Dr. Torsten Pook,

Current research and projects

• Project: "Accessing the genomic and functional diversity of maize to improve quantitative traits" (MAZE)

Membership in scientific societies

• German society for breeding (DGfZ)

Education

• Master thesis: "Forecast of daily shipments in logistics" (in cooperation with DB Schenker)

• Bachelor thesis: "Analysis of the imputing algorithm BEAGLE"

Working experiences

• Working Student in Management board: Finance and Controlling

• Werkstudent in Finance & Controlling

Teaching

• M.Agr.0186 Multivariate statistics with application in agriculture sciences

• M.Agr.0138 Selection theory, design and optimisation of breeding programs

• Student Assistent (Tutor) in Linear Algebra I (WS12,WS13)

• Student Assistent (Tutor) in Calculus II (SS 13)

Scientific awards

• EAAP Scholarship 2017

• Werner-Oettli-Award (2016) - Best Master thesis of the year

Publications

• Pook T, Schlather M, de los Campos G, Mayer M, Schoen CC, Simianer H (2019) HaploBlocker: Creation of subgroup specific haplotype blocks and libraries. Genetics

• Pook T, Schlather M, Simianer H (2020) MoBPS – Modular Breeding Program Simulator. G3: Genes, Genome, Genetics, 10(6):1915-1918

• Pook T, Mayer M, Geibel J, Weigend S, Cavero D, Schoen CC, Simianer H (2020) Improving imputation quality in BEAGLE for crop and livestock data. G3: Genes, Genome, Genetics, 10(1):177-188

• Pook T, Freudenthal J, Korte A, Simianer H (2020) Using local convolutional neural networks for genomic prediction. Frontiers in Genetics, 11:1366.

• Pook, T. , Nemri, E., Gonzalez Segovia, E.G., Simianer, H., Schoen, C.C., (2021), Increasing calling accuracy, coverage, and read depth in sequence data by the use of haplotype blocks, bioRxiv; https://doi.org/10.1101/2021.01.07.425688

Conference talks

• Pook T (2014) BEAGLE Imputation Algorithm. In: Genetisch Statistische Ausschluss der DGfZ, Mariaspring, Germany, 01.10.2014

• Pook T, Unterseer S, Schoen CC, Simianer H (2017) Creation of haplotype blocks in DH-lines in maize. In: Workshop Biometrische Aspekte der Genomanalyse. Heidelberg, Germany, 04.05.2017

• Pook T, Weigend S, Simianer H (2017) A generalized approach to calculate expecta-tion and variance of kinship in complex breeding schemes. In: Annual Meeting of EAAP. Talinn, Estonia, 30.08.2017

• Pook T, Schlather M, de los Campos G, Unterseer S, Schoen CC, Simianer H (2017) Identification of haplotype blocks in DH-lines in maize. In: 4th International Symposium on Genomics of Plant Genetic Ressources. Giessen, Germany, 04.09.2017

• Pook T, Weigend S, Simianer H (2017) Deterministische Berechnung von Erwar-tungswert und Varianz des Inzuchtniveaus in komplexen Zuchtprogrammen. In: Vor-tragstagung der DGfZ und GfT. Hohenheim, Germany, 21.09.2017

• Pook T, Schlather M, de los Campos G, Cavero D, Simianer H (2018) Creation of subgroup specific haplotype blocks and libraries. In: World Congress on Genetics to Livestock Production. Auckland, New Zealand, 15.02.2018

• Pook T, Schlather M, de los Campos G, Schoen CC, Simianer H (2018) Creation of subgroup specific haplotype blocks and libraries. In: World Congress on Genetics Applied to Livestock Production. Auckland, New Zealand, 16.02.2018

• Pook T, Schlather M, de los Campos G, Schoen CC, Simianer H (2018) Creation of subgroup specific haplotype blocks and libraries. In: Workshop on Bayesian statistics in population statistics. Hohenheim, Germany, 27.03.2018

• Pook T, Schlather M, Simianer H (2018) RekomBre – A tool to simulate and compare large scale breeding programs. In: Annual Meeting of EAAP. Dubrovnik, Croatia, 27.08.2018

