Tarmo Äijö

Tarmo Äijö

Tarmo Äijö

Research Interests

My research focuses on answering questions that arise from molecular biology research, especially those related to immune responses, by developing novel computational and probabilistic methods for the integrative analysis of various omics data. The goal is to produce mathematical models that offer insights into information from large datasets or that can be used for predictive purposes. In particular, I am interested in developing accurate generative models and inferring them from experimental data by applying Bayesian methods. The Bayesian approach is particularly useful in bioinformatics since it enables integration of different data types, incorporation of prior information, and propagation of uncertainty. During my doctoral studies and postdoctoral research under the supervision of Harri Lähdesmäki, I have applied generative models to temporal analysis of RNA-seq data and to integrative analysis of different cytosine methylation modifications from bisulfite-based sequencing data. In addition to developing methodologies, I have been involved in many applied and interdisciplinary projects. I have collaborated with Anjana Rao and her lab at the La Jolla Institute for Allergy and Immunology on studies investigating the roles of the calcium-NFAT pathway in T cell exhaustion and of the Tet-induced oxidation products of 5-methylcytosines in T cell development.

My Google Scholar profile can be found here.

Education

D.Sc. (Tech.), Bioinformatics and Computer Science (2014)
Aalto University School of Science, Finland

M.Sc. (Tech.), Signal Processing and Mathematics (2009)
Tampere University of Technology, Finland

B.Sc. (Tech.), Signal Processing and Mathematics (2009)
Tampere University of Technology, Finland

Selected Publications

Äijö T, Huang Y, Mannerström H, Chavez L, Tsagaratou A, Rao A, & Lähdesmäki H. A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways. Genome Biology 2016;17(1):1-22.

Äijö T, Butty V, Chen Z, Salo, V., Tripathi, S., Burge, C. B., … & Lähdesmäki, H. Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation. Bioinformatics 2014;30(12):i113-i120.

Äijö T, Lähdesmäki H. Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 2009;25(22):2937-2944.

Äijö T, Edelman SM, Lönnberg T, Larjo, A., Kallionpää, H., Tuomela, S., … & Lähdesmäki, H. An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation. BMC Genomics 2012;13:572.

Martinez GJ, Pereira RM, Äijö T, Kim, E. Y., Marangoni, F., Pipkin, M. E., … & Rao, A. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 2015;42(2):265-278.

Ko M, An J, Bandukwala HS, Chavez, L., Äijö, T., Pastor, W. A., … & Rao, A. Modulation of TET2 expression and 5-methylcytosine oxidation by the CXXC domain protein IDAX. Nature 2013;497(7447):122-126.