# Graphs for Bioinformatics, Part 2: Finding Eulerian Paths

Posted in Computational Biology

# The Context: de Bruijn Graphs

In Part 1 of this post we discussed a data structure called a de Bruijn graph and covered its application to genome assembly. To summarize, a de Bruijn graph is a type of graph that represents a set of k-mers as a set of directed edges on a graph, connecting the k-mer's (k-1)-mer prefix (the source vertex) to the k-mer's (k-1)-mer suffix (the destination vertex).

As an example, if $$k = 5$$, we can represent the k-mer "AAGCT" as an edge connecting the vertex AAGC to the vertex AGCT.

The de Bruijn graph is used to solve a set of problems on Rosalind.info, a website with bioinformatics programming challenges, as part of working through the textbook Bioinformatics Algorithms: An Active Learning Approach and its associated website (Rosalind.info).

# Assembling the de Bruijn Graph

The problems from Rosalind.info that require the use of a de Bruijn graph come from Chapter 3. These problems generally give the user either a list of k-mers (to assemble into a de Bruijn graph, as in problem BA3E) or a long sequence of DNA (which can be turned into a list of k-mers and assembled into a de Bruijn graph, as in problem BA3D).

If we are starting with a long string of DNA, we can run through the entire string and extract k-mers using a sliding window. For a string of DNA of length $$d$$, this procedure will create $$d - k + 1$$ k-mers.

## Directed Graph Representation: Adjacency List

The de Bruijn graph is a directed graph. To represent this graph in memory, we utilize an adjacency list data structure. An adjacency list is a key-value lookup table (implemented using a hash map) wherein each source vertex in the graph is a key in the lookup table, and the corresponding value is a list of all destination vertices (all vertices that have a directed edge starting from the source vertex and ending at that vertex).

A Python dictionary can be used to implement the adjacency list hash table. The dictionary keys are the source vertices (or rather, their string labels), and the dictionary values are a list of destination vertices (a list of their string labels).

Thus, the graph AA -> BB -> CC -> DD would be represented with the hash table:

adjacency_list['AA'] = ['BB']


(Notice from this example that the keys of the adjacency list gives a list of source vertices only, to get all vertices we need to look at the values of the adjacency list too.)

## A Quick Example

As a simple example, consider the de Bruijn graph formed from the DNA string AAGATTCTCTAC and $$k = 4$$.

This is first turned into a bag of $$d - k + 1 = 9$$ 4-mers (our edges):

Sequence:   AAGATTCTCTAC
4-mers:     AAGA
AGAT
GATT
ATTC
TTCT
TCTC
CTCT
TCTA
CTAC


Next, we also create a bag of $$d - k + 1 = 10$$ 3-mers (vertices):

Sequence:   AAGATTCTCTAC
3-mers:     AAG
AGA
GAT
ATT
TTC
TCT
CTC
TCT
CTA
TAC


Now we can iterate over every 4-mer edge, find its prefix 3-mer and suffix 3-mer, and create a corresponding entry in the adjacency list hash table.

The list of edges looks like this:

AAG -> AGA
AGA -> GAT
ATT -> TTC
CTA -> TAC
CTC -> TCT
GAT -> ATT
TCT -> CTA,CTC
TTC -> TCT


The corresponding dictionary should look like this:

adjacency_list['AAG'] = ['AGA']


## Python vs Go

Now that we're ready to implement a directed graph object and populate it using the data given in the problem, we have to make the difficult choice of what language we want to use to implement the directed graph.

We have covered our use of the Go programming language for Rosalind.info problems before (we have previously covered recursion for Chapter 2 problems in Part 1, Part 2, and Part 3 of another post, and we also wrote this post on our impression of Go and its usefulness in bioinformatics.

We are also implementing all of the solutions to the Rosalind.info problems in our Go libarary, go-rosalind (see corresponding documentation on godoc.org).

However, we have learned the hard way that Go requires a lot of boilerplate code (boilerplate code that is necessary, mind you, because all of that boilerplate will eventually morph into something problem-specific).

This all means that Go is a very cumbersome language to use to get an algorithm prototype up and running.

Python, on the other hand, is a very easy language for prototyping and has plenty of handy built-in functions and modules that make prototyping an algorithm far easier and faster than doing it in Go.

Our strategy, therefore, is to prototype our algorithm and corresponding graph object in Python, get the algorithm working and tested, then convert the code to Go when we are finished.

## Directed Graph Class: Python Implementation

Note that while we could simply use the dictionary object itself as the graph data structure, this is somewhat inelegant, and we would like instead to define a class to bundle related behavior and data together.

We implement the directed graph by defining an AdjacencyGraph class. This is just a glorified wrapper around the ajacency list dictionary, with some extra methods.

