Evolutionary algorithms for the selection of single nucleotide polymorphisms

BACKGROUND: Large databases of single nucleotide polymorphisms (SNPs) are available for use in genomics studies. Typically, investigators must choose a subset of SNPs from these databases to employ in their studies. The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci. We present an evolutionary algorithm for multiobjective SNP selection. RESULTS: We implemented a modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java. Our implementation, Multiobjective Analyzer for Genetic Marker Acquisition (MAGMA), approximates the set of optimal trade-off solutions for large problems in minutes. This set is very useful for the design of large studies, including those oriented towards disease identification, genetic mapping, population studies, and haplotype-block elucidation. CONCLUSION: Evolutionary algorithms are particularly suited for optimization problems that involve multiple objectives and a complex search space on which exact methods such as exhaustive enumeration cannot be applied. They provide flexibility with respect to the problem formulation if a problem description evolves or changes. Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors. MAGMA is open source and available at . Evolutionary algorithms are well suited for many other applications in genomics

Orthology guided transcriptome assembly of Italian ryegrass and meadow fescue for single-nucleotide polymorphism discovery

Single-nucleotide polymorphisms (SNPs) represent natural DNA sequence variation. They can be used for various applications including the construction of high-density genetic maps, analysis of genetic variability, genome-wide association studies, and mapbased cloning. Here we report on transcriptome sequencing in the two forage grasses, meadow fescue (Festuca pratensis Huds.) and Italian ryegrass (Lolium multiflorum Lam.), and identification of various classes of SNPs. Using the Orthology Guided Assembly (OGA) strategy, we assembled and annotated a total of 18,952 and 19,036 transcripts for Italian ryegrass and meadow fescue, respectively. In addition, we used transcriptome sequence data of perennial ryegrass (L. perenne L.) from a previous study to identify 16,613 transcripts shared across all three species. Large numbers of intraspecific SNPs were identified in all three species: 248,000 in meadow fescue, 715,000 in Italian ryegrass, and 529,000 in perennial ryegrass. Moreover, we identified almost 25,000 interspecific SNPs located in 5343 genes that can distinguish meadow fescue from Italian ryegrass and 15,000 SNPs located in 3976 genes that discriminate meadow fescue from both Lolium species. All identified SNPs were positioned in silico on the seven linkage groups (LGs) of L. perenne using the GenomeZipper approach. With the identification and positioning of interspecific SNPs, our study provides a valuable resource for the grass research and breeding community and will enable detailed characterization of genomic composition and gene expression analysis in prospective Festuca Lolium hybrids

Evidence for suppression of immunity as a driver for genomic introgressions and host range expansion in races of Albugo candida, a generalist parasite

How generalist parasites with wide host ranges can evolve is a central question in parasite evolution. Albugo candida is an obligate biotrophic parasite that consists of many physiological races that each specialize on distinct Brassicaceae host species. By analyzing genome sequence assemblies of five isolates, we show they represent three races that are genetically diverged by ∼1%. Despite this divergence, their genomes are mosaic-like, with ∼25% being introgressed from other races. Sequential infection experiments show that infection by adapted races enables subsequent infection of hosts by normally non-infecting races. This facilitates introgression and the exchange of effector repertoires, and may enable the evolution of novel races that can undergo clonal population expansion on new hosts. We discuss recent studies on hybridization in other eukaryotes such as yeast, Heliconius butterflies, Darwin’s finches, sunflowers and cichlid fishes, and the implications of introgression for pathogen evolution in an agro-ecological environment

The transcriptome of the invasive eel swimbladder nematode parasite Anguillicola crassus

BACKGROUND: Anguillicola crassus is an economically and ecologically important parasitic nematode of eels. The native range of A. crassus is in East Asia, where it infects Anguilla japonica, the Japanese eel. A. crassus was introduced into European eels, Anguilla anguilla, 30 years ago. The parasite is more pathogenic in its new host than in its native one, and is thought to threaten the endangered An. anguilla across its range. The molecular bases for the increased pathogenicity of the nematodes in their new hosts is not known. RESULTS: A reference transcriptome was assembled for A. crassus from Roche 454 pyrosequencing data. Raw reads (756,363 total) from nematodes from An. japonica and An. anguilla hosts were filtered for likely host contaminants and ribosomal RNAs. The remaining 353,055 reads were assembled into 11,372 contigs of a high confidence assembly (spanning 6.6 Mb) and an additional 21,153 singletons and contigs of a lower confidence assembly (spanning an additional 6.2 Mb). Roughly 55% of the high confidence assembly contigs were annotated with domain- or protein sequence similarity derived functional information. Sequences conserved only in nematodes, or unique to A. crassus were more likely to have secretory signal peptides. Thousands of high quality single nucleotide polymorphisms were identified, and coding polymorphism was correlated with differential expression between individual nematodes. Transcripts identified as being under positive selection were enriched in peptidases. Enzymes involved in energy metabolism were enriched in the set of genes differentially expressed between European and Asian A. crassus. CONCLUSIONS: The reference transcriptome of A. crassus is of high quality, and will serve as a basis for future work on the invasion biology of this important parasite. The polymorphisms identified will provide a key tool set for analysis of population structure and identification of genes likely to be involved in increased pathogenicity in European eel hosts. The identification of peptidases under positive selection is a first step in this programme

RFreak-An R-package for evolutionary computation

RFreak is an R package providing a framework for evolutionary computation. By enwrapping the functionality of an evolutionary algorithm kit written in Java, it offers an easy way to do evolutionary computation in R. In addition, application examples where an evolutionary approach is promising in computational statistics are included and described in this paper. The package is thus further supporting the use of evolutionary computation in computational statistics. --R,evolutionary algorithms,evolutionary computation,association study,robust regression

The EM Algorithm and the Rise of Computational Biology

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the "central dogma"; of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org