Description

Leader of the group Life Sciences and Health: Leen Stougie.

Our research group creates fundamental knowledge and applied solutions in the broad field of life sciences. We promote understanding of how biological processes work in detail. Our interdisciplinary team of computer scientists, mathematicians and theoretical biologists develops new models, theories, and decision support systems in collaboration with experimental biologists and medical experts. We are motivated by applications of our work in practice

 

Watch our group video to get a glimpse of our activities or click here for more information about our group structure.

 

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LSH Seminar Luca Denti

  • 2019-03-26T16:00:00+01:00
  • 2019-03-26T17:00:00+01:00
March 26 Tuesday

Start: 2019-03-26 16:00:00+01:00 End: 2019-03-26 17:00:00+01:00

L016

MALVA: genotyping by Mapping-free ALlele detection of known VAriants

The amount of genetic variation discovered and characterized in human populations is huge, and is growing rapidly with the widespread availability of modern sequencing technologies. Such a great deal of variation data, that accounts for human diversity, leads to various challenging computational tasks, including variant calling and genotyping of newly sequenced individuals. The standard pipelines for addressing these problems include read alignment, which is a computationally expensive procedure. A few mapping-free approaches were proposed in recent years to speed up the genotyping process. While such tools are very fast, they focus on isolated, bi-allelic SNPs, providing limited support for multi-allelic SNPs, indels, and genomic regions with high variant density. To address these issues, we introduce MALVA, a fast and lightweight mapping-free method to genotype an individual directly from a sample of reads. MALVA is the first mapping-free tool that is able to genotype multi-allelic SNPs and indels, even in high density genomic regions, and to effectively handle a huge number of variants such as those provided by the 1000 Genome Project. An experimental evaluation on whole-genome data shows that MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools.

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Current projects with external funding

  • 3D dose reconstruction for children with long-term follow-up Toward improved decision making in radiation treatment for children with cancer
  • Enhancing protein-drug binding prediction
  • ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning
  • Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion
  • Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
  • Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
  • Multi-Objective Deformable Image Registration (MODIR) – An Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with (MODIR)

Related partners

  • AMC Medical Research
  • Elekta Limited
  • Nucletron Operations BV
  • Xomnia
  • Academisch Medisch Centrum
  • Biomedical Imaging Group Rotterdam
  • Erasmus Universiteit Rotterdam
  • KiKa
  • Technische Universiteit Delft
  • Universiteit Leiden