Ecology, environment
and agriculture

Unraveling ecosystem complexity with bioinformatics

Session chairs: Germán Bonilla-Rosso and Catalina Chaparro Pedraza

Session description: Living organisms interact with each other and their environment, shaping the healthy functioning of our planet. A mechanistic understanding of ecosystem structure and dynamics is crucial to face current threats such as climate change, food security and environmental pollution. Ecosystem diversity is vast, and its complexity hampers our capacity to identify general patterns and predictive models. New monitoring approaches generate massive,  complex and multi-dimensional datasets that demand the development of novel bioinformatic tools and computational approaches. 

These methods will help us understand ecosystem dynamics, leading to applications in biotechnology, conservation biology, ecology and agriculture. Sequencing data (amplicon sequencing, genomics, metagenomics and transcriptomics) has proven invaluable due to its versatility, biological resolution and reusability across biological scales and areas. Integrating sequencing data with other multidimensional datasets like metabolomics,  proteomics, biogeochemical or population data provides a higher level of understanding. This and the adoption of high resolution experiments, temporal dynamics and mathematical modelling opens the unprecedented possibility to unravel the mechanistic processes governing ecosystem dynamics.

This session will showcase research that uses innovative methods for the analysis of large multidimensional datasets that aim to understand the dynamics and mechanisms shaping the abundance, distribution and interactions of organisms in any ecosystem across all biological scales. In particular. Likewise, we invite research  that addresses the transition from descriptive studies into mechanistic understanding. This shift is crucial for sustainable resource management, environmental monitoring, and agricultural development. In order to foster transferability in data science, we also call for approaches that focus on transparent and sharable datasets.

Topics include but are not limited to: microbiomes of all shapes and sizes, diversity, AI approaches to analyse multidimensional datasets, strain diversity, adaptation to environmental changes, micro- and macro-evolution, morphology, remote sensing, host-microbiome interactions and ecological and environmental drivers of microbial communities, particularly with applications to ecology, environmental sciences and agriculture.

Evolution and phylogeny 


Modelling of evolutionary forces within and between species

Session chairs: Frederic Bastian and Nina Marchi

Session description: The growing amounts of genomic data available allow for studying the evolution of organisms, populations and species. Phylogenetics, along with population genomics, form a cornerstone of such studies. They aim at understanding evolutionary processes within and across species: their evolutionary history, genetic information exchanges, phenotypic and developmental changes. The methods used to support these analyses include, but are not limited to: sequence alignment, evolutionary modelling, and description of phenotypic and environmental interactions. The knowledge gained from these studies is valuable for, e.g., informing decisions in public health and medicine, as well as in species conservation and environmental preservation programs.

Topics include but are not limited to: comparative genomics; comparative transcriptomics; phylogenetics; population genetics; evo-devo analyses; likelihood, Bayesian, and other methods for evolutionary modeling.

Genes and genomes

Navigating the rapidly expanding space of Genes and Genomes

Session chairs: Maria Anisimova and David Francisco

Session description: Genomics continues to be the fastest growing field of molecular biology and the volume of sequencing data has reached a petabase-scale requiring and enabling novel analytical methodologies. While we can quantify the uncovered genomic variability, predicting phenotypic variability still remains challenging. The additional layers of molecular phenotypes, the various ‘-omics’ data including epigenetic modifications, promise a solution that connects the genotypes to phenotypes, yet it comes with new levels of complexity. In this session we invite talks from SIB members that tackle these problems.

Topics include but are not limited to: bioinformatics tools for analysing genes and genomes; gene network analysis; gene regulation genomics; comparative genomics; structural variant identification; gene function prediction and analysis; genomic interactions; analysis of genome-scale data; chromatin biology; epigenetics and transcriptional regulation; gene and genome evolution.

Medicine and health

Personalised health and disease, from molecules to systems

Session chairs: Janna Hastings and Patrick Pedrioli

Session description: individual biological differences affect susceptibility to disease and the response to treatments. The promise of a more personalised approach in medicine is that these individual differences can be used to determine optimal health strategies and optimal treatments in an individualised way. Bioinformatics data and methods are essential in realising this vision, spanning the full range of measurements including genomics, transcriptomics, proteomics, metabolomics, and strategies that integrate across different layers, multi-omics and systems biology. In this session we welcome contributions of bioinformatics methods and data-driven results in personalised medicine for health, better understanding the mechanisms of disease, and for treatments.

Topics include but are not limited to: personalised medicine, systems biology, transcriptomics, proteomics, metabolomics, multi-omics, machine learning, functional genomics, risk prediction, clinical data science, language models. 

New methods and tools in
bioinformatics

Mathematical and computational approaches to solve biological problems

Session chairs: Mark Ibberson and Sandra Mitrović

Session description: The quantity of data generated by life sciences has grown exponentially over the years, but finding good ways to handle and analyse these massive data is still a major challenge. It is also necessary to explore them with a systematic approach to reveal the behaviour of the system as a whole rather than as the sum of its parts. Hence, this session of SIB days 2024 brings together researchers to discuss statistical and algorithmic approaches to improve data management, analysis, curation, and interpretation.

