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Prof. Dr. Maria Anisimova

Prof. Dr. Maria Anisimova

Prof. Dr. Maria Anisimova

ZHAW Life Sciences und Facility Management
FS Bioinformatics
Schloss 1
8820 Wädenswil

+41 (0) 58 934 58 82

Arbeit an der ZHAW


  • Abteilungsleitung
  • Schwerpunktleitung

Tätigkeit an der ZHAW

Head of Research Center for Bioinformatics, Lecturer, SIB group leader


Lehrtätigkeit in der Weiterbildung

Bioinformatics for Beginners

Aus- und Weiterbildung

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Stochastic modeling and simulation for genomic data
Model-based classification, prediction and hypothesis testing
Algorithms for genomic sequence analysis
Bio-statistical model selection and hypothesis testing based on molecular sequences
Applied computational genomics for medicine, ecology and agriculture

Beruflicher Werdegang

From December 2021: Professor in Computational Genomics
2015 - now: Research Group leader, Swiss Institute of Bioinformatics (SIB), Switzerland
2014 - now: Research Group leader, ZHAW Wädenswil, Switzerland
2007 - 2014: Senior research fellow and lecturer, Computer Science Department, ETH Zürich, Switzerland
2005 - 2007: Postdoctoral researcher, Biology Department, University College London, UK
2003 - 2004: Postdoctoral researcher, LIRMM-CNRS, University of Montpellier II, France
1997 - 1999: Head of maths and IT, teacher, Bellerbys College London, Study Group International, UK
1993 - 1997: Assistant lecturer, School of Geometry, Nizhny Novgorod Pedagogical State University, Russia

Aus- und Fortbildung

CAS Hochschuldidaktik (2018), PHZH - Pädagogische Hochschule Zürich
PhD in Statistical Genomics (2003) University College London, UK
MRes in Modeling Biological Complexity (2000) University College London, UK
MSci in Mathematics and IT (1993) Pedagogical University of Nizhny Novgorod, Russia
Certified high school teacher (1993) Pedagogical University of Nizhny Novgorod, Russia

My research group focuses on methods for computation genomics, evolution and semantic web technologies - with applications to a variety of problems in pharmacology, medicine, forensics, ecology and agriculture. We are interested in the theoretical aspects of modeling molecular evolution as well as data-driven applications of new methodologies, particularly the computational methods to study the process of gene and genome evolution and the process of adaptive genetic change. Data from transcriptomics, proteomics, metabolomics, metagenomics and epigenomics are growing rapidly and have serious impact on genomics and our understanding of molecular cell biology and its various effects on the phenotype and disease. But the challenge of understanding the dynamics of such large-system data can only be met through an integration of organism, molecular, and mathematical disciplines. New techniques need to be developed for discovering complex patterns from multi-faced systems biology data.

Mitglied in Netzwerken



Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed
Bücher und Monographien, peer-reviewed
Buchbeiträge, peer-reviewed
Konferenzbeiträge, peer-reviewed
Weitere Publikationen
Mündliche Konferenzbeiträge und Abstracts

Publikationen vor Tätigkeit an der ZHAW

All publications can be found on Google Scholar:

*** BOOKS ****************************

Anisimova, M. (Ed.) 2012. Evolutionary Genomics: statistical and computational methods Springer (Humana Press):
vol 1: ISBN 978-1-61779-581-7
vol 2: ISBN 978-1-61779-584-8

*** BOOK CHAPTERS and REVIEWS *********

Kosiol C. and M. Anisimova 2012. Methods for detecting natural selection in protein-coding genes in "Evolutionary Genomics: statistical and computational methods" vol. 2 , ed. Anisimova M. within Meth. Mol. Biol. Series of Humana-Springer link

Anisimova, M. 2012. Parametric models of codon evolution in Codon Evolution: mechanisms and models, eds. Cannarozzi G, Schneider A., Oxford University Press

Anisimova, M. and D. Liberles 2012. Detecting and understanding natural selection, in Codon Evolution: mechanisms and models, eds. Cannarozzi G, Schneider A., Oxford University Press

Roth, A., M. Anisimova, and G.M. Cannarozzi 2012. Measuring codon usage bias, in Codon Evolution: mechanisms and models, eds. Cannarozzi G, Schneider A., Oxford University Press

Anisimova*, M., G.M. Cannarozzi* , and D.A. Liberles* 2010. Finding the balance between the mathematical and biological optima in multiple sequence alignment. Trends Evol. Biol. 2(1): e7

*** EDITORIALS ************************

Anisimova, M., D. A. Liberles, H. Philippe, J. Provan, T. Pupko, and A. von Haeseler 2013. State-of the art methodologies dictate new standards for phylogenetic analysis. BMC Evol. Biol. 13:161

Anisimova, M. 2012. Preface to “Evolutionary genomics: statistical and computational methods” (2 vols), ed. Anisimova, in Meth Mol Biol, Springer


Sidorova, J. and M. Anisimova 2014. NLP-inspired structural pattern recognition in chemical application. Pattern Recognition Letters 45:11–16

