Carl Nettelblad

Key publications

Benedikt J. Daurer, Max F. Hantke, Carl Nettelblad, Filipe R. N. C. Maia. Hummingbird: Monitoring and Analyzing Flash X-Ray Imaging Experiments in Real Time, Journal of Applied Crystallography, 49, 1042-1047 (2016)

John M. Hickey, Gregor Gorjanc, Rajeev K. Varshney, Carl Nettelblad. Imputation of Single Nucleotide Polymorphism Genotypes in Biparental, Backcross, and Topcross Populations with a Hidden Markov Model, Crop Science 55, 1934-1946 (2015)

Tomas Ekeberg et al. Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser, Physical Review Letters 114, 098102 (2015)

Carl Nettelblad. Breakdown of Methods for Phasing and Imputation in the Presence of Double Genotype Sharing, PLoS ONE 8(3), e60354 (2013)

Carl Nettelblad. Inferring haplotypes and parental genotypes in larger full sib-ships and other pedigrees with missing or erroneous genotype data, BMC Genetics 13:85 (2012)

Research Interests

My main interests are developing new statistical models and efficient computational implementations of general applicability. I have worked a lot on methods for imputation and phasing of genotypes, especially in the face of uncertainty (e.g. contaminated samples, low-coverage sequencing, with ancient DNA as a new exciting application area). My original applications were in plant and animal breeding, where pedigrees are available, but we are moving beyond that.

In another exciting collaboration, I am studying computational techniques for a challenging new imaging modality, “flash X-ray imaging”, which allows the study of individual proteins and other small biological particles at high resolution through exposure to very brief X-ray pulses at special X-ray free electron lasers. One unit exists at Stanford in the U.S., another is being constructed in Hamburg. In this case, the possible resolution attainable is also a matter of how to best treat noise and uncertainty, while getting reasonable results in reasonable time.

As a bit of a computer nerd at heart, I also enjoy the fact that this application leads us to use heterogeneous computing, e.g. GPUs. Very soon, we will all need to use more radical high performance approaches in life science data processing to be able to cope with the speed at which data is collected.

Group members

Kristiina Ausmees, PhD student
(Alberto Pietrini, PhD student, in Molecular biophysics group at ICM, UU)

Last updated: 2022-11-30

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