Fig 1.
Graphical representation of RAMBO-K’s workflow.
Reads are simulated from the reference genomes and used to train a foreground and background Markov chain. The simulated sequences and a subset of the real reads are assigned based on these matrices and a preview of the results is presented to the user. If this preview proves satisfactory, the same parameters are used to assign all reads.
Fig 2.
Example of the graphical output of RAMBO-K for a dataset containing human and orthopoxvirus sequences.
The score distribution of both simulated and real reads is displayed for two different k-mer lengths (left: 4, right: 10), allowing the user to choose the best k-mer length and cutoff. In this case, a cutoff around -100 at a k-mer length of 10 would allow a clean separation of foreground and background reads, as visualized by the clearly separated peaks. The estimated abundance of foreground and background reads in the dataset is displayed in the figure title.
Table 1.
Benchmark results.