FASTA Herder: a web application to trim protein sequence sets

doi:10.14293/s2199-1006.1.sor-life.a67837.v1

Abstract

The ever increasing number of sequences in protein databases usually turns out large numbers of homologs in sequence similarity searches. While information from homology can be very useful for functional prediction based on amino acid conservation, many of these homologs usually have high levels of identity among themselves, which hinders multiple sequence alignment (MSA) computation and, especially, visualization. More generally, high redundancy reduces the usability of a protein set in machine learning applications and biases statistical analyses. We developed an algorithm to identify redundant sequence homologs that can be culled producing a streamlined FASTA file. As a difference from other automatic approaches that only aggregate sequences with high identity, our method clusters near-full length homologs allowing for lower sequence identity thresholds. Our method was fully tested and implemented in a web application called FASTA Herder, publicly available at http://www.ogic.ca/projects/fh/orain.html .

Highlights

Multiple sequence alignment (MSA) remains the most important analytic tool to assess evolutionary relations between proteins and to determine the conserved regions of the sequence that usually harbor structural and functional properties
We developed an algorithm to identify redundant sequence homologs that can be culled producing a streamlined FASTA file
The aim of our tool is to reduce potentially high redundancy in a FASTA file that may contain proteins belonging to one or more families. This would speed MSA calculation, facilitate MSA inspection, and remove biases that may affect any statistical analysis or computational application involving that set of proteins

Summary

Introduction

Multiple sequence alignment (MSA) remains the most important analytic tool to assess evolutionary relations between proteins and to determine the conserved regions of the sequence that usually harbor structural and functional properties. While information from homology can be very useful for functional prediction based on amino acid conservation, many of these homologs usually have high levels of identity among themselves, which hinders multiple sequence alignment computation and, especially, visualization. As a difference from other automatic approaches that only aggregate sequences with high identity, our method clusters near full-length homologs allowing for lower sequence identity thresholds.

Results

Conclusion