Zebrafish have become a popular organism for the study of vertebrate gene function1,2. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease3–5. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes6, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.

Metagenomics is the study of the genomic content of a sample of organisms obtained from a common habitat using targeted or random sequencing. Goals include understanding the extent and role of microbial diversity. The taxonomical content of such a sample is usually estimated by comparison against sequence databases of known sequences. Most published studies use the analysis of paired-end reads, complete sequences of environmental fosmid and BAC clones, or environmental assemblies. Emerging sequencing-by-synthesis technologies with very high throughput are paving the way to low-cost random "shotgun" approaches. This paper introduces MEGAN, a new computer program that allows laptop analysis of large metagenomic data sets. In a preprocessing step, the set of DNA sequences is compared against databases of known sequences using BLAST or another comparison tool. MEGAN is then used to compute and explore the taxonomical content of the data set, employing the NCBI taxonomy to summarize and order the results. A simple lowest common ancestor algorithm assigns reads to taxa such that the taxonomical level of the assigned taxon reflects the level of conservation of the sequence. The software allows large data sets to be dissected without the need for assembly or the targeting of specific phylogenetic markers. It provides graphical and statistical output for comparing different data sets. The approach is applied to several data sets, including the Sargasso Sea data set, a recently published metagenomic data set sampled from a mammoth bone, and several complete microbial genomes. Also, simulations that evaluate the performance of the approach for different read lengths are presented.[MEGAN is freely available at http://www-ab.informatik.uni-tuebingen.de/software/megan.]The genomic revolution of the early 1990s targeted the study of individual genomes of microorganisms, plants, and animals. While this type of analysis has almost become routine, the genomic analysis of complex mixtures of organisms remains challenging. Metagenomics has been defined as "the genomic analysis of microorganisms by direct extraction and cloning of DNA from an assemblage of microorganisms" (Handelsman 2004), and its importance stems from the fact that 99% or more of all microbes are deemed to be unculturable. Goals of metagenomic studies include assessing the coding potential of environmental organisms, quantifying the relative abundances of (known) species, and estimating the amount of unknown sequence information (environmental sequences) for which no species, or only distant relatives, have yet been described. It is useful to extend Handelsman's definition to also include sequences from higher organisms as well as just microorganisms, thus opening the door to "environmental forensics." By vastly extending the currently available sequences in databases, metagenomics promises to lead to the discovery of new genes that have useful applications in biotechnology and medicine (Steele and Streit 2005).Early metagenomics projects (Béja et al. 2000(Béja et...

Whole-genome duplication (WGD), or polyploidy, followed by gene loss and diploidization has long been recognized as an important evolutionary force in animals, fungi and other organisms, especially plants. The success of angiosperms has been attributed, in part, to innovations associated with gene or whole-genome duplications, but evidence for proposed ancient genome duplications pre-dating the divergence of monocots and eudicots remains equivocal in analyses of conserved gene order. Here we use comprehensive phylogenomic analyses of sequenced plant genomes and more than 12.6 million new expressed-sequence-tag sequences from phylogenetically pivotal lineages to elucidate two groups of ancient gene duplications-one in the common ancestor of extant seed plants and the other in the common ancestor of extant angiosperms. Gene duplication events were intensely concentrated around 319 and 192 million years ago, implicating two WGDs in ancestral lineages shortly before the diversification of extant seed plants and extant angiosperms, respectively. Significantly, these ancestral WGDs resulted in the diversification of regulatory genes important to seed and flower development, suggesting that they were involved in major innovations that ultimately contributed to the rise and eventual dominance of seed plants and angiosperms.

Ammonia oxidation is the first step in nitrification, a key process in the global nitrogen cycle that results in the formation of nitrate through microbial activity. The increase in nitrate availability in soils is important for plant nutrition, but it also has considerable impact on groundwater pollution owing to leaching. Here we show that archaeal ammonia oxidizers are more abundant in soils than their well-known bacterial counterparts. We investigated the abundance of the gene encoding a subunit of the key enzyme ammonia monooxygenase (amoA) in 12 pristine and agricultural soils of three climatic zones. amoA gene copies of Crenarchaeota (Archaea) were up to 3,000-fold more abundant than bacterial amoA genes. High amounts of crenarchaeota-specific lipids, including crenarchaeol, correlated with the abundance of archaeal amoA gene copies. Furthermore, reverse transcription quantitative PCR studies and complementary DNA analysis using novel cloning-independent pyrosequencing technology demonstrated the activity of the archaea in situ and supported the numerical dominance of archaeal over bacterial ammonia oxidizers. Our results indicate that crenarchaeota may be the most abundant ammonia-oxidizing organisms in soil ecosystems on Earth.

A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.