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Daniel Vasco

Daniel Vasco

Auxiliary Researcher

Auxiliary Researcher
Member type
Former Members
CIBIO-InBIO, Universidade do Porto, Campus de Vairão, R. Padre Armando Quintas, 4485-661 Vairão, Portugal

This is a very exciting time in bioinformatics and computational biology for data analysis and algorithm development. In my work I am broadly interested in simulation-based analysis of complex data sets for biological systems and have published methods for the computational and statistical analysis of population genetic, ecological, clinical and agricultural data. I am currently working on developing algorithms for whole genome inference using next generation sequence (NGS) data, particularly focusing on the reconstruction of demographic and selection history of human populations. A second focus is the development of high-throughput genotyping and phenotyping algorithms to analyze the population genetics and evolution of single cell NGS data sampled from cancer patients.

Whole Genome Inference. The cost of genotyping at the bulk cell level has recently become very cheap, now running at 10 cents per megabase rendering these sorts of data more broadly accessible to individual researchers. Hence my focus is on developing a computational genomics platform that is of general use to investigators engaged in gathering and analyzing NGS data sets.

Single Cell Bioinformatics.Currently, there exist few bioinformatics tools and workflows for single cell data. Fundamental issues include: calling copy number variations, identifying mutated genes in tumor samples, reconstructing single-cell lineages. Work in this field will help transfer new technologies to basic biology and medical research.

Analysis of temporally and spatially dependent time series data. Currently, ecological and evolutionary data in biology that are age or space dependent are often modeled using complex coupled ordinary differential equations (ODE), partial differential equations (PDE) or integral equations (IE). Recently, I have been exploring new and simpler ways of using event-driven simulation algorithms as an alternative (yet mathematically equivalent) method of modeling these types of data and comparing the results of event driven models with more standard ODE, PDE and IE models.

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