Special Issue "Genomes and Evolution: Computational Approaches"
Deadline for manuscript submissions: closed (31 October 2014)
Dr. Marnix Medema
Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
Phone: +31 317 484 706
The computational analysis of gene and genome sequences has become a key methodology for understanding the function and evolution of biological systems. Often, descriptions of specific computational methods that have led to exciting research results are discussed only briefly, or relegated to the supplementary information of the papers describing them. Yet, many of these methods merit a more thorough discussion of the key concepts on which they are based, and of the possible further opportunities for exploiting these methods in other contexts. This Special Issue aims to offer a platform for explaining, discussing and contextualizing important computational methods and algorithms. Such methods can assist other scientists researching the evolutionary history of gene and genome sequences and such genes’ biological functions.
Specific topics include, but are not limited to:
- Methods for tracing the evolutionary history of genome sequences, including, for example, the dynamics of introns and transposons, as well as duplication, recombination, and horizontal transfer events
- Methods for improving (meta)genome assembly by employing evolutionary information
- Phylogenetic methods for evaluating evolutionary relationships between genes and genomes
- Algorithms for studying patterns in amino acid sequences and/or protein structure evolution
- Tools for automating the annotation of genomes or genomic regions according to function
- Algorithms or pipelines for identifying mutations from high-throughput sequencing experiments
- Pipelines for evaluating the outcome of next-generation sequence assemblies
- Methods for evaluating the evolutionary similarity of genes, gene clusters, genomes, pan-genomes or metagenomes
- Models and tools for simulating, predicting or otherwise evaluating the evolution of genome-based metabolic or regulatory networks from a systems biology perspective
Prof. Dr. Rainer Breitling
Dr. Marnix Medema
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- computational biology
- systems biology
- comparative genomics
- sequence analysis
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Authors: Héctor Romero et al.
Title: Operon conservation measures
Abstract: Operons are well known genetic structures prevalent in Bacteria and Archaea. Genes that encode proteins sharing a metabolic pathway, composing the same molecular ensemble, or being nodes in a certain regulation network, are usually organized in operons. Despite there is plenty of data, how operons appear, evolve and die is still matter of hot debate. We developed some methods to measure operon conservation between organisms based on the rather straightforward idea of comparing operon organization of orthologous genes in two different organisms. A given operon organization can be seen as a partition of the gene complement, we used the tools of comparing partitions to asses operon conservation. We then test these measures with different genetic distances, genome sizes, using the complete gene complement, core genes or accessory genes.
Title: Computation of the likelihood in bi-allelic diffusion models using orthogonal polynomials
Authors: Claus Vogl et al.
Abstract: In population genetics, parameters describing forces such as mutation, migration, and drift are generally inferred from molecular data. Lately, methods based on simulations and summary statistics have been widely applied for such inference, even though these methods are only approximate and thus waste information. In contrast, probabilistic methods of inference can be shown to be optimal, if their assumptions are met. In genomic regions where recombination rates are high relative to mutation rates, polymorphic nucleotide sites can be assumed to evolve independently from each other. The distribution of allele frequencies at a large number of such sites has been called ``allele-frequency spectrum'' or ``site-frequency spectrum'' (SFS). Conditionally on the allelic proportions, the likelihoods of such data are binomial. A simple model representing the evolution of allelic proportions is the bi-allelic mutation-drift or mutation-migration-drift diffusion model. With infinite series of orthogonal polynomials, specifically Jacobi and Gegenbauer polynomials, the diffusion equations can be solved by efficiently and flexibly, even in non-equilibrium situations. The product of the binomial likelihoods with the sum of such polynomials leads to finite series of polynomials, i.e., relatively simple equations for the exact likelihoods. In this article, I investigate the use of orthogonal polynomials in the inference of population genetic parameters.
Authors: Michael T. Fluhler, Esq. and Dennis S. Fernandez, Esq.
Title: Intellectual Property Strategies for Genomics, Bioinformatics, and Computational Intelligence
Abstract: Bioinformatics and computational biology, especially in the context of genomics, are ever-growing fields that require the direct application of computational intelligence. The worldwide intellectual property (IP) ecosystem continuously evolves, especially with the recent reformation of the American patent system, and thus IP rights and strategies continue to be increasingly vital in these fields. In order to better understand the status quo of IP specifically in the fields of biology that apply computational intelligence, basic IP definitions, recent IP developments, and advanced protection, enforcement, and monetization strategies are discussed.