• Pook T, Mayer M, Simianer H (2018) Analyse der Imputation-,Inferenz- und Phasingqualität in BEAGLE. In: Vortragstagung der DGfZ und GfT. Bonn, Germany, 13.09.2018

• Geibel J, Weigend S, Weigend A, Reimer C, Pook T, Simianer H (2018) Auswirkungen des Array Design Prozesses auf die Überschätzung der Heterozygotie. In: Vortragstagung der DGfZ und GfT. Bonn, Germany, 13.09.2018

• Pook T, Herzog S, Heise J, Simianer H (2018) Deep Learning – eine Alternative für die Zuchtwertschätzung. In: Genetisch Statistischer Ausschuss der DGfZ. Goettingen, Germany, 19.09.2018

• Simianer H, Pook T, Schlather M (2018) MoBPS – ein modularer Ansatz zur Evaluierung komplexer Zuchtprogramme. In: Genetisch Statistischer Ausschuss der DGfZ. Goettingen, Germany, 10.09.2018

• Pook T, Herzog S, Heise J, Simianer H (2018) Deep Learning – an alternative for genomic prediction. In: PLANTS and ANIMALS: Bridging the Gap in Breeding Research. Goettingen, Germany, 10.10.2018

• Pook T, Ganesan A, Ha T, Schlather M, Simianer H (2019) MoBPS – Modular Breeding Program Simulator. In: EFFAB and FABRE TP Annual General Meeting. Dublin, Ireland, 16.05.2019

• Pook T, Herzog S, Heise J, Simianer H (2019) Deep Learning – an alternative for genomic prediction. In: Annual Meeting of EAAP. Ghent, Belgium, 26.08.2019

• Pook T, Ganesan A, Ha T, Schlather M, Simianer H (2019) Optimization of introgression in breeding programs. In: Annual Meeting of EAAP. Ghent, Belgium, 28.08.2019

• Büttgen L, Ha T, Ganesan A, Simianer H, Pook T (2019) Zuchtplanungsrechnung zur Steigerung der Effizienz von Legehennenzuchtprogrammen. In: Vortragstagung der DGfZ und GfT. Giessen, Germany, 12.09.2019

• Geibel J, Pook T, Weigend S, Weigend A, Simianer H (2019) Wie Imputing SNP Ascertainment Bias reduzieren kann und wann es ihn verschärft. In: Vortragstagung der DGfZ und GfT. Giessen, Germany, 12.09.2019

• Vogjani E, Pook T, Simianer H (2019) Accounting for epistasis improves genomic prediction of phenotypes within and across environments. In: XXIVth EUCARPIA Maize and Sorghum Conference. Munich, Germany, 08.10.2019

• Pook T, Ganesan A, Ha T, Schlather M, Simianer H (2019) MoBPS – Modular Breeding Program Simulator. In: XXIVth EUCARPIA Maize and Sorghum Conference. Munich, Germany, 09.10.2019

• Büttgen L, Geibel J, Simianer H, Pook T (2020) Simulationsstudie zur Integration von Gesundheitsmerkmalen in Pferdezuchtprogramme. In: 9. Pferde-Workshop der DGfZ. Bad Bevensen, Germany, 18.02.2020

• Yokoyama T, Heumos S, Seaman J, Trybushnyi D, Pook T, Guarracino A, Garrison E, Bolleman J (2020) Semantic Variation Graphs: Ontologies for Pangenome Graphs. In: Intelligent Systems for Molecular Biology, Virtual Conference

• Seaman J, Heumos S, Pook T, Yokoyama T, Clark T, Garrison E (2020) Pantograph: Scalable Interactive Graph Genome Visualization. In: Intelligent Systems for Molecular Biology, Virtual Conference

• Büttgen L, Geibel J, Simianer H, Pook T (2020) Simulation study for the integration of health traits in horse breeding programs (2020). In: Annual Meeting of EAAP, Porto, Portugal. Hosted virtually: 01.-04.12.2020

