Local homology recognition and distance measures in linear time using compressed amino acid alphabets

Abstract

Methods for discovery of local similarities and estimation of evolutionary distance by identifying k-mers (contiguous subsequences of length k) common to two sequences are described. Given unaligned sequences of length L, these methods have O(L) time complexity. The ability of compressed amino acid alphabets to extend these techniques to distantly related proteins was investigated. The performance of these algorithms was evaluated for different alphabets and choices of k using a test set of 1848 pairs of structurally alignable sequences selected from the FSSP database. Distance measures derived from k-mer counting were found to correlate well with percentage identity derived from sequence alignments. Compressed alphabets were seen to improve performance in local similarity discovery, but no evidence was found of improvements when applied to distance estimates. The performance of our local similarity discovery method was compared with the fast Fourier transform (FFT) used in MAFFT, which has O(L log L) time complexity. The method for achieving comparable coverage to FFT is revealed here, and is more than an order of magnitude faster. We suggest using k-mer distance for fast, approximate phylogenetic tree construction, and show that a speed improvement of more than three orders of magnitude can be achieved relative to standard distance methods, which require alignments.

CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA

“…Since large-scale microbial community profiling becomes more accessible to scientists, scalable yet accurate tools like CRiSPy are crucial for research in this area. Although CRiSPy is designed for microbial studies targeting DNA sequence analysis, the individual k-mer distance and genetic distance modules on GPUs can easily be extended to support protein sequence analysis and be used in general sequence analysis studies such as the usage of k-mer distance for fast, approximate phylogenetic tree construction by Edgar [17] or the utilization of pairwise genetic distance matrix in multiple sequence alignment programs such as ClustalW [18]. Availability: CRiSPy is available from the authors upon request.…”

Fine Grain Parallel Construction of Neighbour-joining Phylogenetic Trees with Reduced Redundancy Using Multithreading

“…(2) K-mer distance [10,12], which is linear time complexity for pairwise distance estimation. (3) Random generation by computer pseudo number, which is just to provide a fast method to generate data.…”

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

“…On the other hand, the enhancement of efficiency is widely observed in sequence-related bioinformatics studies. For example, a check for the accuracy and efficiency in the local homolog recognition with simplified alphabet is carried out [146]. It is found that the compressed alphabets could greatly improve the performance in local similarity discovery with a comparable coverage.…”