Block 3 - COOI: Recent ploidy changes in the Saccharomycotina (part 2)

Overview

Teaching: 0 min
Exercises: 60 min

Questions

• How can you determine changes in ploidy using next-generation sequencing data?

Objectives

• Generate k-mer profiles from short-read data

• Analyze and interprete k-mer profiles to estimate genome size, heterozygocity, repeat content

• Analyze and interprete k-mer profiles to estimate ploidies

• Generate allele frequency plots from short-read data

• Analyze and interprete allele frequencies to estimate ploidies

2. Identify ploidy variation using k-mer frequencies

Signatures of recent ploidy change can be inferred from next-generation sequencing data by examining k-mer distributions. When interpreting k-mer profiles directly, one needs to have a intuition about the ploidy. For example, a third peak in a k-mer profile could point to repeats as well to a triploid species. Computational approaches such as smudgeplot (see lecture) can estimate the ploidy and genome structure of a genome by analyzing heterozygous k-mer pairs that can be directly found in the sequencing data.

We will first analyse the smudgeplot based on the k-mer data generared for S. cerevisiae strains CBS7837. To be able to analyze heterozygous k-mers, we first need to extract these heterozygous k-mers from the jellyfish count data we have generated in the previous episode.

We can extract k-mers using jellyfish dump and we need to provide both upper and lower count cutoff to excude low and high frequency kmers. The resulting k-mers can be directly provided to smudgeplot.py hetkmers to compute the set of heterozygous k-mer pairs.

However, we first need to change to a smudgeplot environment since it runs in a different python version. This is done with the smudgeplot command. You can at any moment return to your default environment using python2.7

$ smudgeplot
$ jellyfish dump -c -L 10 -U 1000 ScereCBS7837.jf | smudgeplot.py hetkmers -o ScereCBS7837_kmer_pairs

Exercise

Now that we generated the heterozygous k-mers pairs (*_coverages.tsv), we can now use smudgeplot plot to visualize the data.

• What does the x- and y-axis display? What is shown by the color gradient?
• What is the suggested ploidy based on the smudgeplot results?

Solution

smudgeplot.py plot ScereCBS7837_kmer_pairs_coverages.tsv -o ScereCBS7837

The x-axis shows the minor k-mer coverage (ratio of minor allele frequency over the total allele count; for diplaid one would expect 0.5 : 0.5). The y-axis is the k-mer coverage (based on 1n (monoploid) coverage, the coverage is estimated based on the data). The color gradient shows the number of k-mer pairs with this particular minor k-mer coverage (x-axis) and k-coverage (y-axis).

The suggested ploidy is diploid with a normalized minor kmer coverage of 0.5 and peak of data around a 2n coverage based on a 1n of 41.

We can now generate similar smudgeplots for another S. cerevisiae strain and analyze its ploidy. Localize the short-read sequencing data from S. cerevisiae strains CBS2919 at the data storeage folder. As explained in the previous episodes, use jellyfish to generate the k-mer counts. Then use a. genomescope to determine the genome size and visualize the k-mer distribution and b. jellyfish dump together with smudgeplot hetkmers to extract k-mer pairs, and visualize these with smudgeplot plot.

Exercise

What is the suggested ploidy based on the smudgeplot result?

Solution

The suggested ploidy based on smudgeplot is triploid where the minor allele frequency is occuring at a 1/3 ratio and the total coverage of the k-mer pair is at 3n based on a 1n coverage of 27. In the GenomeScope analysis, the first peak of the k-mer distribution is mistaken for an error peak and thus the peak detection and genome size estimate is likely off. Instead of suggesting a 3n it seems like a diploid strain without any heterozygous peak. This example highlights shows that knowledge of ploidy is important prior analysing k-mer distributions.

Key Points

• You can work with next-generation sequencing data

• You can detect (recent) ploidy changes in fungi using next-generation sequencing data