Biological Sequence Analysis learning objective matrix

Principal theme Prerequisite knowledge Approaches the learning objectives Reaches the learning objectives Deepens the learning objectives
Alignments

Some experience in dynamic programming [e.g. from one of the courses: Algorithms for Bioinformatics, String Processing Algorithms, or Design and Analysis of Algorithms]

Basic probability calculus[ e.g. from course Johdatus todennäköisyyslaskentaan]

Basic knowledge of data structures and graphs [e.g. from course Tietorakenteet ja algoritmit]

Can explain the difference between variants of alignment formulations such as global, local, semi-local, overlap, multiple, progressive, DAG, jumping etc. Can derive dynamic programming formulations for simple variants of the alignment formulations covered during the course, and optimize them for speed and space usage

Can apply sparse dynamic programming, invariant technique, space optimizations and other dynamic programming techniques from the course to new problems in bioinformatics

Can apply a suitable alignment formulation in the analysis of real data

Hidden Markov models Some experience in dynamic programming, basic probability calculus Can program the given simple example HMM for gene finding Can derive new HMM formulations for related problems like pair HMMs and  profile HMMs Can design a genome-scale HMM approach to annotate a new genome based on statistical features, biological knowlege, transcriptomics read data, etc.
High-throughput sequencing data analysis Basic knowlege of data structures Can give an overview of how variant calling can be done based on read alignments Can explain how read alignment and simple genome analysis tasks such as maximal repeat finding can be conducted time- and space-efficiently

Understands the compressed data structures behind space-efficient read alignment on pan-genomic data

Can program a competitive tool for structural variant detection

 

08.12.2014 - 11:45 Veli Mäkinen
01.12.2014 - 11:59 Veli Mäkinen