Gil*, M., M.S. Zanetti*, S. Zoller, and M Anisimova 2013. CodonPhyML: Fast maximum likelihood phylogeny estimation under codon substitution models. Mol Biol Evol 30(6):1270-80

Szalkowski, A., and M. Anisimova 2013. Graph-based modeling of tandem repeats improves global multiple sequence alignment. Nucl Acids Res 41(17):e162

Peeters, N., S. Carrère, M. Anisimova, L. Plener, A.C. Cazalé, S. Genin 2013. Repertoire, unified nomenclature and evolution of the Type III effector gene set in the Ralstonia solanacearum species complex. BMC Genomics 14:859

Schirrmeister, B.E., D.A. Dalquen, M. Anisimova and H.C. Bagheri 2012. Gene copy number variation and its significance in cyanobacterial phylogeny. BMC Microbiology 2:177

Schaper, E., A.V. Kajava, A. Hauser, and M. Anisimova 2012. Repeat or not repeat?—Statistical validation of tandem repeat prediction in genomic sequences Nucl. Acids Res. 40 (20): 10005-10017

Anisimova, M., M. Gil, JF. Dufayard, C. Dessimoz, O. Gascuel 2011. Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes. Syst Biol 60:685–699

Schirrmeister B. E., M. Anisimova, A. Antonelli, H. C. Bagheri 2011. Evolution of cyanobacterial morphotypes: taxa required for improved phylogenomic approaches. Communicative & Integrative Biology 4(4): 424–427

Szalkowski A., and M. Anisimova 2011. Markov models of amino acid substitution to study proteins with intrinsically disordered regions. PLoS One 6(5): e20488

Balakirev E.S., M. Anisimova, and F.J. Ayala 2011. Complex interplay of evolutionary forces in the ladybird homeobox genes of Drosophila melanogaster. PLoS One 6(7): e22613

Remigi P., M. Anisimova, A. Guidot, S. Genin and N. Peeters. 2011. Functional diversification of the GALA type-three effector family contributes to Ralstonia solanacearum adaptation on several plant hosts. New Phytologist 192(4): 976–987

Dalquen D. A, M. Anisimova, G. Gonnet, and C. Dessimoz 2011. ALF - A simulation framework for genome evolution. Mol Biol Evol 29(4): 1115-1123

Wang M, M. Kapralov, and M. Anisimova 2011. Co-evolution of amino acid residues in the key photosynthetic enzyme RuBisCo in land plants. BMC Evol Biol 11:266

Guindon, S., JF. Dufayard, V. Lefort, M. Anisimova, W. Hordijk and O. Gascuel 2010. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0 Syst. Biol. 59(3):307-321

Dimitrieva, S. and M. Anisimova 2010. PANDITplus: toward better integration of evolutionary view on molecular sequences with supplementary bioinformatics resources. Trends Evol. Biol. 2(1): e1

Anisimova, M. and C. Kosiol 2009. Investigating protein-coding sequence evolution with probabilistic codon substitution models. Mol. Biol. Evol. 26(2):255-271

Kajava, A.V., M. Anisimova, and N. Peeters 2008. Origin and evolution of GALA-LRR, a new member of the CC-LRR subfamily: from plants to bacteria? PLoS One 3(2): e1694.

Anisimova, M. and Z. Yang 2007. Multiple hypotheses testing to detect adaptive protein evolution affecting individual branches and sites. Mol. Biol. Evol. 24:1219-1228

Anisimova, M., J.P. Bielawski, K. Dunn and Z. Yang 2007. Phylogenomic analysis of natural selection pressure in Streptococcus. BMC Evol. Biol. 7:154-167

Anisimova, M. and D. A. Liberles. 2007 The quest for natural selection in the age of comparative genomics. Heredity 99:567-579

Anisimova, M. and O. Gascuel. 2006. Approximate likelihood ratio test for branches: a fast, accurate and powerful alternative. Syst. Biol. 55(4):539-552

Balakirev, E.S., M. Anisimova, and F.J. Ayala 2006. Positive and negative selection in the beta-esterase gene cluster of the Drosophila melanogaster subgroup. J. Mol. Evol. 62: 496-510

Anisimova, M. and Z. Yang 2004. Molecular evolution of HDV antigen gene: recombination or positive selection? J. Mol. Evol. 59: 815-826

Anisimova, M., R. Nielsen, and Z. Yang 2003. Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics 164: 1229-1236

Anisimova, M., J. P. Bielawski, and Z. Yang 2002. Accuracy and power of Bayes prediction of amino acid sites under positive selection. Mol. Biol. Evol. 19:950-958

Anisimova, M., J. P. Bielawski, and Z. Yang 2001. Accuracy and power of the likelihood ratio test to detect adaptive molecular evolution. Mol. Biol. Evol. 18:1585-1592

Weitere Beiträge


MSc ACLS, semester course "Mathematical modeling" (Stochastic processes)
MSc ACLS, semester course "Computational Genomics" (Track 1 and Track 2)
PhD network in Data Science, semester course "Inferential statistics for data scientists" (UZH-ETHZ-ZHAW)
PhD network in Plant Sciences, short course "Molecular Evolution and Phylogenetics"