Genomics and transcriptomics technologies 1000-718TGT

Both the lectures and the labs are divided into three thematic blocks: (1) genomics, (2) transcriptomics and (3) metagenomics and metatranscriptomics. The course covers the planning and execution of experiments as well as selected methods of data analysis and their application.

I Genomics
1. De novo sequencing of genomes and re-sequencing - methodological requirements (e.g. population or individual species), objectives, sequencing techniques and assembly methods.
2. Assessment of the quality of assembly - technical and biological, assessment of the completeness of the genome, possibility of using data of different quality.
3. Annotation of genomes - prediction of genes and functions. Prediction of non-coding elements, gene structure and functional annotation.
4. Sequencing of single cell genomes in biomedical and environmental research.
5. Selected topics on: large-scale analyses in chromatin structure research, epigenome analysis or population genomics.

II Transcriptomics
1. RNA-seq experiments (analysis of mRNA, total RNA, miRNA). Reference-genome vs. de novo assembly. Analysis of SS (strand-specific) transcriptomes. Sequencing of long non-coding RNA and direct RNA sequencing.
2. Transcriptome assembly and transcriptome annotation, assessment of assembly quality, degree of contamination and completeness. Functional annotation of the transcriptome.
3. Gene expression analysis and analysis of differentially expressed genes. The use of unique molecular markers (Unique Molecular Identifier) in the assessment of gene expression.
4. Sequencing of single cell transcriptomes in biomedical and environmental research.
5. Genome analysis and transcriptome analysis - when to choose which technique and how the results of genome and transcriptome sequencing complement each other, e.g. use of the transcriptome for annotation, use of the genome for transcriptome assembly, detection of fusion genes.

III Metagenomics and metatranscriptomics
1. Assembly of metagenomes (assembly of individual samples and assembly of whole data) and search for genes in metagenomic data. Analysing de novo assembly graphs - theory and application examples (e.g. strain deconvolution). Metagenome analysis: taxonomic annotation, automatic binning and manual processing of genomes from metagenomes.
2. Machine learning methods in metagenomics.
3 Metatranscriptome assembly and analysis of metatranscriptomes.
4. Reconstruction of metabolic networks and metabolic relations between organisms and the flux of metabolites (flux balance analysis)
5. Combining different data types, advantages and limitations (pangenome analyses, integration of amplicon and genomic data, integration of metatranscriptome and genomic data).