Armadillo 1.1: An Original Workflow Platform for Designing and Conducting Phylogenetic Analysis and Simulations

Etienne Lord,Vladimir Makarenkov,Abdoulaye Baniré Diallo,Alix Boc,Mickael Leclercq,Simon Joly

doi:10.1371/journal.pone.0029903

Etienne Lord, Vladimir Makarenkov + Show 4 more

Open Access

https://doi.org/10.1371/journal.pone.0029903

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Abstract

In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010–11. The program and its source code are freely available at: <http://www.bioinfo.uqam.ca/armadillo>.

Highlights

Bioinformatics is a fast-evolving field that encompasses molecular biology, biochemistry, computer science, mathematics and statistics [1]
Phylogenetics, which is a subfield of bioinformatics and molecular biology, studies evolutionary relationships between organisms based on their molecular or morphological proximity and presents those relationships through illustrations called phylogenetic trees [3]
In this article we described Armadillo, an original workflow platform dedicated to designing and performing phylogenetic analysis and simulations

Summary

Introduction

Bioinformatics is a fast-evolving field that encompasses molecular biology, biochemistry, computer science, mathematics and statistics [1]. ‘‘standard practices’’ for bioinformatics and phylogenetic analysis have not been strictly defined (apart from a number of specific fields [6,7]); each analytical step can be carried out using a variety of methods and tools [2]. When conducting their experiments and simulations, computational biologists have to cope with programs’ limitations and data integration issues [1]. Incorrect outcomes of biological data analysis can arise when accessible, but wrong, tools and models are used [8]

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