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Analysing genome skimming data
Kamil S. Jaron edited this page Mar 22, 2024
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This section is presenting you a suite of methods developed for work with genome skimming datasets.
The theory for the five tutorials is split in two lectures. The ⚒ Phylogenetic placement of samples (Skmer) and ⚒ Estimating genomic distance (APPLES) are covered in the first lecture (and corresponding slides). The ⚒ Double phylogenetic placement of mixed samples, ⚒ Genome size estimation of skimming data, and ⚒ Contamination in skimming data are covered in the second lecture (corresponding slides).
Here are the instructions for 🖥️Installation of tools to work with skimming data.
Introduction
k-mer spectra analysis
- 📖 Introduction to K-mer spectra analysis
- 📖 Basics of genome modeling
- ⚒ manual model fitting (for better understanding of the underlying model)
- ⚒ simple diploid
- ⚒ demonstrating the effect of sequencing error rate on k-mer coverage
- 📖 Common difficulties in characterisation of diploid genomes using k mer spectra analysis
- ⚒ low coverage (pitfall) - to be merged
- ⚒ very homozygous diploid
- ⚒ highly heterozygous diploid
- ⚒ Genome size of a repetitive genome (pitfall)
- ⚒ Wrong ploidy (pitfall)
- 📖 Characterization of polyploid genomes using k mer spectra analysis
- ⚒ Autotetraploid
- ⚒ Allotetraploid
- ⚒ Estimating ploidy (smudgeplot)
- 📖 Genome modeling as a quality control
- ⚒ Contamination (pitfall)
- ⚒ k-mers in an assembly (Mercury/KAT)
- 📖 Analysing genome skimming data
Separation of chromosomes
- 📖Separate sub-genomes of an allopolyploid
- 📖Separating chromosomes by comparison of sequencing libraries
Species assignment using short k-mers
Others