# Finding optimal finite biological sequences over finite alphabets: the   OptiFin toolbox

**Authors:** R\'egis Garnier, Christophe Guyeux, and St\'ephane Chr\'etien

arXiv: 1706.08089 · 2017-06-27

## TL;DR

This paper introduces the OptiFin toolbox, a flexible, easy-to-use software solution employing metaheuristics to optimize finite biological sequences over finite alphabets, applicable in genomics and protein structure prediction.

## Contribution

The paper presents a customizable, MPI-compatible toolbox with embedded metaheuristics for optimizing biological sequences, addressing a common problem in bioinformatics.

## Key findings

- Performs well on two practical problems
- Flexible and easy to modify by users
- Embeds three well-known metaheuristics

## Abstract

In this paper, we present a toolbox for a specific optimization problem that frequently arises in bioinformatics or genomics. In this specific optimisation problem, the state space is a set of words of specified length over a finite alphabet. To each word is associated a score. The overall objective is to find the words which have the lowest possible score. This type of general optimization problem is encountered in e.g 3D conformation optimisation for protein structure prediction, or largest core genes subset discovery based on best supported phylogenetic tree for a set of species. In order to solve this problem, we propose a toolbox that can be easily launched using MPI and embeds 3 well-known metaheuristics. The toolbox is fully parametrized and well documented. It has been specifically designed to be easy modified and possibly improved by the user depending on the application, and does not require to be a computer scientist. We show that the toolbox performs very well on two difficult practical problems.

## Full text

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## Figures

13 figures with captions in the complete paper: https://tomesphere.com/paper/1706.08089/full.md

## References

17 references — full list in the complete paper: https://tomesphere.com/paper/1706.08089/full.md

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Source: https://tomesphere.com/paper/1706.08089