced using BCalm, a barcode-level MPRA analysis package developed as part of our IGVF efforts. Further, the tutorial will provide a starting point for training (deep learning) sequence models on MPRA data and related functional genomics datasets. Participants will learn how to extract meaningful insights from their datasets by investigating the sequence activity relationship and extracting important sequence motifs. By integrating these topics and methods, participants will leave the tutorial equipped with both theoretical knowledge and practical skills necessary for analyzing and using MPRA data effectively.

Tutorial IP3: Genomic Variant Interpretation & prioritisation for clinical research

The interpretation of genetic variation is important for understanding human health and disease. Increased knowledge leads to societal benefits including faster disease diagnosis, a better understanding of disease progression, more efficient identification and prioritisation of drug targets for testing, resulting in overall better health outcomes for a population. Whilst the speed and cost of sequencing has reduced, the complexity of variant interpretation remains a bottleneck for understanding. This tutorial will explore the variety of annotations and techniques available to assess human variation and the implications of variant effects on human health and disease.

Tutorial IP4: Quantum Machine Learning for multi-omics analysis

Single-cell and population-level multi-omics analyses have greatly enhanced our understanding of biological complexity. By integrating various types of biological data—such as genomics, proteomics, and transcriptomics, collectively known as multi-omics—these approaches have provided deep insights into the molecular mechanisms underlying complex diseases, both at the cellular level and across patient populations. As the size and complexity of multi-omics data continues to grow, the need to leverage emerging technologies such as artificial intelligence (AI) and quantum computing (QC) also grows. Recently, advances in QC have shown promise in solving real-world problems in machine learning and optimization in biomedicine, drug discovery, biomarker discovery, clinical trials, among other healthcare and life sciences objectives [1,2,3,4,5]. In this tutorial, participants will learn the fundamental concepts of QC, engage in hands-on experiments that apply classical machine learning (ML) techniques. They will also learn best practices for pre-processing multi-omics data in preparation for quantum machine learning (QML) tasks. Through a systematic evaluation of various data complexity measures and their impact on the performance of different ML and QML models, participants will gain insights into when to effectively utilize QML models. Additionally, they will explore quantum-classical hybrid workflows for ML, with a focus in biomedical data analysis.

Tutorial IP5: Introduction to Causal Analysis using Mendelian Randomisation

Mendelian randomisation (MR) is a method that uses genetic variation associated with an exposure (e.g., behaviours, biomarkers) to infer its causal effect on an outcome (e.g. health status). In statistical terms, it functions as an "instrumental variable" approach. By mimicking the design of a randomised controlled trial through genetic inheritance, MR provides a framework for addressing confounding and reverse causation, making it a valuable tool in epidemiological and biomedical research. This workshop offers a beginner-friendly introduction to the key concepts and assumptions underlying MR, such as the use of genome-wide association study (GWAS) data and the three key assumptions for valid instrumental variables: relevance, independence, and exclusion restriction. Participants will explore common challenges in MR analysis, including pleiotropy, population stratification, and measurement error while learning strategies to overcome these using advanced methods. The workshop also includes a two-hour hands-on session in which attendees will work with real-world data to conduct MR analyses using R. By the end of the session, participants will have a clear understanding of MR principles, the ability to critically evaluate MR studies, and practical skills to apply MR methods in their own research.

Tutorial IP6: Hello Nextflow: Getting started with workflows for bioinformatics

Nextflow is a powerful and flexible open-source workflow management system that simplifies the development, execution, and scalability of data-driven computational pipelines. It is widely used in bioinformatics and other scientific fields to automate complex analyses, making it easier to manage and reproduce large-scale data analysis workflows. This training workshop is intended as a “getting started” course for students and early-career researchers who are completely new to Nextflow. It aims to equip participants with foundational knowledge and skills in three key areas: (1) understanding the logic of how data analysis workflows are constructed, (2) Nextflow language proficiency and (3) command-line interface (CLI) execution. Participants will be guided through hands-on, goal-oriented exercises that will allow them to practice the following skills: Use core components of the Nextflow language to construct simple multi-step workflows effectively. Launch Nextflow workflows locally, navigate output directories to access results, interpret log outputs for insights into workflow execution, and troubleshoot basic issues that may arise during workflow execution. By the end of the workshop, participants will be well-prepared for tackling the next steps in their journey to develop and apply reproducible workflows for their scientific computing needs. Additional study-at-home materials will be provided for them to continue learning and developing their skills further.

Youth Bioinformatics Symposium

SCS: Student Council Symposium

ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and MeSH co-occurrence networks from context-specific biomedical literature

Luca Musella, Alejandro Afonso Castro, Xin Lai, Max Widmann, Julio Vera

The accelerating growth of scientific literature overwhelms our capacity to manually distil complex phenomena like molecular networks linked to diseases. Moreover, biases in biomedical research and database annotation limit our interpretation of facts and generation of hypotheses. ENQUIRE (Expanding Networks by Querying Unexpectedly Inter-Related Entities) offers a time- and resource-efficient alternative to manual literature curation and database mining. ENQUIRE reconstructs and expands co-occurrence networks of genes and biomedical ontologies from user-selected input corpora and network-inferred PubMed queries. Its modest resource usage and the integration of text mining, automatic querying, and network-based statistics mitigating literature biases makes ENQUIRE unique in its broad-scope applications. We benchmarked and illustrated ENQUIRE‘s capabilities in several case scenarios and published the results earlier this year (Musella L. et al., 2025, PLoS Comput Biol). At ISMB/ECCB 2025, we showcase how ENQUIRE can support biomedical researchers using melanoma resistance to immunotherapy as an example case study. The frameworks enabled by ENQUIRE include gene set reconstruction, pathway enrichment analysis, and knowledge graph annotation, which can ease literature annotation, boosting hypothesis formulation, and facilitating the identification of molecular targets for subsequent experimentation.

SCS: Student Council Symposium

TCRBench: A Unified Benchmark for TCR–Antigen Binding Prediction and Clustering

Muhammed Hunaid Topiwala, Pengfei Zhang, Heewook Lee

T-cell receptor (TCR) recognition of antigenic peptides presented by major histocompatibility complex (MHC) molecules is central to adaptive immunity, driving pathogen-specific responses and informing therapeutic vaccine development. Computational tasks such as predicting TCR-antigen binding affinity (NetTCR, Montemurro et al., 2021; ImRex, Moris et al., 2021) and clustering TCR sequences by epitope specificity (GLIPH, Glanville et al., 2017; TCRdist, Dash et al., 2017) have emerged as key challenges to decoding immune specificity. While recent models leveraging convolutional neural networks, transformers (e.g., ATM-TCR, Xu et al., 2021), and multimodal embeddings (ERGO, Springer et al., 2020; TCRMatch, Chronister et al., 2021) have significantly advanced performance, fragmented datasets and inconsistent evaluation methods have limited direct model comparisons and generalization. We propose a unified benchmark dataset integrating rigorously curated TCR sequences from human, mouse, and macaque responses to major pathogens (Influenza A, CMV, EBV, SARS-CoV-2) sourced from comprehensive databases such as VDJdb (Shugay et al., 2018) and IEDB (Vita et al., 2019). The benchmark incorporates standardized evaluation splits, structural representations enabled by AlphaFold2 predictions (Jumper et al., 2021), and robust evaluation metrics to ensure fair, reproducible comparisons. By consolidating disparate data and evaluation practices, our benchmark provides clarity on current progress, facilitating future innovation in computational TCR-antigen interaction modeling.

