Colletotrichum species are fungal pathogens that devastate crop plants worldwide. Host infection involves the differentiation of specialized cell types that are associated with penetration, growth inside living host cells (biotrophy) and tissue destruction (necrotrophy). We report here genome and transcriptome analyses of Colletotrichum higginsianum infecting Arabidopsis thaliana and Colletotrichum graminicola infecting maize. Comparative genomics showed that both fungi have large sets of pathogenicity-related genes, but families of genes encoding secreted effectors, pectin-degrading enzymes, secondary metabolism enzymes, transporters and peptidases are expanded in C. higginsianum. Genome-wide expression profiling revealed that these genes are transcribed in successive waves that are linked to pathogenic transitions: effectors and secondary metabolism enzymes are induced before penetration and during biotrophy, whereas most hydrolases and transporters are upregulated later, at the switch to necrotrophy. Our findings show that preinvasion perception of plant-derived signals substantially reprograms fungal gene expression and indicate previously unknown functions for particular fungal cell types

BackgroundThe process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly.ResultsIn Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies.ConclusionsMany current genome assemblers produced useful assemblies, containing a significant representation of their genes and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another.

A finished clone-based assembly of the mouse genome reveals extensive recent sequence duplication during recent evolution and rodent-specific expansion of certain gene families. Newly assembled duplications contain protein-coding genes that are mostly involved in reproductive function.

Background: Rice feeds much of the world, and possesses the simplest genome analyzed to date within the grass family, making it an economically relevant model system for other cereal crops. Although the rice genome is sequenced, validation and gap closing efforts require purely independent means for accurate finishing of sequence build data.

The collection of classical inbred mouse strains displays heritable variation in a large number of complex traits. Many generations of historical recombination have contributed to the panel of classical strain genomes, raising the possibility that quantitative trait loci could be located with high resolution by correlating strain genotypes and phenotypes. Although this association mapping framework has been successful in several empirical applications, its expected performance remains unclear. We used computer simulations based on a publicly available, dense single-nucleotide polymorphism (SNP) map to measure the power and false-positive rate of association mapping on a genomic scale across 30 commonly used classical inbred strains. Expected power is (i) often low for phenotypic effect sizes that are realistic for complex traits, (ii) highly variable across the genome, and (iii) correlated with linkage disequilibrium, aspects of the allele frequency distribution, and haplotype characteristics, as predicted by theory. Simulations also demonstrate clear potential for spurious associations to be generated by unequal relatedness among the strains. These findings suggest that association mapping in the classical strains is best applied in combination with other procedures, such as QTL mapping. C LASSICAL inbred mouse strains provide powerful model systems for dissecting the genetic basis of complex phenotypes. The collection of widely available strains displays dramatic genetic variation in many quantitative traits, and the association of phenotypes with molecular markers in controlled crosses can reveal chromosomal regions that contain the causal loci. This strategy, quantitative trait locus (QTL) mapping, provides essential information about the genetic basis of complex phenotypes, including locus positions, effect sizes, and modes of action. However, standard QTL designs involve only one generation of recombination, so that phenotypic variation is typically associated with large genomic regions. This low level of mapping resolution has left the genes underlying most mouse QTL unidentified . Populations of lines formed by additional generations of recombination, including recombinant inbred lines, advanced intercross lines, and heterogeneous stocks, allow finer mapping resolution (Mott et al. 2000;Williams et al. 2001;Churchill et al. 2004;Yalcin et al. 2005;Valdar et al. 2006), but narrowing the resulting genomic intervals to small numbers of contributing genes still constitutes a formidable challenge.The recent ability to genotype strains at markers from across the genome and the low resolution of most crossing studies has led some investigators to pursue an alternative approach to mapping complex trait variation. In this method (originally referred to as ''in silico mapping''), genotypes and phenotypes from groups of classical inbred strains are compared to identify genomic regions that correlate with phenotypic variation (Grupe et al. 2001;Pletcher et al. 2004). Because the collective genomes of classic...