Applied bioinformatics

elective courses

The classes are designed to familiarize students with the basics of programming (bash, R) and bioinformatics techniques for processing biological data at the level of individual sequences, as well as genomes and transcriptomes.

The classes will focus on working with sequences - from raw data, through analysis of their quality, assembly of genomes and transcriptomes, and functional annotation, to more advanced analyses, enabling answering biological questions. The curriculum will prepare students to work in the command line, use scripts to facilitate the work with biological data. Students will also learn about selected internet resources used for more in-depth data analysis, including functional annotation of biological sequences. We will also present more advanced analyses, such as gene expression analysis, metagenomics and amplicon analysis, elements of comparative genomics, and phylogenetic analyses and molecular modelling.

Classroom

Advances in bioinformatics and high-throughput sequencing technologies have significantly impacted the development of biology and biotechnology. The Applied bioinformatics course aims to prepare students to work with high-throughput sequencing data and use bioinformatics tools in their work. Students will learn the basics of programming and bioinformatics techniques used in sequence analysis, from raw data processing, through the assembly of genomes and transcriptomes, phylogenetic and metagenomic analyses, to comparative genomics. These classes complement the Bioinformatics course, with content related to the analysis of high-throughput sequencing data and analyses of genomes and transcriptomes.

A short theoretical introduction will precede each class, but most of the time will be devoted to independent work and problem solving under the tutors' supervision.

The topics discussed during the classes will include:

1. Introduction to programming: bash and R.

2. Introduction to next-generation sequencing (NGS) data processing: introduction to the basics of sequencing methodology and Illumina data handling, fastq format, raw data quality analysis, preparation of data for assembly and further analysis, genomes and transcriptomes assembly, assembly quality analysis, coverage assessment (bam and sam formats), contamination and completeness estimation.

3. Genome analysis. Whole-genome analysis: whole-genome alignment, detection of large rearrangements. Basic annotation methods: homology-based gene prediction and functional annotation (bed and gff data formats). Basic categories describing the characteristics and functions of genes and proteins (e.g. GO, COG, KEGG categories, EC numbers). Visualization of genomic data.

4. Introduction to transcriptome analysis, processing of raw RNA-seq data from the Illumina platform, visualization and detection of differences in gene expression, preliminary analysis of the obtained data.

5. Introduction to phylogenetics and molecular modelling, sequence alignment, protein structures modelling and phylogenetic trees reconstruction based on sequence and structural data.

6. Introduction to amplicon analysis and metagenomics.

NOTE: each topic will be covered in more than one class.