SCS: Student Council Symposium

Fold first, ask later: structure-informed function prediction in Pseudomonas phages

Hannelore Longin, George Bouras, Susanna Grigson, Robert Edwards, Hanne Hendrix, Rob Lavigne, Vera van Noort

Phages, the viruses of bacteria, are the most abundant biological entities on earth. In general, phage genomes are densely coded and contain many open reading frames, yet up to 70% encode proteins of unknown function. Despite clinical, biotechnological and fundamental interests in unravelling these proteins’ functions, phage proteins are absent from recent large-scale structure-based efforts (such as AlphaFold database). Here, we investigate the efficacy of structure-based protein annotation for Pseudomonas-infecting phages, comparing different post-processing strategies to obtain function annotations from FoldSeek output. Briefly, we collected every protein annotated as ‘hypothetical/phage protein’ in NCBI and of at least 100 amino acids in length, of 887 Pseudomonas-infecting phages. These 38,025 proteins (31% of all proteins) were then clustered into 10,453 groups of homologs. Protein structures were predicted with ColabFold and structural similarity to the PDB and AlphaFold database was assessed with FoldSeek. Of all proteins, 59% displayed significant similarity to at least one structure in these databases. We benchmarked various strategies for extracting function from these FoldSeek hits, integrating different information resources, hit selection methods, and structure-based clustering of the hits. The resulting annotations were then compared with state-of-the-art sequence- and structure-based phage annotation tools Pharokka and Phold. On average, up to 42% of the phage proteins of unknown function could be annotated using structure-based methods, depending on the post-processing strategies applied. While caution is warranted when transferring protein annotations based on similarity, these methods can significantly speed up research into new antimicrobials and biotechnological applications inspired by nature’s finest bioengineers: phages.

SCS: Student Council Symposium

Exploring capabilities of protein language models for cryptic binding site prediction

Identifying protein-ligand binding sites is essential for understanding biological mechanisms and supporting drug discovery. However, accurate prediction remains challenging - particularly in the case of cryptic binding sites (CBSs), which require significant conformational changes to form upon ligand binding. Structure-based prediction methods typically rely on a specific conformation (apo vs. holo), making them less effective for identifying CBSs. A promising alternative is the use of sequence-based approaches, enabled by the emergence of protein language models (pLMs). In this work, we explored the capabilities of various pLMs for predicting CBSs. As a baseline, we created a simple model trained using transfer learning. We then experimented with several fine-tuning strategies to further improve performance. Specifically, we applied multitask learning - not only to predict whether a residue is part of a CBS, but also to estimate its flexibility. This additional task enhanced the model’s awareness of protein dynamics, which is critical for accurate CBS identification. Our primary data source is the recently published CryptoBench dataset, which contains annotations of cryptic sites, although additional data sources were also considered. The combination of novel fine-tuning strategies and various training data improved performance across all key metrics, including a gain of over 2% in AUC. To better understand model limitations, we also conducted an analysis of common prediction errors. Finally, we introduced a simple post-processing method designed to refine and smooth the model’s outputs.

SCS: Student Council Symposium

Coarse-grained and Multi-Scale Modeling of Lytic Polysaccharide Monooxygenases: Insights into Family-Specific Dynamics and Protein Frustration

Lytic polysaccharide monooxygenases (LPMOs) are copper-dependent redox enzymes that catalyze the oxidative cleavage of C1 and/or C4 bonds in recalcitrant polysaccharides, playing a vital role in biomass conversion. The CAZy database classifies LPMOs into eight families (AA9, AA10, AA11, AA13, AA14, AA15, AA16, and AA17). These families exhibit diversity in their structure as well as catalytic features. This study focuses on analyzing the structure, dynamics and energetic landscapes of LPMO families using FrustratormeteR, SignDy, and multiscale modeling approaches. FrustratormeteR quantifies configurational and mutational frustration, identifying energetically unfavorable interactions. AA9 exhibited high local frustration in the residue range of 100-230, while AA10 showed a more stable profile. SignDy was employed to explore slow collective motions, revealing significant conformational changes in AA9 linked to enzymatic adaptability, with the first six modes indicating notable flexibility. In contrast, AA10 displayed lower mobility in its first three modes, suggesting greater rigidity and substrate specificity. Protein models from AlphaFold2 were used for proteins with missing residues. These models were prepared and subjected to 100 ns all-atom molecular dynamics simulations using the OPLS-AA/L force field. The increase in RMSD in the course of the simulation shows the conformational changes. RMSF and energy analyses revealed flexible regions consistent with mode analysis, with average potential energies stabilizing at -6.25×105 kJ/mol. The radius of gyration (Rg) remained stable around 1.65-1.75 nm. Analysing the coarse-grained Gō model simulations, run using SMOG for 200 million steps will provide further insights into the folding and long-range dynamic behavior of these enzymes.

SCS: Student Council Symposium

Identification and structural modeling of the novel TTC33-associated core (TANC) complex involved in DNA damage response

Małgorzata Drabko, Rafał Tomecki, Małgorzata Siek, Aneta Jurkiewicz, Miłosz Ludwinek, Kamil Kobyłecki, Dominik Cysewski, Agata Malinowska, Magdalena Bakun, Łukasz S. Borowski, Roman J. Szczęsny, Rafał Płoski, Agnieszka Tudek

Of the ~20,200 human proteins, ~9% remain functionally uncharacterized, highlighting a gap in our understanding of cell physiology. Structural proteins without enzymatic activity are particularly difficult to study. Here, we applied a “function by proximity” approach to TTC33, a nuclear structural tetratricopeptide repeat (TPR) protein conserved in bony vertebrates. Using comparative label-free mass spectrometry, we identified the TTC33-associated network (TAN), which includes WDR61, CCDC97, UNG, PP2A-B55α, PHF5A, and the SF3B subcomplex of U2. At the core of TAN is a novel trimeric complex (TANC) formed by TTC33:WDR61:PHF5A, with this claim being supported by co-purification and size exclusion chromatography. Structural predictions performed by AlphaFold 3, and their experimental validation showed WDR61 and PHF5A bind opposite sides of TTC33’s TPR4, while TPR1-3 recruit other TAN factors. To expand the structural model we employed molecular dynamics to identify the most stable amino acid contact pairs between complex subunits. Although TTC33 forms a complex with WDR61 and PHF5A, both of which are involved in RNA metabolism, our RNA-seq assays revealed only a subtle impact on mRNA levels and splicing patterns. In contrast, TTC33 appears more involved in DNA repair through interaction with UNG1/2. TTC33 loss led to increased DNA double-strand breaks, a phenotype previously associated with UNG1/2 knock-down. We showed that TTC33 protein levels are regulated in vivo, and that changes to TTC33 abundance reduced cellular proliferation rate and resistance to hydrogen peroxide. Moreover, the depletion or loss of either TTC33 or CCDC97 induced redistribution of p53-S15P, a marker of DNA damage.

SCS: Student Council Symposium

Functional Interfaces at Ordered–Disordered Transitions: Conserved Linear Motifs and Flanking Regions in Modular Proteins

Carla Luciana Padilla Franzotti, Nicolas Palopoli, Gustavo Pierdominici-Sottile, Miguel Andrade

Multidomain proteins integrate ordered domains, structured tandem repeats (STRs), and intrinsically disordered regions (IDRs) to generate modular architectures optimized for dynamic and specific protein-protein interactions. In this study, we analyze the role of short linear motifs (SLiMs) located at the interface between ordered and disordered segments, focusing on their contribution to structural connectivity and interaction regulation. Two model systems are examined: (1) the large T antigen from simian virus 40 (LTSV40), in which the LxCxE motif—positioned at the junction between a folded domain and an IDR—mediates binding to the retinoblastoma protein (pRb), and (2) the regulatory complex between protein phosphatase 1 delta (PP1δ) and its MYPT1 subunit, where ankyrin repeats (ANKs) are connected to DOC-type docking motifs through an intervening IDR. In both cases, the regions flanking the SLiMs exhibit high sequence conservation and specific biophysical properties, consistent with a modulatory role. Molecular dynamics simulations demonstrate that these flanking regions promote extended conformations upon complex formation, facilitating physical occlusion of critical interaction interfaces (such as the E2F-binding pocket in pRb) without requiring large-scale allosteric rearrangements. In the PP1-MYPT1 complex, ANK repeats and IDRs exhibit cooperative behavior that contributes to the stabilization of the bound conformation and enhances interaction specificity. These findings support the existence of a conserved ordered–motif–disordered architectural module recurrently employed in both viral and cellular regulatory systems. This topological arrangement constitutes a potential target for therapeutic intervention in diseases involving aberrant protein-protein interactions mediated by SLiMs at ordered–disordered interfaces.

