Content of lectures: The course is taught as 1h lecture followed by 3h practical session. At the lectures, students are explained theoretical principles and biological mechanisms. At practical sessions, students practice data analysis on topics explained at the lectures, and apply knowledge of biology explained at the lectures to data analysis. 1. RNA-seq library preparation and data analysis I. Principles of RNA-seq library preparation, bioanalyzer, Illumina sequencing, introduction to fasta/fastq and bam file formats. Data trimming, quality control. 2. RNA-seq library preparation and data analysis II. Data mapping, introduction to tool Seqmonk, gene expression quantification, visualisation of the results, gene ontology. 3. RNA-seq: suboptimal results and their troubleshooting, low-input and single cell datasets. Examples of suboptimal results, what can be done, differences in working with low input and single cell datasets vs classical datasets. 4. De novo transcriptome assembly and work with non-model species. Principles of de novo transcriptome assembly, limitations and possibilities of work with non-model species with and without assembled genomes and annotated transcriptomes. 5. Short RNA-seq library preparation and data analysis Principles of short RNA-seq library preparation, miRNA data analysis. 6. DNA methylation, bisulphite-seq library preparation and data analysis I. Principles of bisulphite-seq library preparation, data processing and mapping. 7. DNA methylation, bisulphite-seq library preparation and data analysis II. Analysis of mapped bisulphite-seq datasets. 8. Chromatin accessibility and ATAC-seq Principles of ATAC-seq library preparation and data analysis. 9. Chromatin immunoprecipitation, ChIP-seq library preparation and data analysis I. Principles of chromatin immunoprecipitation, library preparation, data processing and mapping. 10. Chromatin immunoprecipitation, ChIP-seq library preparation and data analysis II. Analysis of mapped data, histone modifications vs transcription factors. 11. Low input ChIP-seq, Cut&Tag and other alternative approaches and data analysis Specific characteristics of low input ChiP-seq library preparation and data analysis, principles of alternative approaches to study histone modifications and data analysis. 12. Combining transcriptome and epigenome datasets I. Example tasks combining transcriptome and epigenome datasets, practice of previously gained skills. 13. Combining transcriptome and epigenome datasets II. Example tasks combining transcriptome and epigenome datasets, practice of previously gained skills.
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