Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
The course provides you with an introduction to computational methods used in sequence and genome analysis. After the course you can analyze and understand real-life genomic data sets encountered in computational and biomedical research groups and in industry. Specifically, you can align genome sequences, identify genes and conserved regions in the genomes, use hidden Markov models for segmentation of genomes, build phylogenetic trees to estimate evolutionary relationships, and recognize and explain the meaning of different kinds of variation observed in real-life genomic data sets.
Schedule: 07.09.2020 - 20.10.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Pekka Marttinen
Teacher in charge (applies in this implementation): Pekka Marttinen
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The course comprises a brief introduction to genes, genomes, and related biological concepts, and covers basic algorithms and models to analyse biological sequences and genomic data sets, including techniques for gene finding, sequence alignment, permutation sampling, hidden Markov models, and the neighbor joining algorithm.
Assessment Methods and Criteria
Examination and exercises.
16 + 18 (4 + 2)
Nello Cristianini and Matthew Hahn: Introduction to Computational Genomics: A Case Studies Approach. Cambridge University Press, 2007.
Substitutes for Courses
Replaces former course CS-E5860 / T-61.5120 Computational Genomics.
Required: Basic courses in mathematics and computer science. In particular, at least one course in programming and one course covering the basics of probability and statistics.