SCS: Student Council Symposium

Automating Linear Motif Predictions to Map Human Signaling Networks

Short linear motifs (SLiMs) are critical mediators of transient protein-protein interactions (PPIs), yet only 0.2% of human SLiMs are experimentally verified. Their short length (3–11 residues), rapid evolution, and frequent location in intrinsically disordered regions make them difficult to systematically uncover using conventional approaches. We present an automated computational framework for proteome-wide SLiM discovery that integrates structural, evolutionary, and machine learning attributes to overcome limitations in current resources (e.g., MEME Suite, ELM). Our method combines Gibbs sampling for de novo motif discovery with hidden Markov models (HMMs) that explicitly model insertions and deletions, enabling a more realistic representation of motif variation. To improve specificity, we incorporate four discriminative features: ProtT5-derived motif propensity scores, AlphaFold-based intrinsic disorder (pLDDT), solvent accessibility, and cross-species conservation from multiple sequence alignments. Together, these features enable robust motif characterization even in noisy biological contexts. Biological relevance is ensured by searching the interactors of the SLiM-binding domain protein through BioGRID PPIs and motif clustering via HMM similarity (HH-suite). Our framework validated MAPK1 (ERK2)-mediated phosphorylation motif in RUNX1, exhibiting high feature scores and validated via independent phosphoproteomic data. This site, previously biochemically characterized but not recognized as an SLiM, shows the power of our approach in identifying functional motifs missed by traditional tools. Our database allows biologists to browse through validated motifs alongside high-quality predictions. This work lays the foundation for systematic reconstruction of motif-mediated signaling networks and advances the discovery of novel regulatory mechanisms and therapeutic targets.

SCS: Student Council Symposium

Deep Phylogenetic Reconstruction Reveals Key Functional Drivers in the Evolution of B1/B2 Metallo-β-Lactamases

Samuel Davis, Pallav Joshi, Ulban Adhikary, Julian Zaugg, Phil Hugenholtz, Marc Morris, Gerhard Schenk, Mikael Boden

Metallo-β-lactamases (MBLs) comprise a diverse family of antibiotic-degrading enzymes. Despite their growing implication in drug-resistant pathogens, no broadly effective clinical inhibitors against MBLs currently exist. Notably, β-lactam-degrading MBLs appear to have emerged twice from within the broader, catalytically diverse MBL-fold protein superfamily, giving rise to two distinct monophyletic groups: B1/B2 and B3 MBLs. Comparative analyses have highlighted distinct structural hallmarks of these subgroups, particularly in metal-coordinating residues. However, the precise evolutionary events underlying their emergence remain unclear due to challenges presented by extensive sequence divergence. Understanding the molecular determinants driving the evolution of β-lactamase activity may inform design of broadly effective inhibitors. We sought to infer the evolutionary features driving the emergence of B1/B2 MBLs via phylogenetics and ancestral reconstruction. To overcome challenges associated with evolutionary analysis at this scale, we developed a phylogenetically aware sequence curation framework centred on iterative profile HMM refinement. This framework was applied over several iterations to construct a comprehensive phylogeny encompassing the B1/B2 MBLs and several other recently diverged clades. The resulting tree represents the most robust hypothesis to date regarding the emergence of B1/B2 MBLs and implies a parsimonious evolutionary history of key features, including variation in active site architecture and insertions and deletions of distinct structural elements. Ancestral proteins inferred at key internal nodes were experimentally characterised, revealing distinct activity profiles that reflect underlying evolutionary transitions. These findings give rise to testable hypotheses regarding the molecular basis and evolutionary drivers of functional diversification, as well as potential targets for MBL inhibitor design.

SCS: Student Council Symposium

Multilingual model improves zero-shot prediction of disease effects on proteins

Models for mutation effect prediction in coding sequences rely on sequence-, structure-, or homology-based features. Here, we introduce a novel method that combines a codon language model with a protein language model, providing a dual representation for evaluating effects of mutations on disease. By capturing contextual dependencies at both the genetic and protein level, our approach achieves a 3% increase in ROC-AUC classifying disease effects for 137,350 ClinVar missense variants across 13,791 genes, outperforming two single-sequence-based language models. Obviously the codon language model can uniquely differentiate synonymous from nonsense mutations at the genomic level. Our strategy uses information at complementary biological scales (akin to human multilingual models) to enable protein fitness landscape modeling and evolutionary studies, with potential applications in precision medicine, protein engineering, and genomics.

SCS: Student Council Symposium

Integrated analysis of bulk and single-nuclei RNA sequencing data of primary and metastatic pediatric Medulloblastoma.

Ana Isabel Castillo Orozco, Geoffroy Danieau, Livia Garzia

Medulloblastoma (MB) is a highly aggressive and the most common brain tumor in childhood. MB presents a high intertumoral heterogeneity, with at least four molecular subgroups identified (SHH, WNT, Group 3, and Group 4). Metastatic MB, or Leptomeningeal Disease (LMD), is predominantly found in the MB Group 3 type. Although LMD represents a main clinical challenge, its molecular mechanisms remain poorly characterized. Recent research has shown that primary and MB metastasis diverge dramatically. Our work has focused on establishing therapy naïve Group 3 Patient-Derived Xenografts models of primary and metastatic Medulloblastoma to conduct transcriptomic profiling at the bulk and single-nuclei RNAseq levels to identify genetic drivers/pathways that sustain leptomeningeal disease compartment. Our results show various signaling pathways enriched across LMD models, such as MYC targets, unfolded protein response, and fatty acid metabolism. Using single-sample GSEA analysis (ssGSEA) and deconvolution approaches, we have also identified that our PDXes models retain neoplastic subpopulations previously identified in MB single-cell sequencing studies. Similarly, we have identified slight differences in cell subpopulation proportions between primary and leptomeningeal compartments. Our single-nuclei studies have confirmed these results and differentially expressed genes previously found in bulk RNAseq analyses. These results suggest the presence of cell populations enriched in the metastatic compartment with an aberrant transcription phenotype and adaptations in metabolism to survive the leptomeningeal space. Our recent findings suggest that LMD should be treated differently from primary brain tumors and that identified metabolic pathways may be potential targets for targeted therapeutics to treat or prevent this devastating disease.

SCS: Student Council Symposium

Investigating novel transcriptional regulators in symbiotic nodule development of Medicago truncatul

Biological nitrogen fixation is a crucial process for sustainable agriculture, allowing leguminous plants to convert atmospheric nitrogen into bioavailable forms through a symbiotic relationship with rhizobia. This interaction results in the formation of specialized root structures called nodules, where nitrogen fixation takes place. A deeper understanding of the molecular mechanisms governing nodule formation is essential for enhancing plant-microbe interactions and improving agricultural productivity. In this study, we investigate key transcription factors (TFs) involved in the nodulation process of Medicago truncatula, including MtIPD3, MtNSP1, MtNSP2, MtNIN, and MtERNs. Using co-expression analysis (Phytozome database) and interaction network studies (STRING database), we identify novel regulatory elements that potentially play a role in nodule organogenesis. Our findings suggest a strong interaction between IPD3 and splicing factors, implicating its involvement in RNA processing and cell cycle regulation during nodule formation. Additionally, we identify the cytokinin transporter gene ABCG38 as significantly upregulated in nodules, suggesting its role in cytokinin-mediated regulation. Moreover, our analysis indicates that the auxin response factor Medtr2g043250 is a likely transcriptional target of NIN, highlighting a possible cross-talk between auxin and cytokinin signaling in nodulation. These insights contribute to a deeper understanding of the transcriptional and hormonal regulation of nodule development, offering potential strategies for enhancing biological nitrogen fixation in legumes.

