Discover the SIB Remarkable Outputs 2025

Seven outstanding data resources, software tools, and peer-reviewed publications from our members have been recognized as SIB Remarkable Outputs – an annual spotlight on the excellence and diversity of bioinformatics achievements in Switzerland.

Would you like to be among next year’s selected outputs? Visit the Remarkable Outputs page for the next submission deadline.

A fast search tool for biological sequences
Published in: Nature
Group led by: Gunnar Rätsch, ETH Zurich
Description: MetaGraph finds specific DNA, RNA and protein sequences across millions of public records in seconds. Like a regular internet search engine, the scalable tool efficiently indexes and queries massive global datasets – setting a new standard for large-scale exploration of sequencing data.
What the committee said: “MetaGraph addresses a major bottleneck: the ability to search and explore rapidly growing volumes of sequencing data. Its outstanding technical contribution transforms how scientists explore large-scale biological data and accelerates discovery across many areas of life science.”
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A tool to evaluate AI-predicted macromolecular complexes
Published in: Nature Methods
Group led by: Torsten Schwede, University of Basel
Description: New AI tools can predict the structure of large biological complexes involving proteins, DNA and small molecules. OpenStructure evaluates the accuracy of such predictions against experimentally determined structures, through a reproducible, transparent and scalable workflow.
What the committee said: “OpenStructure represents a major advance for structural biology and benchmarking practices, by establishing a robust, fully automated system for evaluating next-generation, AI-driven structure prediction methods. The system is already integrated into community benchmarking efforts, showing its relevance and impact.”
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A method to detect parent-of-origin effects on traits
Published in: Nature
Group led by: Zoltán Kutalik, University of Lausanne
Description: Metabolism, disease risk and other traits were shown to shift in different — and even opposite — directions depending on whether a genetic variant comes from the mother or father. The study used a novel approach to analyse parent-of-origin effects across large human genetic datasets.
What the committee said: “This analytical framework enables the systematic study of an important but often overlooked evolutionary phenomenon. The ability to infer parental origin of alleles with limited pedigree information represents an impactful contribution to statistical genetics and bioinformatics.”
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A gut microbiome resource at the subspecies level
Published in: Cell Host & Microbe
Groups led by: Evgeny Zdobnov, University of Geneva and Evgenia Kriventseva, University of Geneva
Description: The first comprehensive genomic catalogue of human gut microbes at a subspecies level captures within-species genetic variation that standard analyses miss. Machine-learning models using HuMSub subspecies profiles predicted colorectal cancer from stool samples more accurately than species-level models.
What the committee said: “HuMSub addresses a long-standing limitation of microbiome research. By distinguishing subspecies within the same species and linking these differences to functional and disease-related traits, the approach enables more precise biological interpretation of microbiome data.”
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A tool for studying evolution using 3D protein structures
Published in: Nature Structural & Molecular Biology
Group led by: Christophe Dessimoz, University of Lausanne
Description: FoldTree uncovers relationships between proteins by comparing their three-dimensional structures rather than their genetic sequences. This novel approach allows scientists to trace ancient evolutionary histories that are difficult to detect using traditional sequence-based methods.
What the committee said: “FoldTree shows how a transformative breakthrough in one field – in this case, AI-based protein structure prediction – can help tackle a long-standing challenge in another field. Its innovative methodology has applications across structural biology, evolutionary biology and related fields.”
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A method for genetically characterizing cancer-cell clusters
Published in: Nature Genetics
Groups led by: Niko Beerenwinkel, ETH Zurich and Nicola Aceto, ETH Zurich
Description: Groups of tumour cells in the bloodstream can seed metastases. A new algorithm enables DNA sequence analysis of individual cells within such clusters – and provides the first evidence that many contain genetically distinct cells. The method opens the door to less invasive diagnostics.
What the committee said: “By solving a challenging problem at the interface of phylogenetics and single-cell genomics, this work highlights the power of bioinformatics to drive discovery in biomedical research. It also exemplifies the value of close collaboration between experimental and computational scientists.”
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An AI tool for faster, cheaper drug discovery
Published in: Nature
Group led by: Bruno Correia, EPFL
Description: BindCraft designs ‘binder’ proteins that modify the activity of therapeutic targets, in a single computational step – overcoming the need for large-scale experimental screening. The open-source AI tool is already widely used, with proven applications including blocking allergens and precisely targeting gene therapies.
What the committee said: “This innovative method marks a significant advance for protein design. By turning binder discovery into a reliable computational process, it opens new opportunities for therapeutic design and innovation in biotechnology.”
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