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| Detailed information |
| Original study plan |
Master's programme Artificial Intelligence 2025W |
| Learning Outcomes |
Competences |
| Students have a strong understanding of sequence analysis and phylogenetics. They know how to compare, align, and analyze biological sequences using computational techniques. They are able to compute evolutionary relationships and construct phylogenetic trees, and to apply statistical methods to interpret sequence alignments and evolutionary distances.
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| Skills |
Knowledge |
| • Performing Sequence Comparisons and Alignments (k5)
Students can apply simple scoring schemes to compare biological sequences and conduct pairwise sequence alignments, using computational tools to identify similarities and differences.
• Conducting Multiple Sequence Alignments (k5)
Students are able to perform multiple sequence alignments to analyze sets of biological sequences, identifying conserved regions and evolutionary relationships across species or genes.
• Applying Statistical Methods to Sequence Alignments (k5)
Students can use statistical techniques to assess the significance of sequence alignments, evaluating the likelihood that observed similarities are due to evolutionary relationships rather than chance.
• Computing Evolutionary Distances (k5)
Students are capable of computing evolutionary distances between sequences using established methods, enabling them to quantify genetic divergence over time.
• Constructing and Interpreting Phylogenetic Trees (k5)
Students can apply computational methods to construct phylogenetic trees, representing evolutionary relationships between species or genes, and interpreting the resulting tree structures to infer common ancestry.
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Students know sequence analysis techniques, including pairwise and multiple sequence alignments, and the statistical analysis of alignment results. They have learned how to compute evolutionary distances and construct phylogenetic trees, gaining insights into the evolutionary relationships between biological sequences and how these methods are applied in molecular biology and genetics.
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| Criteria for evaluation |
Written exam
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| Methods |
Slide presentations complemented by examples presented on the blackboard
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| Language |
English |
| Study material |
- D. W. Mount, Bioinformatics: Sequences and Genome analysis, CSHL Press, 2001
- D. Gusfield, Algorithms on strings, trees and sequences: computer science and computational biology, Cambridge Univ. Press, 1999
- R. Durbin, S. Eddy, A. Krogh, G. Mitchison, Biological sequence analysis, Cambridge Univ. Press, 1998
- M. Waterman, Introduction to Computational Biology, Chapmann & Hall, 1995
- Setubal and Meidanis, Introduction to Computational Molecualar Biology, PWS Publishing, 1997
- Pevzner, Computational Molecular Biology, MIT Press, 2000
- J. Felsenstein: Inferring phylogenies, Sinauer, 2004
- W. Ewens, G. Grant, Statistical Methods in Bioinformatics, Springer, 2001
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| Changing subject? |
No |
| Further information |
Until term 2025S known as: 663INFOSAPV22 VL Sequence Analysis and Phylogenetics
until term 2022S known as: 675GTSBSAPV16 VL Sequence Analysis and Phylogenetics
until term 2016S known as: 875BIN1SAPV12 VL Sequence Analysis and Phylogenetics
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
663INFOSAPV22: VL Sequence Analysis and Phylogenetics (2022W-2025S)
675GTSBSAPV16: VL Sequence Analysis and Phylogenetics (2016W-2022S)
875BIN1SAPV12: VL Sequence Analysis and Phylogenetics (2012W-2016S)
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