We are delighted to present a ring lecture and seminar introducing students to Modern Bioinformatics.

Bioinformatics is a particularly heterogeneous discipline. Rather than only presenting basic material in talks, the focus in this course is on different lecturers showcasing exciting areas of research, modern methods, and current challenges in the field. An aim is to share our excitement with students by providing insight into some of the most interesting and relevant research challenges of these times. The course also offers an invaluable opportunity for meeting several of the key Bioinformatics group leaders in Vienna, and to learn about institutes offering research opportunities towards an M. Sc. thesis in Bioinformatics.

Guest lecturers will introduce the areas of their respective research interests. Each lecture will conclude with a recommendation of scientific papers for further reading and discussion in the seminar.

After the initial lecture presentations, a selection of papers and seminar days for presentations will be provided online (below, updates to be announced during the lectures). You need to select your paper through an online system and provide a critical discussion in the January seminar (instructions will be announced in the lectures and will appear below). Plan for 10-15 minutes of presentation and 5 minutes for discussion. When you are not presenting yourself, you are expected to actively participate in the discussion.

Suggested papers and complementary materials

If you have any difficulties obtaining a copy of the manuscripts from the journal web-page or the below links, please contact us.

David Kreil – Quantitative and functional genomics

1. An unbiased comparison of hybridization and sequencing based platforms for expression profiling that can discriminate alternative transcripts and spliceforms from the US National Institutes of Health in Bethesda
   Raghavachari et al. (2012) A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease. BMC Medical Genomics, 5, 28.
   For additional background, see our recent paper in Nature Biotechnology, A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.
2. An integrated (and controversial?) analysis combining measurements across multiple species from the lab of Ziv Bar-Joseph at Carnegie Mellon University
   Lu et al. (2007) Combined analysis reveals a core set of cycling genes. Genome Biology 8, R146.

If you are interested in in learning about our research interests, please browse our web-site or look at a compilation of typical research projects where you could get involved (contact us for updates).
If you want to take part in an exciting international data analysis challenge, have a look at CAMDA and sign up to the announcements mailing list and check out the contest data sets. Since 2017 CAMDA has run as a full regular conference track of ISMB, the preeminent bioinformatics conference worldwide.

Peter Sykacek – Probabilistic Methods in Bioinformatics

1. R. Verhaak et al. (2010) An integrated genomic analysis identifies subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1. Cancer Cell 17(1): 98.
2. M. Hawrylycz et al. (2012) An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391-399
3. The Cancer Genome Atlas Network (2016) Genomic Classification of Cutaneous Melanoma. The Cancer Genome Atlas Network. Cell 161, 1681-96.

Lecture notes (last updated 2016)

Thomas Rattei – Microbial Metagenomics

1. N. Mollentze, S. A. Babayan, and D. G. Streicker (2021) Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biology 19, e3001390.

Lecture notes (last updated 2021)

Christoph Flamm – Metabolic Network Analysis

1. A. I. Hanopolskyi, V. A. Smaliak, A. I. Novichkov, and S. N. Semenov (2020) Autocatalysis: Kinetics, Mechanisms and Design. Chem Systems Chem 2, e2000026.
2. A. Blokhuis, D. Lacoste, and P. Nghe (2020) Universal motifs and the deversity of autocatalytic systems. PNAS 117, 25230–25236.
3. J. L. Andersen, C. Flamm, D. Merkle, and P. F. Stadler (2020) Defining Autocatalysis in Chemical Reaction Networks. J Sys Chem 8 121&ndash133 (preprint)
4. U. Barenholz, D. Davidi, E. Reznik, Y. Bar-On, N. Antonovsky, E. Noor, and R. Milo (2017) Design principles of autocatalytic cycles constrain enzyme kinetics and force low substrate saturation at fluxbranch points. eLife 6, e20667.
5. S. N. Semenov, L. J. Kraft, A. Ainla, M. Zhao, M. Baghbanzadeh, V. E. Campbell, K. Kang, J. M. Fox, and G. M. Whitesides (2016) Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions. Nature 537, 656–660.
6. A. J. Bissette and S. P. Fletcher (2013) Mechanisms of Autocatalysis. Angew Chem Int Ed 52, 12800–12826.

Lecture notes (last updated 2020)

Chris Oostenbrink – Molecular dynamics simulations

1. Zuzana Jandova, Samuel C Gill, Nathan M Lim, David L Mobley, and Chris Oostenbrink (2019) Binding Modes and Metabolism of Caffeine. Chem. Res. Toxicol. 32, 1374–1383.
2. Anthony J Clark et al. (2017) Free Energy Perturbation Calculation ofRelative Binding Free Energy betweenBroadly Neutralizing Antibodies and thegp120 Glycoprotein of HIV-1. J. Mol. Biol. 429, 930–947.

Lecture notes (last updated 2019)

Heiko Schmidt – A Short Introduction To Likelihood in Phylogenetics

1. Ziheng Yang and Bruce Rannala (2012) Molecular phylogenetics: principles and practice. Nature Reviews Genetics 13, 303-314.
2. Michael J. Sanderson and H. Bradley Shaffer (2002) Troubleshooting molecular phylogenetic analyses. Annu. Rev. Ecol. Syst. 33, 49–72.

Lecture notes (last updated 2020)

Friedrich Leisch – Clustering, Mixtures and Reproducability of Results

Study

Torsten Hothorn and Friedrich Leisch (2011) Case studies in reproducibility. Brief Bioinform 12 (3), 288-300.

and select the results of one of the below papers for reanalysis:

1. Moyses Nascimento et al. (2012) Bayesian model-based clustering of temporal gene expression using autoregressive panel data approach. Bioinformatics. For R-code of the analysis see the link in the Abstract.
   or:
2. Riccardo De Bin and Davide Risso (2011) A novel approach to the clustering of microarray data via nonparametric density estimation. BMC Bioinformatics.

Lecture notes (last updated 2015)

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