Jean-Michel Richer

NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference

MOTIVATION Reconstruction of gene regulatory networks (GRNs) is of utmost interest to biologists and is vital for understanding the complex regulatory mechanisms within the cell. Despite various methods developed for reconstruction of GRNs from gene expression profiles, they are notorious for high false positive rate owing to the noise inherited in the data, especially for the dataset with a large number of genes but a small number of samples. RESULTS In this work, we present a novel method, namely NARROMI, to improve the accuracy of GRN inference by combining ordinary differential equation-based recursive optimization (RO) and information theory-based mutual information (MI). In the proposed algorithm, the noisy regulations with low pairwise correlations are first removed by using MI, and the redundant regulations from indirect regulators are further excluded by RO to improve the accuracy of inferred GRNs. In particular, the RO step can help to determine regulatory directions without prior knowledge of regulators. The results on benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and experimentally determined GRN of Escherichia coli show that NARROMI significantly outperforms other popular methods in terms of false positive rates and accuracy. AVAILABILITY All the source data and code are available at: http://csb.shu.edu.cn/narromi.htm.

Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem

The Maximum Parsimony problem aims at reconstructing a phylogenetic tree from DNA sequences while minimizing the number of genetic transformations. To solve this NP-complete problem, heuristic methods have been developed, often based on local search. In this article, we focus on the influence of the neighborhood relations. After analyzing the advantages and drawbacks of the well-known NNI, SPR and TBR neighborhoods, we introduce the concept of Progressive Neighborhood which consists in constraining progressively the size of the neighborhood as the search advances. We empirically show that applied to the Maximum Parsimony problem, this progressive neighborhood turns out to be more efficient and robust than the classic neighborhoods using a descent algorithm. Indeed, it allows to find better solutions with a smaller number of iterations or trees evaluated.

A Memetic Algorithm for Phylogenetic Reconstruction with Maximum Parsimony

The Maximum Parsimony problem aims at reconstructing a phylogenetic tree from DNA, RNA or protein sequences while minimizing the number of evolutionary changes. Much work has been devoted by the research community to solve this NP-complete problem and many algorithms and techniques have been devised in order to find high quality solutions with reasonable computational resources. In this paper we present a memetic algorithm (implemented in the software Hydra) which is based on an integration of an effective local search operator with a specific topological tree crossover operator. We report computational results of Hydra on a set of 12 benchmark instances from the literature and demonstrate its effectiveness with respect to one of the most powerful software (TNT). We also study the behavior of the algorithm with respect to some fundamental ingredients.

Maximum Parsimony Phylogenetic Inference Using Simulated Annealing

The Maximum Parsimony (MP) problem aims at reconstructing a phylogenetic tree from DNA sequences while minimizing the total number of genetic transformations. In this paper we propose a carefully devised simulated annealing implementation, called SAMPARS (Simulated Annealing for Maximum PARSimony), for finding near-optimal solutions for the MP problem. Different possibilities for its key components and input parameter values were carefully analyzed and tunned in order to find the combination of them offering the best quality solutions to the problem at a reasonable computational effort. Its performance is investigated through extensive experimentation over well known benchmark instances showing that our SAMPARS algorithm is able to improve some previous best-known solutions.

A distance-based information preservation tree crossover for the maximum parsimony problem

The Maximum Parsimony problem aims at reconstructing a phylogenetic tree from DNA sequences while minimizing the number of evolutionary changes. Known to be NP-complete, the MP problem has many applications. This paper introduces a Distance-based Information Preservation (DiBIP) Tree Crossover. Contrary to previous crossover operators, DiBIP uses a distance measure to characterize the semantic information of a phylogenetic tree and ensures the preservation of distance related properties between parents and offspring. The performance of DiBIP is assessed with a mimetic algorithm on a set of 28 benchmark instances from the literature. Comparisons with 3 state-of-the-art algorithms show very competitive results of the proposed approach with improvement of some previously best results found.

Local search for the maximum parsimony problem

Four local search algorithms are investigated for the phylogenetic tree reconstruction problem under the Maximum Parsimony criterion. A new subtree swapping neighborhood is introduced and studied in combination with an effective array-based tree representation. Computational results are shown on a set of randomly generated benchmark instances as well as on 8 real problems (sequences of phytopathogen γ-proteobacteria) and compared with two references from the literature.

A new dynamic programming algorithm for multiple sequence alignment

Multiple sequence alignment (MSA) is one of the most basic and central tasks for many studies in modern biology. In this paper, we present a new progressive alignment algorithm for this very difficult problem. Given two groups A and B of aligned sequences, this algorithm uses Dynamic Programming and the sum-of-pairs objective function to determine an optimal alignment C of A and B. The proposed algorithm has a much lower time complexity compared with a previously published algorithm for the same task [11]. Its performance is extensively assessed on the well-known BAliBase benchmarks and compared with several state-of-the-art MSA tools.

A bottom-up implementation of Path-Relinking for Phylogenetic reconstruction applied to Maximum Parsimony

In this article we describe a bottom-up implementation of Path-Relinking for Phylogenetic Trees in the context of the resolution of the Maximum Parsimony problem with Fitch optimality criterion. This bottom-up implementation is compared to two versions of an existing top-down implementation. We show that our implementation is more efficient, more interesting to compare trees and to give an estimation of the distance between two trees in terms of the number of transformations.

Strategies for Phylogenetic Reconstruction

The phylogenetic reconstruction is considered a central underpinning of diverse field of biology like: ecology, molecular biology and physiology. The main example is modeling patterns and processes of evolution. Maximum Parsimony (MP) is an important approach to solve the phylogenetic reconstruction by minimizing the total number of genetic transformations, under this approach different metaheuristics have been implemented like tabu search, genetic and memetic algorithms to cope with the combinatorial nature of the problem. In this paper we review different strategies that could be added to existing implementations to improve their efficiency and accuracy. First we present two different techniques to evaluate the objective function by using CPU and GPU technology, then we show a Path-Relinking implementation to compare tree topologies and finally we introduces the application of these techniques in a Simulated Annealing algorithm looking for an optimal solution.