SCS: Student Council Symposium

Meta-Analysis of Bovine Transcriptome Reveals Key Immune Gene Profiles and Signaling Pathways

Vennila Kanchana Devi Marimuthu, Kishore Matheswaran, Menaka Thambiraja, Ragothaman M Yennamalli

Understanding immune mechanisms in cattle is crucial for improving disease resistance through informed breeding decision and development. Meta-analysis serves as a powerful approach to integrate findings from multiple transcriptomic studies that uncover significant gene expression patters across various experimental conditions and increase statistical power and. In this study, we conducted a meta-analysis of four bovine transcriptomic datasets (GSE45439, GSE62048, GSE125964, and GSE247921) to identify immune-related differentially expressed genes (DEGs) in Bos taurus. These datasets encompassed a range of immune-challenging conditions, including infections caused by Mycobacterium bovis and Mycobacterium avium subsp. paratuberculosis, comparing transcriptomic profiles between diseased and healthy cattle. We implemented a comprehensive transcriptome analysis pipeline involving FastQC, Trimmomatic, Bowtie2, SAMtools, FeatureCounts, DESeq2, and MetaRNASeq, which resulted in the identification of 28 significant DEGs, comprising 12 upregulated and 16 downregulated genes. Comparison with an innate immune gene database revealed five immune-related genes such as IL1A, RGS2, RCAN1, and ZBP1, known to play important regulatory roles in immune responses. KEGG pathway enrichment analysis showed that these genes were involved in four critical immune-related pathways: Necroptosis, Osteoclast Differentiation, Oxytocin Signaling, and cGMP–PKG Signaling. These pathways are associated with various immune functions, including inflammatory cell death, cytokine signaling, immune cell differentiation, and leukocyte trafficking. Overall, this meta-analysis provides a deeper understanding of conserved immune signaling mechanisms in cattle and highlights key genes that could serve as biomarkers for immune competence, disease susceptibility, or vaccine responsiveness. The findings offer valuable insights for future functional studies and applications in bovine immunogenomics.

SCS: Student Council Symposium

Post-translational regulation of stemness under DNA damage response contributes to the gingivobuccal oral squamous cell carcinoma relapse and progression

Tobacco consumption (smoking and particularly smokeless form) contributes to a high prevalence of gingivobuccal oral squamous cell carcinoma (OSCC-GB) in India. OSCC-GB patients exhibit high rates of locoregional relapse and therapeutic failure, often attributed to the involvement of cancer stem cells (CSCs). This study aims to leverage the generalizability of the machine learning prediction model for ‘Tumor Status’ to conduct a comparative somatic mutation analysis between ‘With Tumor’ (recurred/relapsed/progressed) and ‘Tumor Free’ (disease-free/complete remission) OSCC-GB patients. Our results revealed that support vector machines (SVM) classified the ‘Tumor Status’ classes with a mean accuracy of 89% based on clinical features. Furthermore, RNA-seq-based somatic mutation analysis using the classified groups revealed molecular mechanisms underlying tumor relapse and progression within OSCC-GB subgroups. The identified mutational signature (C>T mutations) linked to DNA damage suggests the role of tobacco-related carcinogens in OSCC-GB subgroups. The analysis of distinct somatic variants, functional impact predictions, protein-protein interactions, and survival analysis highlights the involvement of DNA damage response (DDR)-related genes in the ‘With Tumor’ subgroup. This analysis particularly emphasizes the significant role of the Mitogen-activated protein kinase associated protein 1 (MAPKAP1) gene, a key player in the mTORC2 signaling pathway. The study suggests that loss-of-function in the identified MAPKAP1 somatic variant may promote stemness and elevate the risk of disease relapse and progression in ‘With Tumor’ OSCC-GB under DDR conditions, potentially contributing to higher mortality rates among Indian OSCC-GB patients.

SCS: Student Council Symposium

Tutorial IP7: AI large cellular models and in-silico perturbation

Transformer-based large language models (LLMs) are changing the world. The capabilities they illustrated in sophisticated natural language, vision and multi-modal tasks have inspired the development of large cellular models (LCMs) for single-cell transcriptomic data, such as scBERT, Geneformer, scGPT, scFoundation, GeneCompass, scMulan, etc. After pretraining on massive amount of single-cell RNA-seq data agnostic to any downstream task, these transformer-based models have demonstrated exceptional performance in various tasks such as cell type annotation, data integration, gene network inference, and the prediction of drug sensitivity or perturbation responses. Such advancements, albeit still in their early stage, suggested promising revolutionary approaches for leveraging AI to understand the complex system of cells from extensive datasets beyond human analytical capacity. Especially, such models have made it possible to conduct in-silico perturbation on cells of various types to predict their responses to gene perturbations without doing experiments on the cells. These models provided prototypes of digital virtual cells that can be used to reconstruct and simulate live cells, which will revolutionize many aspects of future biomedical studies. Although the community is high enthusiastic to these exciting progresses, the structures and algorithms of LCMs and other similar-scale AI models are mysterious to many people who were not equipped with relevant backgrounds. This tutorial will try to fill this gap. In the tutorial, we will begin from an introduction of basic principles of deep neural networks, and explain the basic structure and algorithm of the original Transformer for natural language tasks. We’ll show to the attendees how to build such models based on current machine learning platforms. Then we’ll introduce several successful ways to build large cellular models based on the basic Transformer model, and overview how such models are pretrained on single-cell RNA-seq data. We’ll show and let the attendees to practice how to use LCMs for basic tasks such as cell type annotation, and look into the specific application of LCMs on in-silico perturbation tasks. Attendees will engage in hands-on activities such as building basic transformer models and executing downstream single-cell tasks, including cell type annotation and in-silico perturbation. These activities will remove the mystery of LCMs for the attendees and help them better understand and feel how LCMs can be built and applied

Tutorial IP8: Representation Learning and Feature Engineering for Genomic Sequences Analysis

Machine learning (ML) has been successfully applied in different omics problems, such as sequence classification in the field of genomics. The effectiveness of ML methods relies greatly on the selection of the data representation, or features, that extract meaningful information from sequences. Genomic sequences can be viewed as one-dimensional strings of successive letters representing nucleotides. However, to make these sequences compatible with ML methods, they must first be transformed into structured numerical representations, such as vectors or matrices. Traditional methods for sequence classification often rely on manually crafted or pre-defined features, which require domain expertise and may not fully capture the complexity of the underlying biological information. Recently, representation learning has emerged as a powerful alternative, enabling the automatic extraction of latent patterns directly from raw data and reducing the dependence on manually crafted features. In genomics, representation learning methods have been introduced to characterize DNA and RNA sequences. In genomics, techniques like Word2Vec, Convolutional Neural Networks (CNNs) and Large Language Models (LLMs) have demonstrated the ability to learn optimal sequence representations that effectively capture both local and global patterns in DNA and RNA sequences. This tutorial provides a comprehensive introduction to feature engineering and representation learning for genomic sequences (DNA/RNA). Participants will explore traditional techniques for extracting features from genomic sequences, building a foundation in classical approaches. Furthermore, the tutorial will cover representation learning, introducing concepts such as embeddings and their applications. Topics include methods such as Word2vec and LLMs to obtain meaningful representations from genomic sequences. Through hands-on exercises and comparative analyses, attendees will learn to combine traditional feature engineering with representation learning approaches, developing practical skills and insights that are adaptable to diverse genomic research challenges. The goal is to offer participants the knowledge and tools to enhance genomic sequence analysis using different techniques for sequence representation.

