Position/Title: Assistant Professor
Phone: (519) 824-4120 ext. 52482
Office: ANNU 127
Dan Tulpan’s research interests range from computational biology and bioinformatics to mathematical modelling and computer vision (using computers to extract and process data from images). His expertise has application across a broad range of topics, but at U of G, Dan is applying his skills to livestock breeding and other areas of plant and animal science. Previously, Dan was a research officer in the Scientific Data Mining Team, Digital Technologies Research Center of the National Research Council in Moncton, New Brunswick. There, he headed the NRC Atlantic Bioinformatics Laboratory. He has held numerous research and academic positions across Canada and worked in the software industry internationally.
- Postdoctoral Fellow, Simon Fraser University, Department of Molecular Biology and Biochemistry, 2006-2007
- Ph.D., University of British Columbia, Department of Computer Science, 2000-2006
- B.Sc./B.Eng., Politechnic University of Bucharest, Department of Engineering Sciences and Computers, 1995-2000
Affiliations and Partnerships
- Adjunct Professor, School of Computer Science, University of Guelph
- Association of Computing Machinery (ACM)
Selected Awards and Honours
- 2018: NRC Instant Award, For significant effort and support in hosting the Next Generation Sensors workshop and developing the subsequent proposal in support of potential Challenge programs.
- 2015: ISMB - BioVis Design Contest Award (1st Place), The Circular Secondary Structure Uncertainty Plot (CS2-UPlot) - Visualizing RNA Secondary Structure with Base Pair Binding.
- 2015: Breakthrough of the Year - NRC ICT Award Project, An Enrichment Model for Wheat Gene Annotations Using Phylogeny, Orthology and Existing Gene Ontologies
- AGR*3200: Computing for Bioscientists, Winter 2022, new course
- ANSC*6330: Topics in Computational Biology and Bioinformatics, Fall 2021
- ANSC*6100: Special Project - Machine Learning Modelling, Winter 2022, new course
Dan is relatively new to U of G, and he’s enthused about building an innovative research program. His focus will be on advanced computing and information technology to provide solutions to challenges in animal agriculture such as automatic animal identification, tracking and phenotype acquisition. He has published over 50 peer-reviewed articles, edited a book, co-authored scientific software for computational biology and bioinformatics and contributed to the formation of several highly qualified personnel such as students and programmers. Dan serves on several international conferences and journal editorial boards, co-organizes the ACM Symposium of Applied Computing Track on Bioinformatics and maintains strong research collaboration with researchers in Italy, Switzerland, Spain and other countries.
Research Themes and Projects
- Theme 1: Development of an automatic real-time data acquisition platform for phenomics, physiological and environment information for livestock and poultry. The platform should be capable to capture and store information in real-time and periodically based on a variety of on-animal (e.g. accelerometers, magnetometers, gyroscopes, visual, audio, location), off-animal (e.g. cameras, thermal imaging, walk-over scales, LIDAR lasers) and/or in-animal (e.g. RFID chips) sensors. Ideally, the platform will be able to interface with existing acquisition systems developed by other research groups.
- Theme 2: Development of bioinformatics and computer vision data integration pipelines capable to efficiently select, filter, model and process the acquired information. The original data will be transformed into ready-to-analyze information and will be combined when needed with complementary external information resources (e.g. environment data, GPS locations, maps, other omics sources)
- Theme 3: Development of real-time interactive analytic and visualization tools to turn high-throughput phenomics data into testable hypotheses and actionable results and facilitate genotype-phenotype association studies.
(Under)Graduate Student Information
Dan’s research is truly interdisciplinary and has a strong bioinformatics and biology component. Therefore, graduate students should have good computing skills and be keenly interested in learning new tools and applying their knowledge to biological problems. Supervising, mentoring, guiding and working with talented, hard working students is a passion for Dan and he is enthused about helping students reach their research potential and expand their knowledge with each project they contribute to.
- coming soon
Currently Dan supervises or co-supervises the following undergraduate and graduate students and PDF:
|Aricibasi, Harry||B.Sc. Bio-medical science (Contract)||Dr. Renee Bergeron (main advisor, Animal Biosciences)|
|Chan, Esther||M.Sc., ANSC|
|Harvie, Julia||M.Sc., BINF+AI specialisation||Dr. Dirk Steinke (main advisor, Integrative Biology)|
|Lopes, Lucas||Ph.D., ANSC||Dr. Christine Baes (main advisor, Animal Biosciences)|
|Rodriguez, Kaitlyn||M.Sc., BINF|
|Saljay, Niela (Charis)||B.Sc., Neuroscience (URA)|
|Dr. Saeed Shadpour||Post-Doctoral Fellow||
Dr. Christine Baes (Animal Biosciences)Dr. Flavio Schenkel (Animal Biosciences)
|Wang, Zhuoyi (Elena)||M.Sc., ANSC|
|You, Jihao||Ph.D., ANSC||Dr. Jennifer Ellis (main advisor, Animal Biosciences)|
|Adams, Sarah||Ph.D., ANSC||Dr. Jennifer Ellis (main advisor, Animal Biosciences)|
|Ahmed, Syed||B.Sc., CS (URA, Work Study Student)|
- Z. Wang, S. Shadpour, E. Chan, V. Rotondo, K.M. Wood, D. Tulpan. Applications of machine learning for livestock body weight prediction from digital images. Journal of Animal Science, 99(2):1-15, skab022, 2021.
- M. Yoosefzadeh-Najafabadi, D. Tulpan, M. Eskandari. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. Plos One, 16(4):1-18,
- JL. Ellis, M. Jacobs, J. Dijkstra, H. van Laar, J.P. Cant, D. Tulpan, N. Ferguson. Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data. Animal, 14(S2):s223–s237, 2020.
- S. Nayeri, M. Sargolzaei, D. Tulpan. A review of traditional and machine learning methods applied to animal breeding. Animal Health Research Reviews, 20(1): 31-46, 2019.
- S Léger, MBW Costa, D Tulpan. Pairwise visual comparison of small RNA secondary structures with base pair probabilities. BMC Bioinformatics 20 (1), 293, 2019.
- R. Goyal, D. Tulpan, D. Gonzalez-Pena Fundora, N. Chomistek, C. West, B.E. Ellis, M. Frick, A. Laroche, N.A. Foroud. Analysis of MAPK and MAPKK gene families in wheat and related Triticeae species. BMC Genomics, 19:178, 2018.
- M.B.W. Costa, C.H. Siederdissen, D. Tulpan, P. Stadler, K. Nowick. Temporal ordering of substitutions in RNA evolution: Uncovering the structural evolution of the Human Accelerated Region 1. Journal of Theoretical Biology, 438:143-150, 2018.
- D. Tulpan, S. Leger. The Plant Orthology Browser: An Orthology and Gene-Order Visualizer for Plant Comparative Genomics. The Plant Genome, 10(1):1-12, 2017.
- D. Tulpan, C. Bouchard, K. Ellis, C. Minwalla. Detection of clouds in sky/cloud and aerial images using moment based texture segmentation. In Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, Florida, USA, June 13-16, 2017.
For a more comprehensive list of publications please visit Dr. Tulpan's Google Scholar page.
Featured Bioinformatics Software and Data Sets
- The Circular Secondary Structure Base Pair Probabilities Plot, 2019
- The Plant Orthology Browser, 2017
- Dataset for Sense and Avoid Collision Detection for Drones, 2016
- MetaboHunter - identification of individual metabolites in 1H-NMR spectra of complex mixtures, 2011