Bioinformatics 2

Teacher: dr. Pavle Goldstein, assistant professor
Semester: second
ECTS: 6
Required course

To further develop understanding – both practical and theoretical – of main bioinformatics methods, primarily sequence analysis,
To introduce structure analysis and related topics; and to introduce various topics in machine learning in bioinformatics, through case studies and interaction with life-sciences researchers using bioinformatics methods.

After the completion of the course, it is expected that a student:

will be able to apply mathematical results to problems in biological sequence analysis,
will be familiar with techniques for protein analysis and classification,
will be familiar with protein structure analysis techniques,
will be familiar with main phylogenetic analysis techniques,
will be familiar with the main machine learning methods in bioinformatics.

modelling evolution of biological sequences – multiple sequence alignment; hidden Markov models for multiple sequence alignment and associated topics,
protein structure analysis: protein fold, secondary structure elements, distance matrices; structure alignment,
phylogenetic analysis – UPGMA and neighbour-joining algorithms,
clustering and classification: k-means clustering, how many k in k-means; elements of machine learning for biological sequence classification – support vector machines.

Course objectives
1. To further develop understanding – both practical and theoretical – of main bioinformatics methods, primarily sequence analysis,
2. To introduce structure analysis and related topics; and to introduce various topics in machine learning in bioinformatics, through case studies and interaction with life-sciences researchers using bioinformatics methods.
Expected learning outcomes
After the completion of the course, it is expected that a student:
1. will be able to apply mathematical results to problems in biological sequence analysis,
2. will be familiar with techniques for protein analysis and classification,
3. will be familiar with protein structure analysis techniques,
4. will be familiar with main phylogenetic analysis techniques,
5. will be familiar with the main machine learning methods in bioinformatics.
Course content
1. modelling evolution of biological sequences – multiple sequence alignment; hidden Markov models for multiple sequence alignment and associated topics,
2. protein structure analysis: protein fold, secondary structure elements, distance matrices; structure alignment,
3. phylogenetic analysis – UPGMA and neighbour-joining algorithms,
4. clustering and classification: k-means clustering, how many k in k-means; elements of machine learning for biological sequence classification – support vector machines.