• Meszaros G, Martinzes R, Lucero C, Burgozs Paz W, Navas M, Doekes H, Winding J, Pook T, Simianer H (2020) Breeding programs in the South American Creole cattle (2020). In: Annual Meeting of EAAP, Porto, Portugal. Hosted virtually: 01.-04.12.2020

List of Posters

• Ha NT, Pook T, Dierks C, Weigend S, Preisinger R, Simianer H (2017) A simulation approach to optimize breeding programs with application to the introgression of the blue egg color into a high performing layer line. In: 10th European Symposium on Poultry Genetics. St. Malo, France, 26.-28.6.2017

• Pook T, Schlather M, de los Campos G, Schoen CC, Simianer H (2018) Creation of subgroup specific haplotype blocks and libraries. In: PLANT2030 Statusseminar. Potsdam, Germany, 05.-07.02.2018

• Geibel J, Weigend S, Weigend A, Reimer C, Pook T, Simianer H (2018) Array design and SNP ascertainment bias. In: World Congress on Genetics Applied to Livestock Production. Auckland, New Zealand, 13.02.2018

• Pook T, Schlather M, de los Campos G, Schoen CC, Simianer H (2018) Creation of subgroup specific haplotype blocks and libraries. In: 60th Annual Maize Genetics Conference. St. Malo, France, 22.-25.03.2018

• Geibel J, Weigend S, Weigend A, Reimer C, Pook T, Simianer H (2018) Array design and SNP ascertainment bias. In: Population, Evolutionary and Quantitative Genetics Conference. Madison, USA, 13.-16.05.2018

• Pook T, Herzog S, Heise J, Simianer H (2019) Deep Learning – an alternative for genomic prediction. In: Gordon Research Conference on “Quantitative Genetics and Genomics”. Lucca, Italy, 10.-15.02.2019

• Pook T, Mayer M, Geibel J, Schoen CC, Simianer H (2019) Improving imputation quality in BEAGLE for crop data. In: PLANT2030 Statusseminar. Potsdam, Germany, 13.-15.03.2019

• Vogjani E, Martini JWR, Pook T, Schoen CC, Simianer H (2019) Accounting for epistasis improves genomic prediction of phenotypes within and across environments. In: PLANT2030 Statusseminar. Potsdam, Germany, 13.-15.03.2019

• Büttgen L, Ha T, Ganesan A, Simianer H, Pook T (2019) Zuchtplanungsrechnung zur Steigerung der Effizienz von Legehennenzuchtprogrammen. In: CiBreed Workshop on Breeding Challenges and Opportunities in the Realm of Biotic Stress. Goettingen, Germany, 09.09.2019

• Valle Torres D, Mayer M, Pook T, Ouzunova M, Presterl T, Schoen CC (2020) Optimizing the construction of haplotype blocks to increase genomic prediction accuracy across maize landraces. In: GPZ Conference Digital Breeding. Tulln, Austria, 11.-13.02.2020

• Pook T, Nemri A, Gonzalez Segovia, Mayer M, Simianer H, Schoen CC (2020) Increasing calling accuracy, coverage and read-depth in sequence data by the use of haplotype blocks. In: The 6th International Conference of Quantitative Genetics, Brisbane, Australia. Hosted virtually 02.-12.11.2020.

• Reimer C, Geibel J, Pook T, Weigend S, Simianer H (2020) Employing trio information to assess CNV detection performance in array data of Göttingen Minipigs (2020). In: Annual Meeting of EAAP, Porto, Portugal. Hosted virtually: 01.-04.12.2020

List of Software

• Published via CRAN: https://cran.r-project.org/web/packages/MoBPS/index.html and available at https://github.com/tpook92/MoBPS and www.mobps.de

• Available at https://github.com/tpook92/HaploBlocker

• Available at https://github.com/tpook92/HBimpute

• Available at https://github.com/evojgani/EpiGP

Workshops

• Using gene bank material for livestock populations: case studies and optimization using the MoBPS software

• Simulation of animal and plant breeding programs

• Deep Learning - from theory to application in genetics

List of Patents