# Recursive Backtracking in Go for Bioinformatics Applications: 3. Go Implementation of Backtracking

Posted in Computational Biology

This is the third in a series of three blog posts describing our solution to a bioinformatics problem from Rosalind.info, Problem BA1(i) (Find most frequent words with mismatches in a string). To solve this problem and generate variations of a DNA string as required, we implemented a recursive backtracking method in the Go programming language.

## Problem Description

The task at hand is to take a given input strand of DNA, and generate variations from it that have up to $$d$$ differences (a Hamming distance of $$d$$) in the codons (base pairs).

In part 1 of this series, we walk through the construction of an analytical formula to count the number of variations of a given DNA string that can be generated, given the constraints of the problem.

In part 2 of this series, we cover several techniques to generate variations on a DNA string, and present pseudocode for the recursive backtracking method that we use here.

In part 3 of this series, we will cover our implementation of the recursive backtracking method in the Go programming language.

## Recursive Backtracking Pseudocode

To review from the prior post, our pseudocode for recursive backtracking to explore variations or combinations looks like the following:

explore method:
base case:
visit this solution
recursive case:
for each available choice:
make a choice
explore outcomes
unmake the choice
move on to the next choice


The key elements there are the base and recursive cases, and the mechanism of iterating over each possible choice and making/exploring/unmaking the choice.

## Recursive Backtracking: Go Implementation

In total, we have three different methods to accomplish this task:

• VisitHammingNeighbors(input,d): this is the public method that the user calls to generate a string array of all strings that are a Hamming distance of up to d from the input string input. This public method performs parameter and error checking, initializes space for data, and collects results.

• visitHammingNeighbors_recursive(base_kmer, depth, choices, results_map): this method is the private recursive method available only to the package. This method performs the actual recursive work.

NOTE: the function name starts with a lower case letter, so it is not exported by the package - i.e., it is not available to the user when they import this package.

The base case of the visitHammingNeighbors_recursive() function will pass the final set of choices to the final step:

• assemble_variations(base_kmer, choices, results_map): this method (private to the package) is a recursive method that uses the chosen indices and

### Visit Hamming Neighbors Function

The function call to visit all Hamming neighbors and add them to the results set is split into two parts: a non-recursive public function, which provides a public wrapper that is user-friendly and performs error-checking on the parameters provided, and a recursive private function that is used internally but not intended to be called by users directly.

#### Public, Non-Recursive Function

Here is the entry point function that the user calls when they wish to generate all variations on a given string of DNA, and have the variations returned as a string slice.

// Given an input string of DNA, generate variations
// of said string that are a Hamming distance of
// less than or equal to d.
func VisitHammingNeighbors(input string, d int) (map[string]bool, error) {

// a.k.a. visit_kmer_neighbors

// number of codons
n_codons := 4

// Use combinatorics to calculate the total
// number of variation.
buffsize, _ := CountHammingNeighbors(len(input), d, n_codons)


The call to CountHammingNeighbors() uses the counting formula from Part 1 to predict the number of variations. If the user has selected an astronomical problem size, the program warns the user.

    // This blows up quickly, so warn the user
// if their problem is too big
MAX := int(1e6)
if buffsize > MAX {
msg := fmt.Sprintf("Error: you are generating over MAX = %d permutations, you probably don't want to do this.", d)
return nil, errors.New(msg)
}


Now the actual recursive backtracking algorithm begins. The code loops over every possible value of Hamming distance $$d$$ and calls the recursive method at each value of $$d$$.

    // Store the final results in a set (string->bool map)
results := make(map[string]bool)

// Begin backtracking algorithm
for dd := 0; dd <= d; dd++ {

// The choices array will change with each recursive call.
// Go passes all arguments by copy, which is good for us.
choices := []int{}

// Populate list of neighbors
visitHammingNeighbors_recursive(input, dd, choices, results)

}


We don't assign any results from the call to visitHammingNeighbors_recursive() because we pass in a data structure (actually a pointer to a data structure), results, that is modified in-place.

Thus, when we complete a call to visitHammingNeighbors_recursive(), results will contain all variations already.

    // Check if we have the right number of results
if len(results) != buffsize {
fmt.Printf("WARNING: number of results (%d) did not match expected value (%d)\n", len(results), buffsize)
}

return results
}


#### Private, Recursive Function

In the above function, the call to the recursive function to visit all Hamming neighbors happens here:

        // Populate list of neighbors
visitHammingNeighbors_recursive(input, dd, choices, results)


The user passes the original kmer input, along with the Hamming distance parameter dd, the list of choices of indices that have already been selected choices, and the data structure storing all resulting strings results.

As with the pseudocode, we have a base case and a recursive case. The recursive function is being called repeatedly until it reaches a depth of 0, with the depth parameter being decremented each call.

// Recursive function: given an input string of DNA,
// generate Hamming neighbors that are a Hamming
// distance of exactly d. Populate the neighbors
// array with the resulting neighbors.
func visitHammingNeighbors_recursive(base_kmer string, depth int, choices []int, results map[string]bool) error {

if depth == 0 {

// Base case

} else {

// Recursive case

}
}


The base case occurs when we reach a depth of 0 and have no further choices to make. We reach this base case for each binary number with $$d$$ digits set to 1; once the base case is reached, we call the assemble_variations() function to substitute all possible codons at the selected indices.

func visitHammingNeighbors_recursive(base_kmer string, depth int, choices []int, results map[string]bool) error {

if depth == 0 {

// Base case
assemble_variations(base_kmer, choices, results)
return nil


The recursive case is slightly more complicated, but it follows the same backtracking pseudocode covered previously: from a set of possible choices, try each choice, recursively call this function, then unmake the choice and move on to the next choice.

