Debby is a demonstration of the succinct de Bruijn Graph implementation, in Python. Details can be found on my blog post.

As it is only a demonstration, it doesn't use any efficient data structures for compression or rank/select, and some operations could be shaved off. Perhaps one day Debby will grow up, and I will write tests for, refactor, and optimize her.

1. First you need to stream (k+1)-mers (that is, a kmer and an edge label) into format.sh. So, if you want to make a de Bruijn graph with 3-mers, you would need to break your reads into 4-mers. Also, they reads must be padded with $ signs (see the blog for why). I'll leave this up to you, but for testing I have included the file sample-edges.

2. These need to be sorted, and filtered for unique nodes.

3. Finally, run reduce.py on the output to format the graph correctly (in plaintext) and output to a file. The format is that the last line is the k value, the second last line is the F-array (how we represent the node labels), and the previous lines represent a (last-flag, edge-label, shared-outgoing-node-flag) tuple, for each edge.

Here is how to do so with the supplied sample-edges file:

$ cat sample-edges | ./format.sh | sort -u | ./reduce.py > my-graph

The reason I split it into stages (and made my Python code operate entirely on streams in a map-reduce fashion) was to demonstrate that these parts can be easily distributed, or the sort phase can be replaced with a more sophisticated k-mer counting method (e.g. bloom filters).

After you have your graph file, you can open a python interpreter, import debby, and play with the interface:

>>> import debby as db
>>> g = db.debruijn_graph.load("my-graph")
>>> g.label(4)
'TAC'
>>> g.indegree(4)
1

and so on...