Introduction to Bioinformatics 1000-711bBI1

Laboratory classes:
● Introduction to bioinformatics and working in a UNIX/Linux environment. File and directory
organization, data compression, and backup strategies.
● Basic command-line tools for processing text data in Linux (regular expressions, pipelines,
grep, sed, awk).
● Automation of bioinformatics analyses using the command line.
● Major biological databases used in bioinformatics: data retrieval and database searching.
● Principles of metadata creation and research data management. FAIR data principles.
● Pairwise sequence alignment: local versus global approaches.
● Searching for similar sequences in biological databases. BLAST from the command line
(local and remote databases), output formats, and the BLAST web interface.
● Multiple sequence alignment (MSA): sequence selection criteria and overview of alignment
algorithms.
● Sequence profiles and iterative homology search methods (PSI-BLAST). Conserved Domain
Database (CDD).
● Analysis of biological sequences using the EMBOSS package. Biological data formats.
● Hidden Markov Models (HMMs) in biological sequence analysis. Protein motifs and domains.
● Introduction to biological data analysis in Python using the Polars library.
Lectures:
● Biological and evolutionary foundations of bioinformatics analyses.
● Biological sequence analysis and alignment, including multiple sequence alignment and
Markov models.

● DNA sequencing technologies and fundamentals of sequencing data quality control.
● Genome assembly methods and reconstruction of genomic sequences.
● Identification of coding and non-coding DNA sequences using ab initio and homology-based
approaches.
● Genome annotation and gene prediction.
● Bioinformatics data formats used in genomics and transcriptomics (including FASTA,
GFF/GTF, BED, and VCF).
● Theoretical foundations of transcriptomics and RNA-seq data analysis.
● Comparative and functional genomics at the whole-genome level.
● Application of biological databases and bioinformatics tools in the analysis of omics datasets.