Here, the choice is which index c in the kmer to modify. Each kmer can only be modified once, so we have a for loop to check if the index c is in the list of choices already made.

    } else {

// Recursive case
for c := 0; c <= len(base_kmer); c++ {

for _, choice := range choices {
if c == choice {
}
}


As before, the recursive call to this function will not return any values that need to be stored, since results points to a data structure (map) that is modified in-place.

            if !indexAlreadyTaken {

// This will make a new copy of choices
// for each recursive function call
choices2 := append(choices, c)
err := visitHammingNeighbors_recursive(base_kmer, depth-1, choices2, results)
if err != nil {
return err
}

}
}

}

return nil
}


### Assemble Visit Variation Function

Once we've generated each list of indices to modify, we call a second recursive function to substitute each codon into each index.

In the recursive method above, each recursive function call added a new choice to choices; in this recursive function, each recursive funcction call pops a choice from choices. Thus, the base case is when choices is empty.

Here are the base and recursive cases:

// Given a base kmer and a choice of indices where
// the kmer should be changed, generate all possible
// variations on this base_kmer.
func assemble_variations(base_kmer string, choices []int, results map[string]bool) {

if len(choices) > 0 {

// Recursive case
...

} else {

// Base case
...

}
}


The recursive case pops a choice from choices, finds which nucleotide (AGCT) is at that location, and assembles the list of possible choices (the other 3 nucleotide values). It then performs the recursive backtracking algorithm, choosing from each of the three possible nucleotide values, exploring the choice by making a recursive call, then un-making the choice.

func assemble_variations(base_kmer string, choices []int, results map[string]bool) {

if len(choices) > 0 {

// Recursive case

all_codons := []string{"A", "T", "G", "C"}

// Pop the next choice
// https://github.com/golang/go/wiki/SliceTricks
ch_ix, choices := choices, choices[1:]

// Get the value of the codon at that location
if ch_ix < len(base_kmer) {
// slice of string is bytes,
// so convert back to string
this_codon := string(base_kmer[ch_ix])
for _, codon := range all_codons {

if codon != this_codon {
// Swap out the old codon with the new codon
new_kmer := base_kmer[0:ch_ix] + codon + base_kmer[ch_ix+1:]
assemble_variations(new_kmer, choices, results)
}
}
}

} else {

// Base case
results[base_kmer] = true

}
}


## Tests

The last step after some debugging was to write tests for the function to generate all variations of a DNA string, to ensure the recursive backtracking functions work correctly.

The pattern we use is to create a struct containing test parameters, then create a test matrix by initializing instances of the parameter struct with the parameters we want to test.

Here is how we set up the tests:

func TestMatrixVisitHammingNeighbors(t *testing.T) {
var tests = []struct {
input string
d     int
gold  []string
}{
{"AAA", 1,
[]string{"AAC", "AAT", "AAG", "AAA", "CAA", "GAA", "TAA", "ATA", "ACA", "AGA"},
},
}
for _, test := range tests {

...

}
}


Each test case should generate all Hamming neighbors, and compare to the list of Hamming neighbors provided. This requires two tricks:

• sort before comparing, to ensure a proper comparison
• use a custom EqualStringSlices() function that will iterate through two string slices element-wise to check if they are equal.

The EqualStringSlices() function is required because Go does not have built-in equality checks for slices.

Here is what the tests look like:

    for _, test := range tests {

// Money shot
result, err := VisitHammingNeighbors(test.input, test.d)

// Check if there was error
if err != nil {
msg := fmt.Sprintf("Error: %v", err)
t.Error(msg)
}

// Sort before comparing
sort.Strings(test.gold)
sort.Strings(result)

if !EqualStringSlices(result, test.gold) {
msg := fmt.Sprintf("Error testing VisitHammingNeighbors():\ncomputed = %v\ngold     = %v",
result, test.gold)
t.Error(msg)
}
}


## Final Code

The final version of the recursive function to visit all Hamming neighbors and return them in a string array can be found in the go-rosalind library on Github.

Specifically, in the file rosalind_ba1.go, there is a VisitHammingNeighbors() function that is the public function that calls the private recursive function visitHammingNeighbors_recursive(), and the recursive function to swap out codons is in the visit() funciont.

## Go Forth and Be Fruitful

Now that you have the basic tools to imlement a recursive backtracking algorithm in Go to generate string variations, you have one of the key ingredients to solve Rosalind.info problem BA1i, "Find Most Frequent Words with Mismatches by String".

This problem is tricky principally because it requires generating every DNA string variation, so now you should have the key ingredient to solve BA1i (and several problems that follow).

You can use the final version of the methods we covered by importing the go-rosalind library in your Go code (link to go-rosalind documentation on godoc.org) or you can implement your own version of these algorithms. The Go code we covered in this post is also on Github in the charlesreid1/go-rosalind repository.