We start by defining the class (it inherits from object so it has no parent type):

class AdjacencyGraph(object):
"""Directed graph stored using an adjacency list"""
def __init__(self):
"""Constructor"""
self.dfs_started = False


The constructor just initializes an empty adjacency list dictionary.

We also define two built-in methods for convenience: __str__ for the string representation of the graph (so we can pass the graph object to print()), and __len__ for getting the number of (source) vertices on the graph.

    def __str__(self):
"""String representation"""
s = []
for source in self.adj.keys():
for sink in sinks:
m = "%s -> %s\n"%(source,sink)
s.append(m)
return "".join(s)

def __len__(self):
"""Number of vertices on graph"""
s = set()
for source in self.adj.keys():
for sink in self.adj[k]:
return len(s)


Next, we define some basic functionality useful for all graphs:

• Getting the in-degree and out-degree of a vertex
    def in_degree(self,u):
n = 0
for v in self.adj.keys():
if u in sinks:
n += 1
return n

def out_degree(self,u):
if u in self.adj.keys():
else:
return 0


We also define a generator for creating vertices:

    def vertices(self):
vertices = set()
for k in self.adj.keys():
for m in self.adj[k]:
for v in vertices:
yield v

def n_vertices(self):
return len(self)

def n_edges(self):
n = 0
for source in self.adj.keys():
try:
except:
# in case value is None
pass
return n

def get_neighbors(self,u):
"""Get all neighbors of node u"""
# Note: neighbors are stored in
# sorted order
if u in self.adj.keys():
else:
return []


Finally, we add a method add_edge() that allows us to create an edge from vertex u to vertex v (and add the vertices to the graph if either do not yet exist on the graph).

For convenience, we maintain the adjacency list values (the list of destination vertices) in lexicographic order.

    def add_edge(self, u, v):
"""Add an edge from u to v"""
# For each source vertex:
if u in self.adj.keys():

# Get existing sink list

# Append to it
t.append(v)

# Keep list of sinks sorted
# (lexicographic string sorting)
t.sort()

# Create the new edge
# from source to sink

else:
# Initialize the list of sinks (v)
# for the given source (u)


Now, to assemble the de Bruijn graph, we can iterate over every k-mer edge, form the prefix and suffix vertices, and call the add_edge() function on the graph.

# Checking for Eulerian Paths and Cycles

To recap Eulerian paths versus Eulerian cycles (discussed in Part 1 of this post:

• An Eulerian path is a path that visits every edge of a given graph exactly once.
• An Eulerian cycle is an Eulerian path that begins and ends at the ''same vertex''.

According to Steven Skienna's Algorithm Design Handbook, there are two conditions that must be met for an Eulerian path or cycle to exist. These conditions are different for undirected graphs versus directed graphs.

Undirected graphs:

• An undirected graph contains an Euler cycle iff (1) it is connected, and (2) each vertex is of even degree.

• An undirected graph contains an Euler path iff (1) it is connected, and all but two vertices are of even degree. These two vertices will be the start and end vertices for the Eulerian path.

Directed graphs:

• A directed graph contains an Euler cycle iff (1) it is strongly-connected, and (2) each vertex has the same in-degree as out-degree

• A directed graph contains an Euler path iff (1) it is connected, and (2) all vertices except two (x,y) have the same in-degree as out-degree, and (x,y) are vertices with in-degree one less than and one more than out-degree

# Algorithm Building Blocks

Algorithm to find Eulerian paths/cycles consists of several steps using several algorithms.

Undirected graphs are the simpler case; directed graphs are more complicated.

## Depth First Search

To perform a DFS on a directed graph, implement two functions:

1. Write a DFS function that takes a graph as an input argument and that visits each node of the graph in a depth-first search.

2. Write a visitation function that takes a node as an input argument and that performs some action on the node. This visitation function is called by the DFS function on each node that it visits.

## Kosaraju's Algorithm: Connected Components

On an undirected graph, can use Fleury's Algorithm to follow edges (classify edges as bridge or non-bridge, then leave bridges for last).

On a directed graph, we have twice the amount of work: we are not just checking that all vertices are reachable from a given vertex, we are also checking that all vertices can also reach that vertex.

# To Be Continued...

In the next part of this post, we will start with the slightly simpler case of finding an Euler cycle (which has no start or end vertices). Then we will show how finding the Euler path is actually a special case of finding the Euler cycle.

First, we will use Hierholzer's Algorithm to find Euler cycles (this is the simpler case). Order does not matter because it is a cycle; Hierholzer's algorithm is used to find the Euler cycle.

Next, we will modify the above algorithm to find Euler paths. This requires keeping track of the start and end candidate nodes. We verify only one each; we complete the cycle by adding an edge. Once we find the cycle, we remove the edge. Finally, we rearrange the cycle to have the correct start and end nodes.

Stay tuned for Part 3...