Tutorial IP1: Machine Learning for Omics: Best practices and Real-Life Insights with TidyModels

Omics data analysis presents unique challenges due to its high dimensionality and complexity. Supervised machine learning (ML) offers powerful tools for gaining insights from these data but currently faces a crisis of reproducibility due to poor adherence to best practices when undertaking feature selection, model evaluation, and needs for further interpretability. This full-day tutorial introduces participants to the common pitfalls and best practices of applying ML to omics research. It exemplifies good practice through example using the Tidymodels framework for ML workflows in R, tailored to omics applications. The course will feature a mixture of lectures, quizzes, real-life coding tutorials and hands-on practicals with 1-1 support. Example applications will illustrate regression analysis with methylation clocks, gene prioritisation and classification with cancer biomarker discovery. Special attention will be paid to challenges in working with highly multivariate data and integrating various data types as well as providing tips to extract meaningful insights from complex data. Beginner-level R skills are required, and attendees will leave with practical skills to apply Tidymodels to their own datasets.

Tutorial IP2: Massively parallel reporter assays in functional regulatory genomics and as part of the IGVF data resource

This tutorial is designed to empower bioinformatics researchers with the knowledge and skills to effectively utilize Massively Parallel Reporter Assays (MPRAs) data in their work. MPRAs are gaining wider applications across the functional genomics community and are used as part of the Impact of Genomic Variation on Function (IGVF) Consortium. IGVF is a collaborative research initiative funded by the NHGRI that aims to systematically study how genomic variations affect genome function and, consequently, phenotypes. By integrating experimental and computational approaches, IGVF seeks to map and predict the functional impacts of genetic variants, providing a comprehensive catalog of these effects. This tutorial provides a thorough introduction in MPRAs and IGVF data resources, practical training on MPRA data, and insights into advanced analysis methods for such data. Participants will gain an understanding of MPRA experiments, including their various experimental designs and the rationale for using them in functional genomics. This will involve learning the process of associating tags/barcodes with sequences incorporated in the reporter constructs from raw sequencing reads and counting barcodes from DNA sequencing and RNA expression. The tutorial will guide participants through data processing using MPRAsnakeflow, a streamlined snakemake workflow developed with IGVF for efficient MPRA data handling and QC reporting. Statistical analysis for sequence-level and variant-level effect testing of MPRA count data will be introduced using BCalm, a barcode-level MPRA analysis package developed as part of our IGVF efforts. Further, the tutorial will provide a starting point for training (deep learning) sequence models on MPRA data and related functional genomics datasets. Participants will learn how to extract meaningful insights from their datasets by investigating the sequence activity relationship and extracting important sequence motifs. By integrating these topics and methods, participants will leave the tutorial equipped with both theoretical knowledge and practical skills necessary for analyzing and using MPRA data effectively.

Tutorial IP3: Genomic Variant Interpretation & prioritisation for clinical research

The interpretation of genetic variation is important for understanding human health and disease. Increased knowledge leads to societal benefits including faster disease diagnosis, a better understanding of disease progression, more efficient identification and prioritisation of drug targets for testing, resulting in overall better health outcomes for a population. Whilst the speed and cost of sequencing has reduced, the complexity of variant interpretation remains a bottleneck for understanding. This tutorial will explore the variety of annotations and techniques available to assess human variation and the implications of variant effects on human health and disease.

Tutorial IP4: Quantum Machine Learning for multi-omics analysis

Single-cell and population-level multi-omics analyses have greatly enhanced our understanding of biological complexity. By integrating various types of biological data—such as genomics, proteomics, and transcriptomics, collectively known as multi-omics—these approaches have provided deep insights into the molecular mechanisms underlying complex diseases, both at the cellular level and across patient populations. As the size and complexity of multi-omics data continues to grow, the need to leverage emerging technologies such as artificial intelligence (AI) and quantum computing (QC) also grows. Recently, advances in QC have shown promise in solving real-world problems in machine learning and optimization in biomedicine, drug discovery, biomarker discovery, clinical trials, among other healthcare and life sciences objectives [1,2,3,4,5]. In this tutorial, participants will learn the fundamental concepts of QC, engage in hands-on experiments that apply classical machine learning (ML) techniques. They will also learn best practices for pre-processing multi-omics data in preparation for quantum machine learning (QML) tasks. Through a systematic evaluation of various data complexity measures and their impact on the performance of different ML and QML models, participants will gain insights into when to effectively utilize QML models. Additionally, they will explore quantum-classical hybrid workflows for ML, with a focus in biomedical data analysis.

Youth Bioinformatics Symposium

SCS: Student Council Symposium

Rethinking Rigor: Benchmarking in the Age of Foundation Models

SCS: Student Council Symposium

Gaps in the capabilities of generative AI systems; ISCB's role in promoting international research and educational collaborations

Tutorial IP7: AI large cellular models and in-silico perturbation

Transformer-based large language models (LLMs) are changing the world. The capabilities they illustrated in sophisticated natural language, vision and multi-modal tasks have inspired the development of large cellular models (LCMs) for single-cell transcriptomic data, such as scBERT, Geneformer, scGPT, scFoundation, GeneCompass, scMulan, etc. After pretraining on massive amount of single-cell RNA-seq data agnostic to any downstream task, these transformer-based models have demonstrated exceptional performance in various tasks such as cell type annotation, data integration, gene network inference, and the prediction of drug sensitivity or perturbation responses. Such advancements, albeit still in their early stage, suggested promising revolutionary approaches for leveraging AI to understand the complex system of cells from extensive datasets beyond human analytical capacity. Especially, such models have made it possible to conduct in-silico perturbation on cells of various types to predict their responses to gene perturbations without doing experiments on the cells. These models provided prototypes of digital virtual cells that can be used to reconstruct and simulate live cells, which will revolutionize many aspects of future biomedical studies. Although the community is high enthusiastic to these exciting progresses, the structures and algorithms of LCMs and other similar-scale AI models are mysterious to many people who were not equipped with relevant backgrounds. This tutorial will try to fill this gap. In the tutorial, we will begin from an introduction of basic principles of deep neural networks, and explain the basic structure and algorithm of the original Transformer for natural language tasks. We’ll show to the attendees how to build such models based on current machine learning platforms. Then we’ll introduce several successful ways to build large cellular models based on the basic Transformer model, and overview how such models are pretrained on single-cell RNA-seq data. We’ll show and let the attendees to practice how to use LCMs for basic tasks such as cell type annotation, and look into the specific application of LCMs on in-silico perturbation tasks. Attendees will engage in hands-on activities such as building basic transformer models and executing downstream single-cell tasks, including cell type annotation and in-silico perturbation. These activities will remove the mystery of LCMs for the attendees and help them better understand and feel how LCMs can be built and applied

Tutorial IP8: Representation Learning and Feature Engineering for Genomic Sequences Analysis

Machine learning (ML) has been successfully applied in different omics problems, such as sequence classification in the field of genomics. The effectiveness of ML methods relies greatly on the selection of the data representation, or features, that extract meaningful information from sequences. Genomic sequences can be viewed as one-dimensional strings of successive letters representing nucleotides. However, to make these sequences compatible with ML methods, they must first be transformed into structured numerical representations, such as vectors or matrices. Traditional methods for sequence classification often rely on manually crafted or pre-defined features, which require domain expertise and may not fully capture the complexity of the underlying biological information. Recently, representation learning has emerged as a powerful alternative, enabling the automatic extraction of latent patterns directly from raw data and reducing the dependence on manually crafted features. In genomics, representation learning methods have been introduced to characterize DNA and RNA sequences. In genomics, techniques like Word2Vec, Convolutional Neural Networks (CNNs) and Large Language Models (LLMs) have demonstrated the ability to learn optimal sequence representations that effectively capture both local and global patterns in DNA and RNA sequences. This tutorial provides a comprehensive introduction to feature engineering and representation learning for genomic sequences (DNA/RNA). Participants will explore traditional techniques for extracting features from genomic sequences, building a foundation in classical approaches. Furthermore, the tutorial will cover representation learning, introducing concepts such as embeddings and their applications. Topics include methods such as Word2vec and LLMs to obtain meaningful representations from genomic sequences. Through hands-on exercises and comparative analyses, attendees will learn to combine traditional feature engineering with representation learning approaches, developing practical skills and insights that are adaptable to diverse genomic research challenges. The goal is to offer participants the knowledge and tools to enhance genomic sequence analysis using different techniques for sequence representation.

