Dromi: Python package for parallel computation of similarity measures among vector-encoded sequences

Abstract

Calculating similarities among sequences (i.e biological sequences) can be a challenging task. Here I introduce Dromi, a simple python package that can compute different similarity measurements (i.e percent identity, cosine similarity, kmer similarities) across aligned vector-encoded sequences. This is a crucial step required to perform both upstream and downstream sequence machine learning tasks such as sequence clustering [1, 2, 3], sequence analysis [4] and other pre- or post-processing demands on sequences. Additionally, this package introduces the calculation of the measure referred as positional weights. These represent the cosine similarities or residue-conservation across sequence elements (i.e amino acids in peptide sequences) in the same site (column). The program can also deal with sequences of variable length since end-padded positions are not considered for the calculations. The presented implementations are an incorporation into the arsenal of tools to measure similarity among small peptide sequences such as epitopes.