# Algorithms in Molecular Biology learning objective matrix

Principal theme | Prerequisite knowledge | Approaches the learning objectives | Reaches the learning objectives | Deepens the learning objectives |
---|---|---|---|---|

Fragment assembly | Basic knowledge of data structures and graphs [e.g. from courses Tietorakenteet ja algoritmit, Algorithms for Bioinformatics] | Can give an overview of different core problems of fragment assembly, like read error correction, contig assembly, scaffolding, and gap filling | Can explain solutions / complexity of several core problems of fragment assembly, like the ones listed in the previous column |
Is able to program a competitive fragment assembly tool for bacterial genomes Understands how de Bruijn graphs and overlap graphs can be represented space-efficiently Is able to design a parallel fragment assemly too that scales to the biggest plant genomes |

Transcriptomics and metagenomics |
Basic knowledge of graphs and problem reductions [e.g. from courses Design and Analysis of Algorithms, Algorithms for Bioinformatics] |
Can identify different variants of the problems, with genomes known/unknown, short/ long reads, and sequence estimation / differential expression as the goal |
Can describe reductions from several transcriptomics problems to problems in graphs, like the ones listed in the previous column Can explain how splicing graphs and subpath constraints are derived using read alignments and co-linear chaining |
Is able to program a competitive tool for transcriptomics data analysis Understands space-efficient string kernel algorithms that enable parallel metagenomics data analysis on terabytes of data Is able to apply different transcriptomics and metagenomics tools on real data and argue about the biological interpretation of the results |

Evolution-related algorithmics | Basic knowledge of problem reductions, and of sequence evolution [biology studies or related bioinformatics courses] | Can explain what evolution-related problems the formulations like haplotype assembly, motif discovery, permutation patterns, perfect phylogeny, and genome rearrangements try to solve | Can explain solutions / complexity of several evolution-related problems, like the ones listed in the previous column |
Is able to prepare simulated data to test the evolution-related algorithms Is able to apply evolution-related algorithms on real data and argue about the biological interpretation of the results |