Topics include but are not limited to: machine learning; text mining (including language models); pattern recognition; Bayesian approaches; read mapping for second and third generation sequencing technologies; analysis of high-throughput biological data (transcriptomics, proteomics, metabolomics, fluxomics); synthetic data generation; techniques for managing (e.g., data compression) and visualizing massive amount of sequencing data; methods for analysis of RNA sequencing (RNA-seq) data, including RNA expression, novel transcript assembly and splicing; methods for novel sequencing technologies such as single-cell sequencing, spatial transcriptomics, sequencing and Hi-C; epigenetics and gene regulation, including ChIP-seq analysis, methylation profiling, and histone modification; curation support tools; personalized health.

Proteins and proteomes 

Proteins and proteomes, from data to knowledge 

Session chairs: Frédérique Lisacek and Witold Wolski

Session description: Proteins are essential functional contributors in living organisms, for instance in building and protecting cells from diseases. We can now study proteins and protein modifications on a larger scale thanks to recent technological advancements. However, there is still a need to better understand and analyse this data in order to refine our knowledge of proteomes, to identify specific proteins or protein-related characteristics that could serve as markers for certain diseases or conditions and to discover drug targets to either treat or prevent these diseases. In this discussion, we will explore the different aspects of proteins, including how we measure and analyse their abundance, modifications, interactions, and their individual and collective roles in biology.

Topics include but are not limited to: 

  • Novel mass spectrometry tools and workflows for proteomics, metaproteomics, and immunoproteomics
  • Imaging mass spectrometry for proteomics
  • Analysis and annotation of proteoforms and post-translational modifications and prediction of protein function
  • Bioinformatics for protein-protein interactions and interactions with small molecules, DNA or RNA
  • Omics data integration with a particular focus on Proteomics datasets
  • New applications of AI to the field of Proteomics

Single cell biology

Biology through the lens of single-cell and spatial omics technologies

Session chairs: Mark D. Robinson and Aurélie Grabriel

Session description: single-cell and spatial omic technologies have revolutionized cell biology discovery by providing unprecedented insights into the heterogeneity and spatial organization of cells within tissues. The measurement of multiple modalities enables the identification and characterization of rare cell populations, cell states, and dynamic cellular processes, and provides a comprehensive view of gene expression, chromatin accessibility, and epigenetic modifications at the single-cell level. The surge of single-cell omics data is leading to the generation of atlases composed of millions of cells, making computational methods indispensable to effectively harmonize, integrate and interpret these data. More recently, spatial transcriptomic and proteomic technologies provide spatially-resolved information about gene expression patterns and protein localization within intact tissues, enabling the mapping of cell types and their interactions within complex tissue architectures, facilitating the understanding of cellular heterogeneity and tissue organization. In this session, we focus on the bioinformatics contributions to these burgeoning fields.

Topics include but are not limited to: scRNA-seq, scATAC-seq, scDNA-Seq, single-cell multi-omics, single-cell mass and flow cytometry, single-cell imaging, new computational methods for single-cell data analysis, single-cell data integration, single-cell data harmonization, new insights from single-cell measurements, spatially-resolved transcriptomics, spatial omics.

Structural biology

Innovative tools to address bio-macromolecular structure and dynamics

Session chairs: Andrea Cavalli and Joana Pereira 

Session description: The three-dimensional structures of bio-macromolecules is an invaluable source of information to understand and predict what their function is and remain the cornerstone of structure-based drug design and protein engineering. Technical progress is providing an ever-increasing amount of high-quality experimental and predicted 3D macromolecular data, which requires computational approaches to evaluate, process and apply. These data are also being integrated with information regarding biology and function, existing variants, and binding partners such as proteins or drug-like molecules. Moreover, deep learning has revolutionized the theoretical prediction of bio-macromolecule tertiary and quaternary structures, opening new avenues to study complex systems. This session is dedicated to projects related to the collection, prediction, dissemination, use and interpretation of bio-macromolecular structures, from their 3D folds to their interactions, conformational variability and downstream applications.

Topics include but are not limited to: databases of bio-macromolecular structures; theoretical prediction and experimental characterization of tertiary and quaternary bio-macromolecular structures, including X-ray crystallography, NMR spectroscopy, electron microscopy and small angle X-ray scattering; molecular simulation and modelling; drug design; protein engineering; personalized medicine; evolution; protein interaction networks.

Variants interpretation in cancer: current limitations and future prospects

Panel moderators:  Daniel Stekhoven and Valérie Barbié 
Special guest : Obi Griffith
Panelists: GL names (to be chosen soon)

Panel description: Are the days of DNA counted? We challenge your perceptions at SIB Days this year in this exciting panel discussion. Despite remarkable advances in scientific knowledge, the clinical significance of genetic variants remains largely elusive, especially within the complex landscape of cancer biomarker discovery and management. This lack of understanding hinders patients from fully benefiting from the potential of genomics in their medical treatment. It is therefore legitimate to  question the future of genomics in clinical settings and whether it alone can be expected to provide all answers - or even worse, the stagnating impact is spilling over to other modalities too. Discover how computational epigenetics, systems immunology, and advanced machine learning are not just supplementing but revolutionizing our approach to diagnosing and treating cancer, or are they? Learn about the transformative impact of analyzing epigenetic landscapes, the predictive power of immune system interactions, and the precision of computational models in refining therapeutic strategies - potentially? This panel discussion promises to offer a deep dive into the sophisticated art of interpreting complex biological data, providing a broader, more integrated view of oncology, and challenge your ideas. 

Topics include but are not limited to: Genomics, variants annotation, somatic genetic variants, clinical interpretation, proteomics, artificial intelligence