Deep sequencing of a diverse population¶

This parameter set emulates a deep sequencing run of 12 individuals with a number of different alleles per locus and an increased chance for mutation events.

(ddrage) me@machine:~$ ddrage -l 10000 -n 12 --coverage 100 --diversity 5.0 --event-probabilities 0.8 0.05 0.15

‘Budget’ sequencing run¶

These parameters emulate a cheap sequencing run with low coverage, a single end library, and 96 individuals in one file.

(ddrage) me@machine:~$ ddrage -l 10000 -n 96 --coverage 10 --combine-p7-bcs --single-end

Sequencing of a large genome with short fragments¶

This parameters create a dataset with many loci and long reads, but the ends of the reads overlap.

(ddrage) me@machine:~$ ddrage -l 300000 -n 24 -r 250 --overlap 0.3

Simulate reads from a reference genome¶

This tutorial assumes that you already have a FASTA file (here: fragments.fa) that contains generated fragments. The restriction enzymes used to create the fragments (for example by in-silico digestion) are also specified. In this case, the p5 restriction enzyme has the recognition site AATT and does not leave an overhang, and the p7 enzymes has the recognition site CCGG and leaves the overhang CGG in the reads.

Overlap is controlled by the size of the fragments in relation to the read length.

(ddrage) me@machine:~$ ddrage -l fragments.fa --p5-rec-site AATT --p5-overhang "" --p7-rec-site CCGG --p7-overhang CGG

Low quality sequencing¶

These parameters alter the coverage model to only yield values below 30 and a per base error probability of 5%, simulating a low quality dataset.

The precise shape of the expected quality distribution can be seen by entering the –BBD-alpha 8.1 –BBD-beta 1.6 parameters in the visualize_bbd script.

(ddrage) me@machine:~$ ddrage -l 10000 -n 24 --coverage 25 --BBD-alpha 8.1 --BBD-beta 1.6 --prob-seq-error 0.05