Tutorial IP1: Machine Learning for Omics: Best practices and Real-Life Insights with TidyModels

Omics data analysis presents unique challenges due to its high dimensionality and complexity. Supervised machine learning (ML) offers powerful tools for gaining insights from these data but currently faces a crisis of reproducibility due to poor adherence to best practices when undertaking feature selection, model evaluation, and needs for further interpretability. This full-day tutorial introduces participants to the common pitfalls and best practices of applying ML to omics research. It exemplifies good practice through example using the Tidymodels framework for ML workflows in R, tailored to omics applications. The course will feature a mixture of lectures, quizzes, real-life coding tutorials and hands-on practicals with 1-1 support. Example applications will illustrate regression analysis with methylation clocks, gene prioritisation and classification with cancer biomarker discovery. Special attention will be paid to challenges in working with highly multivariate data and integrating various data types as well as providing tips to extract meaningful insights from complex data. Beginner-level R skills are required, and attendees will leave with practical skills to apply Tidymodels to their own datasets.

Tutorial IP2: Massively parallel reporter assays in functional regulatory genomics and as part of the IGVF data resource

This tutorial is designed to empower bioinformatics researchers with the knowledge and skills to effectively utilize Massively Parallel Reporter Assays (MPRAs) data in their work. MPRAs are gaining wider applications across the functional genomics community and are used as part of the Impact of Genomic Variation on Function (IGVF) Consortium. IGVF is a collaborative research initiative funded by the NHGRI that aims to systematically study how genomic variations affect genome function and, consequently, phenotypes. By integrating experimental and computational approaches, IGVF seeks to map and predict the functional impacts of genetic variants, providing a comprehensive catalog of these effects. This tutorial provides a thorough introduction in MPRAs and IGVF data resources, practical training on MPRA data, and insights into advanced analysis methods for such data. Participants will gain an understanding of MPRA experiments, including their various experimental designs and the rationale for using them in functional genomics. This will involve learning the process of associating tags/barcodes with sequences incorporated in the reporter constructs from raw sequencing reads and counting barcodes from DNA sequencing and RNA expression. The tutorial will guide participants through data processing using MPRAsnakeflow, a streamlined snakemake workflow developed with IGVF for efficient MPRA data handling and QC reporting. Statistical analysis for sequence-level and variant-level effect testing of MPRA count data will be introduced using BCalm, a barcode-level MPRA analysis package developed as part of our IGVF efforts. Further, the tutorial will provide a starting point for training (deep learning) sequence models on MPRA data and related functional genomics datasets. Participants will learn how to extract meaningful insights from their datasets by investigating the sequence activity relationship and extracting important sequence motifs. By integrating these topics and methods, participants will leave the tutorial equipped with both theoretical knowledge and practical skills necessary for analyzing and using MPRA data effectively.

Tutorial IP3: Genomic Variant Interpretation & prioritisation for clinical research

The interpretation of genetic variation is important for understanding human health and disease. Increased knowledge leads to societal benefits including faster disease diagnosis, a better understanding of disease progression, more efficient identification and prioritisation of drug targets for testing, resulting in overall better health outcomes for a population. Whilst the speed and cost of sequencing has reduced, the complexity of variant interpretation remains a bottleneck for understanding. This tutorial will explore the variety of annotations and techniques available to assess human variation and the implications of variant effects on human health and disease.

Tutorial IP4: Quantum Machine Learning for multi-omics analysis

Single-cell and population-level multi-omics analyses have greatly enhanced our understanding of biological complexity. By integrating various types of biological data—such as genomics, proteomics, and transcriptomics, collectively known as multi-omics—these approaches have provided deep insights into the molecular mechanisms underlying complex diseases, both at the cellular level and across patient populations. As the size and complexity of multi-omics data continues to grow, the need to leverage emerging technologies such as artificial intelligence (AI) and quantum computing (QC) also grows. Recently, advances in QC have shown promise in solving real-world problems in machine learning and optimization in biomedicine, drug discovery, biomarker discovery, clinical trials, among other healthcare and life sciences objectives [1,2,3,4,5]. In this tutorial, participants will learn the fundamental concepts of QC, engage in hands-on experiments that apply classical machine learning (ML) techniques. They will also learn best practices for pre-processing multi-omics data in preparation for quantum machine learning (QML) tasks. Through a systematic evaluation of various data complexity measures and their impact on the performance of different ML and QML models, participants will gain insights into when to effectively utilize QML models. Additionally, they will explore quantum-classical hybrid workflows for ML, with a focus in biomedical data analysis.

Youth Bioinformatics Symposium

Extending AlphaFold to make predictions across the universe of biomolecular interactions

The high accuracy of AlphaFold 2 in predicting protein structures and protein-protein interactions raises the question of how to extend the success of AlphaFold to general biomolecular modeling, including protein-nucleic and protein-small molecule structure predictions as well as the effects of post-translational modification. In this talk, I will discuss our latest work on AlphaFold 3 to develop a single deep learning system that makes accurate predictions across these interaction types, as well as examine some of the remaining challenges in predicting the universe of biologically-relevant protein interactions.

Amos Bairoch will reflect on 40 years of biocuration, from Swiss-Prot to Cellosaurus, highlighting how core challenges and values have endured despite the many developments in computational biology over that time.

3DSIG: Structural Bioinformatics and Computational Biophysics

Decoding Immunity: Structural and Dynamical Insights Driving Antibody Innovation

Effective adaptive immune responses rely on antibodies of different isotypes performing distinct effector functions. Understanding their structural diversity is crucial for engineering antibodies with optimal stability, binding, and therapeutic potential. In this keynote, I will present our integrative computational approaches to guide antibody design, which include isotype classification, chain compatibility prediction, 3D structural modeling, and analysis of allosteric communication. In designing novel antibodies, effective pairing of antibody heavy and light chains is essential for effective function, yet the rules governing this remain unclear. I will introduce ImmunoMatch, a suite of AI models fine-tuned on full-length variable regions to predict cognate H–L chain pairs. Built on the AntiBERTa2 language model, ImmunoMatch outperforms CDR- and gene usage–based models, with further improvements from chain type–specific tuning. Applied to B cell repertoires and therapeutic antibodies, ImmunoMatch identifies chain pairing refinement as a hallmark of B cell maturation and uncovers key sequence features driving specificity. Moving beyond the traditional focus on CDRs, we show that framework (FW) mutations can modulate antibody stability and effector function through long-range structural effects. Our analyses revealed that antibody language models (AbLMs) alone lack predictive power for FW mutagenesis. To improve on this, we adopted a structure-based approach, suggesting future directions such as fine-tuning AbLMs with in vitro FW-specific mutational data to improve their utility in antibody design. This shift can broaden the scope of rational engineering toward non-CDR regions and developability attributes, highlighting the need for a holistic view of antibody design.

Molecular Digitisation and Biodiversity Bioinformatics

Biological collections (such as herbarium and fungarium specimens) are the prescurors of modern biobanks; the defining types of taxonomic concepts; together with their associated metadata, a record of what lifeforms were found where and when; there; and increasingly, a physical reference from which molecular data can be extracted. Digitisation of specimen images and metadata, and molecular characterisation through DNA sequencing, are making historic collections newly relevant to contemporary scientific questions. Although DNA degrades with age, it is still possible to obtain significant information about the phylogenetic placement and gene content of many specimens. In this talk, Dr. Kersey will present three large-scale sequencing projects that utilise the collections of the Royal Botanic Gardens, Kew: the Plant and Fungal Trees of Life project, the Darwin Tree of Life project, and the Fungarium Sequencing project. He will discuss the data they are generating, the challenges these raise and the opportunities these present, and the changing role of collections in the scientific community as biodiversity science becomes a big data field.

