With the advent of high-throughput genome sequencing, bioinformatics training has become essential for research in evolutionary biology and related fields. However, individual research groups are often not in the position to teach students about the most up-to-date methodology in the field. To fill this gap, extended bioinformatics courses have been developed by various institutions and provide intense training over the course of two or more weeks. Here, we describe our experience with the organization of a course in one of the longest-running extended bioinformatics series of workshops, the Evomics Workshop on Population and Speciation Genomics that takes place biennially in the UNESCO world heritage town of Český Krumlov, Czech Republic. We list the key ingredients that make this workshop successful in our view, explain the routine for workshop organization that we have optimized over the years, and describe the most important lessons that we have learned from it. We report the results of a survey conducted among past workshop participants that quantifies measures of effective teaching and provide examples of how the workshop setting has led to the cross-fertilisation of ideas and ultimately scientific progress. We expect that our account may be useful for other groups aiming to set up their own extended bioinformatics courses.

Department of Biology University of Oxford 11a Mansfield Road Oxford OX1 3SZ UK

Department of Life and Environmental Sciences Marche Polytechnic University Via Brecce Bianche 60131 Ancona Italy

Department of Pathology and Immunology Washington University School of Medicine 660 S Euclid Ave St Louis MO 63110 1010 USA

Faculty of Science University of South Bohemia Branis̆ovská 1760 37005 Ceske Budejovice Czech Republic

Infocentrum Český Krumlov náměstí Svornosti 2 38101 Český Krumlov Czech Republic

Institute of Ecology and Evolution University of Bern Baltzerstrasse 6 3012 Bern Switzerland

Institute of Parasitology Biology Centre CAS Branis̆ovská 31 37005 Ceske Budejovice Czech Republic

Natural History Museum Unversity of Oslo Sars' gate 1 0562 Oslo Norway

Research Unit for Evolutionary Immunogenomics Department of Biology University of Hamburg Martin Luther King Platz 3 20146 Hamburg Germany

Zoological Institute Department of Environmental Sciences University of Basel Vesalgasse 1 4051 Basel Switzerland

Zobrazit více v PubMed

Campbell CE, Nehm RH. A critical analysis of assessment quality in genomics and bioinformatics education research. CBE—Life Sci Educ. 2013;12(3):530–541. doi: 10.1187/cbe.12-06-0073. PubMed DOI PMC

Excoffier L, Dupanloup I, Huerta-Sánchez E, Sousa VC, Foll M. Robust demographic inference from genomic and SNP data. PLOS Genet. 2013;9(10):1003905. doi: 10.1371/journal.pgen.1003905. PubMed DOI PMC

Faria R, Triant D, Perdomo-Sabogal A, Overduin B, Bleidorn C, Santana CIB, Langenberger D, Dall’Olio GM, Indrischek H, Aerts J, Engelhardt J, Engelken J, Liebal K, Fasold M, Robb S, Grath S, Kolora SRR, Carvalho T, Salzburger W, Jovanovic V, Nowick K. Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses. Evol Educ Outreach. 2018;11:8. doi: 10.1186/s12052-018-0080-z. DOI

Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLOS Genet. 2009;5(10):1000695. doi: 10.1371/journal.pgen.1000695. PubMed DOI PMC

Harris K, Pritchard JK. Rapid evolution of the human mutation spectrum. eLIFE. 2017;6:24284. doi: 10.7554/eLife.24284. PubMed DOI PMC

Heled J, Drummond AJ. Bayesian inference of population size history from multiple loci. BMC Evol Biol. 2008;8(1):289. doi: 10.1186/1471-2148-8-289. PubMed DOI PMC

Kelleher J, Wong Y, Wohns AW, Fadil C, Albers PK, McVean G. Inferring whole-genome histories in large population datasets. Nat Genet. 2019;51:1330–1338. doi: 10.1038/s41588-019-0483-y. PubMed DOI PMC

Lawson DJ, Hellenthal G, Myers S, Falush D. Inference of population structure using dense haplotype data. PLOS Genet. 2012;8(1):1002453. doi: 10.1371/journal.pgen.1002453. PubMed DOI PMC

Li H, Durbin R. Inference of human population history from individual whole-genome sequences. Nature. 2011;475(7357):493–496. doi: 10.1038/nature10231. PubMed DOI PMC

Malinsky M, Trucchi E, Lawson DJ, Falush D. RADpainter and fineRADstructure: population inference from RADseq data. Mol Biol Evol. 2018;35(5):1284–1290. doi: 10.1093/molbev/msy023. PubMed DOI PMC

Pop M, Salzberg SL. Bioinformatics challenges of new sequencing technology. Trends Genet. 2008;24(3):142–149. doi: 10.1016/j.tig.2007.12.006. PubMed DOI PMC

Speidel L, Forest M, Shi S, Myers SR. A method for genome-wide genealogy estimation for thousands of samples. Nat Genet. 2019;51:1321–1329. doi: 10.1038/s41588-019-0484-x. PubMed DOI PMC