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
-
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.
-
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
-
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.
- M. Hawrylycz et al. (2012)
An anatomically comprehensive atlas of the adult human brain transcriptome.
Nature 489, 391-399
-
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
-
E. Pasolli et al.
(2019) Extensive
Unexplored Human Microbiome Diversity Revealed by Over
150,000 Genomes from Metagenomes Spanning Age, Geography,
and Lifestyle.
Cell 176, 649-662.
- R. C. Shean, N. Makhsous, G. D. Stoddard, M. J. Lin,
and A. L. Greninger (2019)
VAPiD:
a lightweight cross-platform viral annotation pipeline and
identification tool to facilitate virus genome submissions
to NCBI GenBank.
BMC Bioinformatics 20, 48.
Lecture
notes (last updated 2019)
Christoph Flamm – Metabolic Network Analysis
-
A. I. Hanopolskyi, V. A. Smaliak, A. I. Novichkov, and S. N. Semenov
(2020)
Autocatalysis: Kinetics, Mechanisms and
Design.
Chem Systems Chem 2, e2000026.
- A. Blokhuis, D. Lacoste, and P. Nghe (2020)
Universal motifs and
the deversity of autocatalytic
systems. PNAS 117, 25230–25236.
-
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)
-
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.
-
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.
-
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
- 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.
- 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
-
Ziheng Yang and Bruce Rannala (2012)
Molecular phylogenetics: principles and practice.
Nature Reviews Genetics 13, 303-314.
- 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
and select the results of one of the below papers for reanalysis:
-
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:
-
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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