By the end of the course students will be able to describe representation of biological sequences, query and retrieve data from biological databases, apply and classify algorithms and tools for nucleotide and protein sequence alignment, genome and transcriptome assembly, and gene finding. Students will also learn how to apply methods for estimating molecular sequence evolution and how to build a phylogenetic tree.
- Explain some of the issues and challenges in contemporary Bioinformatics
- Evaluate bioinformatics algortihms
- Design algorithms solving sequence assembly problems
- compare and evalute methods for sequence alignment
- Design algorithms for building phylogenetics trees
- Analyze data from biological databases
Forms of Teaching
Lectures will involve discussion and practical work.Exams
The mid-term examination will be held in Week 7, and the final exam in Week 15.Other Forms of Group and Self Study
Project or seminar work in groups up to 6 students
|Type||Threshold||Percent of Grade||Comment:||Percent of Grade|
|Seminar/Project||0 %||40 %||0 %||40 %|
|Mid Term Exam: Written||0 %||25 %||0 %|
|Final Exam: Written||0 %||35 %|
|Exam: Written||0 %||60 %|
Week by Week Schedule
- What is bioinformatics? Introduction to molecular biology: DNA, RNA, genome, protein sequences. Biological databases. Data formats of biological sequences.
- Introduction to genome sequencing. Human genome and other genome projects. High through-put sequencing and genome and transcriptome assembly. Assembly evaluation.
- Sequence homology and sequence similarity. Sequence alignment: local, global. Dynamic programming algorithms for alignment.
- Indexed based alignment. BLAST and its variants.
- Suffix and prefix tries. Suffix tree. Suffix array.
- High through-put sequencing hashing methods. Pair-wise and multiple sequence alignment.
- The Burrows-Wheeler transform. Self-indexes. Hash-based methods vs. BWT-based methods.
- Miterm exam
- Phylogeny. Evolutionary models. Phylogenetic tree construction.
- Gene prediction.
- Data storage structures. Data compression for high through-put sequencing.
- Sequence assembly. Mapping and de novo assembling. Mapping algorithms. Repeats and masking.
- De novo assembly. Hamiltonian and Eulerian paths in graph.
- Overlap-layout-consensus approach. De Bruijn graph for assembly. Complexities in graphs.
- Final exam