BOSC: Bioinformatics Open Source Conference

EvolCompGen: Evolution & Comparative Genomics

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

Xuan Li, Yuelin Liu, Alejandro Schäffer, Stephen Mount, Cenk Sahinalp

In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step towards tailoring healthcare interventions. However, the critical aspect of fairly selecting molecular features across multiple patients is often overlooked. To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization. FALAFL is designed to perform feature selection in sequencing data which ensures a balanced selection of features from all patient samples in a cohort. We have applied FALAFL to the problem of selecting lineage-informative CpG sites within a cohort of colorectal cancer patients subjected to low read coverage single-cell methylation sequencing. Our results demonstrate that FALAFL can rapidly and robustly determine the optimal set of CpG sites, which are each well covered by cells across the vast majority of the patients, while ensuring that in each patient a large proportion of these sites have high read coverage. An analysis of the FALAFL-selected sites reveals that their tumor lineage-informativeness exhibits a strong correlation across a spectrum of diverse patient profiles. Furthermore, these universally lineage-informative sites are highly enriched in the inter-CpG island regions. FALAFL brings unsupervised fairness considerations into the molecular feature selection from single-cell sequencing data obtained from a patient cohort. We hope that it will aid in designing panels for diagnostic and prognostic purposes and help propel fair data science practices in the exploration of complex diseases.

GenCompBio: General Computational Biology

Harnessing Deep Learning for Proteome-Scale Detection of Amyloid Signaling Motifs

Amyloid signaling sequences adopt the cross-β fold that is capable of self-replication in the templating process. Propagation of the amxyloid fold from the receptor to the effector protein is used for signal transduction in the immune response pathways in animals, fungi and bacteria. So far, a dozen of families of amyloid signaling motifs (ASMs) have been classified. Unfortunately, due to the wide variety of ASMs it is difficult to identify them in large protein databases available, which limits the possibility of conducting experimental studies. To date, various deep learning (DL) models have been applied across a range of protein-related tasks, including domain family classification and the prediction of protein structure and protein-protein interactions. In this study, we develop tailor-made bidirectional LSTM and BERT-based architectures to model ASM, and compare their performance against a state-of-the-art machine learning grammatical model. Our research is focused on developing a discriminative model of generalized amyloid signaling motifs, capable of detecting ASMs in large data sets. The DL-based models are trained on a diverse set of motif families and a global negative set, and used to identify ASMs from remotely related families. We analyze how both models represent the data and demonstrate that the DL-based approaches effectively detect ASMs, including novel motifs, even at the genome scale.

MLCSB: Machine Learning in Computational and Systems Biology

The identification of genetic variants strongly affecting when phenotypes remains an unsolved problem with major relevance in rare diseases diagnostics, oncology, and for the identification of effector genes of complex traits and diseases. I will present a series of published and ongoing work from my lab tackling this issue, with a focus on non-coding variants. This will span variant scoring based on genomic language models [1], methods to predict aberrant expression [2] and splicing [3], all the way to integrative deep learning models for rare variant association analyses demonstrated on UK Biobank [4]. 1. Tomaz da Silva, et al. Nucleotide dependency analysis of DNA language models reveals genomic functional elements. bioRxiv, 2024 2. Hölzlwimmer et al. Aberrant gene expression prediction across human tissues. Nature Communications, 2025 3. Wagner et al. Aberrant splicing prediction across human tissues. Nature Genetics, 2023 4. Clarke, Holtkamp, et al. Integration of variant annotations using deep set networks boosts rare variant association genetics. Nature Genetics, 2024

NIH Track on GenAI, Cyberinfrastructure, Digital Twins, and Quantum Computing

Publications - Navigating Journal Submissions

Ragothaman Yennamalli, Yana Bromberg, Sergio Pantano, Ragothaman Yennamalli

UniProt: Evolving Tools and Data for Protein Science

The Universal Protein Resource (UniProt) is a cornerstone of molecular biology and bioinformatics, delivering high-quality, freely accessible protein sequence and functional information for over 20 years. This session presents a guided tour of UniProt’s latest features, datasets, and tools, reflecting its continued evolution to meet the needs of the scientific community. We will highlight data integrations—including AlphaFold structural predictions, AlphaMissense variant effect predictions, RNA editing, post-translational modifications (PTMs), and Human Proteome Project (HPP) datasets—and demonstrate embedded visualizations developed by UniProt and third-party contributors. Attendees will learn about improved tools for browsing, analyzing, and exporting data, along with recent enhancements to UniProt’s API and new Swagger documentation that streamline programmatic access and data integration. Whether you're a student or a seasoned researcher, this session will help you better leverage UniProt in your work. We will emphasize practical applications and encourage engagement with UniProt’s expanding capabilities. Attendees will leave with a deeper understanding of how to integrate UniProt resources into their workflows—and how to contribute feedback to guide its future development.

Enhancing Multi-Task CNNs for Regulatory Genomics Through Allelic and High-Resolution Training

Multi-task Convolutional Neural Networks (CNNs) have emerged as powerful tools for deciphering how genomic sequence determines gene regulatory responses such as chromatin accessibility or transcript abundance. These models can learn the sequence patterns recognized by regulatory factors from the variation across hundreds of thousands of loci in the genome. Their understanding of gene regulatory syntax enables them to be used to predict individual genomic variant effects across the cell types they were trained on, and to point to the affected biological mechanisms. However, our recent study and that of another group (Sasse et al. 2023) revealed in parallel that, despite strong performance on various variant effect prediction benchmarks (Avsec et al. 2021), these models fail to correctly determine how variants affect the direction of gene expression across individual, an essential capability for associating variants with phenotypes or diseases. To address these limitations and improve model learning from available data, I present two strategies. First, training with sequence variation: we developed a modeling approach that directly contrasts sequence differences to predict allele-specific and personalized functional measurements from RNA-seq, ATAC-seq, and ChIP-seq (Tu, Sasse, and Chowdharry et al. 2025; Spiro and Tu et al. 2025). We applied this approach to data from F1 hybrid mice and from humans with personal whole genome information, with varying degrees of success: while training on allele-resolved data improved predictions of differential signals, training on hundreds of personal genomes did not generalize variant effects to unseen genes. Second, training at higher resolution: we created models that analyze ATAC-seq at base-pair resolution, capturing both overall chromatin accessibility and the distribution of Tn5 transposase insertions (Chandra et al. 2025). Our results demonstrate that additionally modeling the ATAC-seq profile consistently improves predictions of differential chromatin accessibility. Systematic analysis of the models’ sequence attributions confirms that base-pair resolution training enables the model to learn a more sensitive representation of the regulatory syntax that drives differences between immunocytes.

Publications - Navigating Journal Submissions

Effective cover letter writing and manuscript preparation for submitting manuscripts in crowded research areas

I am one of the two Editors-in-Chief of the ISCB Society Journal Bioinformatics Advances that is published jointly with Oxford University Press. After giving a short introduction into the profile of the journal I will describe the process of editorial paper handling by our journal and the recommendation that can be derived from that about preparing a submission such as to most clearly place the contribution made by the authors. Careful placement of the original contribution is especially critical for topics in research areas that gather a large number of publications.

BOSC: Bioinformatics Open Source Conference

BOSC: Bioinformatics Open Source Conference

BOSC: Bioinformatics Open Source Conference

Working together to develop, promote and protect our data resources: Lessons learnt developing CATH and TED

The CATH protein domain structure classification was the vision of the pioneering computational scientist Janet Thornton. Algorithms developed by Orengo and Taylor in the lab of Willie Taylor enabled the analyses that laid the foundations for CATH. Since then, the Orengo team have taken CATH forward in many ways. Working closely with the protein sequence, structural and evolutionary biology communities provided the focus and feedback to shape the resource. Maintaining the value and integrity of CATH has necessitated continuously embracing new types of data as it became relevant and developing the appropriate tools for this. For example, CATH was recently expanded >400-fold with predicted structures from AlphaFold Database (AFDB) using novel AI-based tools and the CATH team are collaborating with the AFDB team at the EBI to make the data available to the wider community. CATH is also a partner resource in InterPro and was also used by the Structural Genomics Consortia in the States for more than 15 years to probe novel fold and function space. All CATH data and tools are publicly available. The talk will present landmark developments and how the resource has benefitted from extensive collaborations with the wider community to handle the data expansions and to provide accurate data needed by the community. It will also draw on CATH experience to reflect on strategies for supporting open data and open source.

