k-Mer Composition solved by 3431

Generalizing GC-Content

Figure 1. The 2-mer composition of TTGATTACCTTATTTGATCATTACACATTGTACGCTTGTGTCAAAATATCACATGTGCCT

A length $k$ substring of a genetic string is commonly called a k-mer. A genetic string of length $n$ can be seen as composed of $n-k+1$ overlapping k-mers. The k-mer composition of a genetic string encodes the number of times that each possible k-mer occurs in the string. See Figure 1. The 1-mer composition is a generalization of the GC-content of a strand of DNA, and the 2-mer, 3-mer, and 4-mer compositions of a DNA string are also commonly known as its di-nucleotide, tri-nucleotide, and tetra-nucleotide compositions.

The biological significance of k-mer composition is manyfold. GC-content is helpful not only in helping to identify a piece of unknown DNA (see “Computing GC Content”), but also because a genomic region having high GC-content compared to the rest of the genome signals that it may belong to an exon. Analyzing k-mer composition is vital to fragment assembly as well.

In “Computing GC Content”, we also drew an analogy between analyzing the frequency of characters and identifying the underlying language. For larger values of $k$, the k-mer composition offers a more robust fingerprint of a string's identity because it offers an analysis on the scale of substrings (i.e., words) instead of that of single symbols. As a basis of comparison, in language analysis, the k-mer composition of a text can be used not only to pin down the language, but also often the author.

Problem

For a fixed positive integer $k$, order all possible k-mers taken from an underlying alphabet lexicographically.

Then the k-mer composition of a string $s$ can be represented by an array $A$ for which $A[m]$ denotes the number of times that the $m$th k-mer (with respect to the lexicographic order) appears in $s$.

Given: A DNA string $s$ in FASTA format (having length at most 100 kbp).

Return: The 4-mer composition of $s$.

Sample Dataset

>Rosalind_6431
CTTCGAAAGTTTGGGCCGAGTCTTACAGTCGGTCTTGAAGCAAAGTAACGAACTCCACGG
CCCTGACTACCGAACCAGTTGTGAGTACTCAACTGGGTGAGAGTGCAGTCCCTATTGAGT
TTCCGAGACTCACCGGGATTTTCGATCCAGCCTCAGTCCAGTCTTGTGGCCAACTCACCA
AATGACGTTGGAATATCCCTGTCTAGCTCACGCAGTACTTAGTAAGAGGTCGCTGCAGCG
GGGCAAGGAGATCGGAAAATGTGCTCTATATGCGACTAAAGCTCCTAACTTACACGTAGA
CTTGCCCGTGTTAAAAACTCGGCTCACATGCTGTCTGCGGCTGGCTGTATACAGTATCTA
CCTAATACCCTTCAGTTCGCCGCACAAAAGCTGGGAGTTACCGCGGAAATCACAG

Sample Output

4 1 4 3 0 1 1 5 1 3 1 2 2 1 2 0 1 1 3 1 2 1 3 1 1 1 1 2 2 5 1 3 0 2 2 1 1 1 1 3 1 0 0 1 5 5 1 5 0 2 0 2 1 2 1 1 1 2 0 1 0 0 1 1 3 2 1 0 3 2 3 0 0 2 0 8 0 0 1 0 2 1 3 0 0 0 1 4 3 2 1 1 3 1 2 1 3 1 2 1 2 1 1 1 2 3 2 1 1 0 1 1 3 2 1 2 6 2 1 1 1 2 3 3 3 2 3 0 3 2 1 1 0 0 1 4 3 0 1 5 0 2 0 1 2 1 3 0 1 2 2 1 1 0 3 0 0 4 5 0 3 0 2 1 1 3 0 3 2 2 1 1 0 2 1 0 2 2 1 2 0 2 2 5 2 2 1 1 2 1 2 2 2 2 1 1 3 4 0 2 1 1 0 1 2 2 1 1 1 5 2 0 3 2 1 1 2 2 3 0 3 0 1 3 1 2 3 0 2 1 2 2 1 2 3 0 1 2 3 1 1 3 1 0 1 1 3 0 2 1 2 2 0 2 1 1