Microbiota Data Analysis and Statistics 

My background is in agricultural soil science, which is largely based on field experiments. I have a PhD in microbiology and in my thesis project I studied antimicrobial resistance in Finnish agroecosystems. After my graduation I worked briefly at the Risk Assessment Research Unit at the Finnish Food Authority and then continued as a postdoc at Purdue University, Indiana, US. I returned to Finland in 2020 for another postdoc in an EU-project on recycled fertilizers and microbial threats. In early 2023 I established my own research group studying One Health threats and antimicrobial resistance in agriculture and in the past and the present. In practice we sample archeological excavations and corresponding samples in modern world and compare genomes and metagenomes from different sources. Throughout my scientific career I have been studying real-life scenarios by combining fieldwork and experiments to data analyses, and to my surprise I have noted that often in microbiology the link between the research plan and statistical analyses is not understood. In my presentation I will walk you through the process from obtaining samples to statistical analyses. 

Tuomas Borman is a doctoral researcher at the University of Turku in the Turku Data Science Group. His research focuses on developing computational methods for microbiome data science. He is a key developer of the mia (MIcrobiome Analysis) framework, which includes tools and resources for microbiome analysis within the Bioconductor ecosystem. 

In this presentation, we will discuss Bioconductor, the world's largest bioinformatics project, and the options it offers for microbiome analysis. We will specifically focus on the MIcrobiome Analysis (mia) ecosystem, which provides tools for microbiome data science. Additionally, we will introduce an online book that aims to demonstrate effective microbiome data analysis techniques and disseminate best practices in the field. All these resources are open-source and available for free.

Tanel Kaart received his PhD in mathematical statistics in 2006 at University of Tartu, Estonia. Currently he holds the data science professor position in Estonian University of Life Sciences. He has developed many lecture courses in mathematical statistics and population genetics, currently he teaches ten courses at the University of Tartu and the Estonian University of Life Sciences. His research interests focus on statistical methods (especially modelling and multivariate statistics) applied in life sciences (animal and plant science, medicine, genetics, psychology, ecology). He has published over 130 peer-reviewed papers in international journals, h-index (WoS) is 22.

Dr. Ilja Weinstein has his main background in biology with MS degree in physiology, PHD in bioinformatics and licentiate in veterinary medicine. His main interests are in applying novel sequencing methods and bioinformatics to improve diagnostics of viral diseases in both healthcare and veterinary medicine. Weinstein has 14 years of experience in developing and implementing methods for biological sequence analysis, is experienced in laboratory work with PCR, and has published 15 research papers with 556 citations and h-index of 11.