Martin Middendorf

MITOS: improved de novo metazoan mitochondrial genome annotation.

About 2000 completely sequenced mitochondrial genomes are available from the NCBI RefSeq data base together with manually curated annotations of their protein-coding genes, rRNAs, and tRNAs. This annotation information, which has accumulated over two decades, has been obtained with a diverse set of computational tools and annotation strategies. Despite all efforts of manual curation it is still plagued by misassignments of reading directions, erroneous gene names, and missing as well as false positive annotations in particular for the RNA genes. Taken together, this causes substantial problems for fully automatic pipelines that aim to use these data comprehensively for studies of animal phylogenetics and the molecular evolution of mitogenomes. The MITOS pipeline is designed to compute a consistent de novo annotation of the mitogenomic sequences. We show that the results of MITOS match RefSeq and MitoZoa in terms of annotation coverage and quality. At the same time we avoid biases, inconsistencies of nomenclature, and typos originating from manual curation strategies. The MITOS pipeline is accessible online at http://mitos.bioinf.uni-leipzig.de.

A hierarchical particle swarm optimizer and its adaptive variant

A hierarchical version of the particle swarm optimization (PSO) metaheuristic is introduced in this paper. In the new method called H-PSO, the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure. Depending on the quality of their so-far best-found solution, the particles move up or down the hierarchy. This gives good particles that move up in the hierarchy a larger influence on the swarm. We introduce a variant of H-PSO, in which the shape of the hierarchy is dynamically adapted during the execution of the algorithm. Another variant is to assign different behavior to the individual particles with respect to their level in the hierarchy. H-PSO and its variants are tested on a commonly used set of optimization functions and are compared to PSO using different standard neighborhood schemes.

Bi-Criterion Optimization with Multi Colony Ant Algorithms

In this paper we propose a new approach to solve bi-criterion optimization problems with ant algorithms where several colonies of ants cooperate in finding good solutions. We introduce two methods for co-operation between the colonies and compare them with a multistart ant algorithm that corresponds to the case of no cooperation. Heterogeneous colonies are used in the algorithm, i.e. the ants differ in their preferences between the two criteria. Every colony uses two pheromone matrices -- each suitable for one optimization criterion. As a test problem we use the Single Machine Total Tardiness problem with changeover costs.

Multi Colony Ant Algorithms

In multi colony ant algorithms several colonies of ants cooperate in finding good solutions for an optimization problem. At certain time steps the colonies exchange information about good solutions. If the amount of exchanged information is not too large multi colony ant algorithms can be easily parallelized in a natural way by placing the colonies on different processors. In this paper we study the behaviour of multi colony ant algorithms with different kinds of information exchange between the colonies. Moreover we compare the behaviour of different numbers of colonies with a multi start single colony ant algorithm. As test problems we use the Traveling Salesperson problem and the Quadratic Assignment problem.

A Population Based Approach for ACO

A population based ACO (Ant Colony Optimization) algorithm is proposed where (nearly) all pheromone information corresponds to solutions that are members of the actual population. Advantages of the population based approach are that it seems promising for solving dynamic optimization problems, its finite state space and the chances it offers for designing new metaheuristics. We compare the behavior of the new approach to the standard ACO approach for several instances of the TSP and the QAP problem. The results show that the new approach is competitive.

Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP

We investigate strategies for pheromone modification of ant algorithms in reaction to the insertion/deletion of a city of Traveling Salesperson Problem (TSP) instances. Three strategies for pheromone diversification through equalization of the pheromone values on the edges are proposed and compared. One strategy acts globally without consideration of the position of the inserted/deleted city. The other strategies perform pheromone modification only in the neighborhood of the inserted/deleted city, where neighborhood is defined differently for the two strategies. We furthermore evaluate different parameter settings for each of the strategies.

Applying Population Based ACO to Dynamic Optimization Problems

Population based ACO algorithms for dynamic optimization problems are studied in this paper. In the population based approach a set of solutions is transferred from one iteration of the algorithm to the next instead of transferring pheromone information as in most ACO algorithms. The set of solutions is then used to compute the pheromone information for the ants of the next iteration. The population based approach can be used to solve dynamic optimization problems when a good solution of the old instance can be modified after a change of the problem instance so that it represents a reasonable solution for the new problem instance. This is tested experimentally for a dynamic TSP and dynamic QAP problem. Moreover the behavior of different strategies for updating the population of solutions are compared.

An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems

Ant Colony Optimization is an evolutionary method that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem. Compared to an existing ACO algorithm for the unweighted Total Tardiness Problem our algorithm has several improvements. The main novelty is that in our algorithm the ants are guided on their way to good solutions by sums of pheromone values. This allows the ants to take into account pheromone values that have already been used for making earlier decisions.

An Island Model Based Ant System with Lookahead for the Shortest Supersequence Problem

In this paper we introduce an Ant Colony Optimisation (ACO) algorithm for the Shortest Common Supersequence (SCS) problem, which has applications in production system planning, mechanical engineering and molecular biology. The ACO algorithm is used to find good parameters for a heuristic for the SCS problem. An island model with several populations of ants is used for the ACO algorithm. Besides we introduce a lookahead function which makes the decisions of the ants dependent on the state arrived after the decision.

A comprehensive analysis of bilaterian mitochondrial genomes and phylogeny

About 2800 mitochondrial genomes of Metazoa are present in NCBI RefSeq today, two thirds belonging to vertebrates. Metazoan phylogeny was recently challenged by large scale EST approaches (phylogenomics), stabilizing classical nodes while simultaneously supporting new sister group hypotheses. The use of mitochondrial data in deep phylogeny analyses was often criticized because of high substitution rates on nucleotides, large differences in amino acid substitution rate between taxa, and biases in nucleotide frequencies. Nevertheless, mitochondrial genome data might still be promising as it allows for a larger taxon sampling, while presenting a smaller amount of sequence information. We present the most comprehensive analysis of bilaterian relationships based on mitochondrial genome data. The analyzed data set comprises more than 650 mitochondrial genomes that have been chosen to represent a profound sample of the phylogenetic as well as sequence diversity. The results are based on high quality amino acid alignments obtained from a complete reannotation of the mitogenomic sequences from NCBI RefSeq database. However, the results failed to give support for many otherwise undisputed high-ranking taxa, like Mollusca, Hexapoda, Arthropoda, and suffer from extreme long branches of Nematoda, Platyhelminthes, and some other taxa. In order to identify the sources of misleading phylogenetic signals, we discuss several problems associated with mitochondrial genome data sets, e.g. the nucleotide and amino acid landscapes and a strong correlation of gene rearrangements with long branches.