EvolCompGen: Evolution & Comparative Genomics

Fast tumor phylogeny regression via tree-structured dual dynamic programming

Henri Schmidt, Yuanyuan Qi, Ben Raphael, Mohammed El-Kebir

Reconstructing the evolutionary history of tumors from bulk DNA sequencing of multiple tissue samples remains a challenging computational problem, requiring simultaneous deconvolution of the tumor tissue and inference of its evolutionary history. Recently, phylogenetic reconstruction methods have made significant progress by breaking the reconstruction problem into two parts: a regression problem over a fixed topology and a search over tree space. While effective techniques have been developed for the latter search problem, the regression problem remains a bottleneck in both method design and implementation due to the lack of fast, specialized algorithms. Here, we introduce fastppm, a fast tool to solve the regression problem via tree-structured dual dynamic programming. fastppm supports arbitrary, separable convex loss functions including the L2, piecewise linear, binomial and beta-binomial loss and provides asymptotic improvements for the L2 and piecewise linear loss over existing algorithms. We find that fastppm empirically outperforms both specialized and general purpose regression algorithms, obtaining 50-450x speedups while providing as accurate solutions as existing approaches. Incorporating fastppm into several phylogeny inference algorithms immediately yields up to 400x speedups, requiring only a small change to the program code of existing software. Finally, fastppm enables analysis of low-coverage bulk DNA sequencing data on both simulated data and in a patient-derived mouse model of colorectal cancer, outperforming state-of-the-art phylogeny inference algorithms in terms of both accuracy and runtime.

GenCompBio: General Computational Biology

From High-Throughput Evaluation to Wet-Lab Studies: Advancing Mutation Effect Prediction with a Retrieval-Enhanced Model

Yang Tan, Ruilin Wang, Banghao Wu, Liang Hong, Bingxin Zhou

Enzyme engineering is a critical approach for producing enzymes that meet industrial and research demands by modifying wild-type proteins to enhance properties such as catalytic activity and thermostability. Beyond traditional methods like directed evolution and rational design, recent advancements in deep learning offer cost-effective and high-performance alternatives. By encoding implicit coevolutionary patterns, these pre-trained models have become powerful tools for mutation effect prediction, with the central challenge being to uncover the intricate relationships among protein sequence, structure, and function. In this study, we present VenusREM, a retrieval-enhanced protein language model designed to capture local amino acid interactions across both spatial and temporal scales. VenusREM achieves state-of-the-art performance on 217 assays from the ProteinGym benchmark. Beyond high-throughput open benchmark validations, we conducted a low-throughput post-hoc analysis on more than 30 mutants to verify the model’s ability to improve the stability and binding affinity of a VHH antibody. We also validated the practical effectiveness of VenusREM by designing 10 novel mutants of a DNA polymerase and performing wet-lab experiments to evaluate their enhanced activity at elevated temperatures. Both in silico and experimental evaluations not only confirm the reliability of VenusREM as a computational tool for enzyme engineering but also demonstrate a comprehensive evaluation framework for future computational studies in mutation effect prediction. The implementation is publicly available at https://github.com/tyang816/VenusREM.

NIH Track on GenAI, Cyberinfrastructure, Digital Twins, and Quantum Computing

Graph Kolmogorov-Arnold Networks for Interpretable Alzheimer's Disease Diagnosis from Structural MRI

Ariosto S. Silva, Ariosto Silva, Ariosto S. Silva, Ariosto S. Silva, Liang Dong

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, an architecture that integrates Kolmogorov-Arnold Networks (KANs) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data from 91 subjects, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 5.2% in classification accuracy (62.6% vs. 57.4%) while providing interpretable insights into key brain regions associated with AD. The model identifies the hippocampus, inferior parietal gyrus, and amygdala as most critical for diagnosis, with normalized importance scores of 0.65, 0.61, and 0.60, respectively. These identified regions align with established neurological research on AD pathology. This approach offers a robust and explainable tool for AD diagnosis, potentially facilitating earlier intervention and more personalized treatment planning.

Publications - Navigating Journal Submissions

Choosing journals for submission in popular topics

There are many factors that authors may take into account when submitting to a journal: scope fit, journal metrics, speed, names on the editorial board, business model (e.g. Open Access) and a journal's reputation. With the number of journals increasing at a rapid pace and the increasing prevalence of broad-scope journals, how authors make decisions about where to publish becomes increasingly complex. This presentation will cover successful strategies for choosing the right journal, and ways to pivot if a manuscript is rejected by the first-choice journal.

Genomics 2 Proteins portal: A resource and discovery platform for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types – to “map” variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org): a human proteome-wide resource that maps 49,500,857 genetic variants onto 42,481 protein sequences and 84,318 structures (according to Dec 2024 release), with a comprehensive set of structural and functional features. Additionally, the G2P portal allows users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotypes.

Publications - Navigating Journal Submissions

How editors make decisions on submissions?

Jason Papin, University of Virginia, PLOS Computational Biology, USA, Feilim Mac Gabhann

3DSIG: Structural Bioinformatics and Computational Biophysics

Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm

Meghana Kshirsagar, Artur Meller, Ian R. Humphreys, Samuel Sledzieski, Yixi Xu, Rahul Dodhia, Eric Horvitz, Bonnie Berger, Gregory R Bowman, Juan Lavista Ferres, David Baker, Minkyung Baek

The majority of proteins must form higher-order assemblies to perform their biological functions, yet few machine learning models can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by finetuning several classes of protein foundation models, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods achieving an average AUC-PR of 0.47, 0.44 and 0.49 across homo-oligomer symmetries on three held-out test sets compared to 0.24, 0.24 and 0.25 with template-based search. Seq2Symm uses a single sequence as input and can predict at the rate of ~80,000 proteins/hour. We apply this method to 5 proteomes and ~3.5 million unlabeled protein sequences, showing its promise to be used in conjunction with downstream computationally intensive all-atom structure generation methods such as RoseTTAFold2 and AlphaFold2-multimer. Code, datasets, model are available at: https://github.com/microsoft/seq2symm.

KnetMiner for Smarter Science: Leveraging Knowledge Graphs & LLMs for Productive Gene Research

Arne De Klerk, Marco Brandizi, Sam Holegar, Alex Warr, Sardor Asatillaev, Keywan Hassani-Pak

In the interpretation of high‑throughput genomic data, the identification of candidate genes underlying differential expression or genome‑wide association study (GWAS) signals remains a major challenge. Here, we describe recent enhancements to the KnetMiner platform, which integrates knowledge mining, large language models (LLMs) and retrieval‑augmented generation (RAG) to accelerate gene discovery. KnetMiner constructs a comprehensive knowledge graph by integrating curated ontologies, structured databases and literature‑derived relationships. Upon input of a gene list or genomic loci, semantic queries extract relevant subgraphs that are transformed into context‑aware prompts for an LLM. Through RAG, the model retrieves supporting evidence from external sources - including publications and functional annotations - to produce gene summaries and prioritisation scores. We will present the platform's modular architecture and real use cases of KnetMiner assisting scientists in mining for candidate genes for complex traits in wheat and other crops.

EvolCompGen: Evolution & Comparative Genomics

Bayesian inference of fitness landscapes via tree-structured branching processes

Xiang Ge Luo, Jack Kuipers, Kevin Rupp, Koichi Takahashi, Niko Beerenwinkel

Motivation: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. Results: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies.