A 2.91-billion base pair (bp) consensus sequence of the euchromatic portion of the human genome was generated by the whole-genome shotgun sequencing method. The 14.8-billion bp DNA sequence was generated over 9 months from 27,271,853 high-quality sequence reads (5.11-fold coverage of the genome) from both ends of plasmid clones made from the DNA of five individuals. Two assembly strategies—a whole-genome assembly and a regional chromosome assembly—were used, each combining sequence data from Celera and the publicly funded genome effort. The public data were shredded into 550-bp segments to create a 2.9-fold coverage of those genome regions that had been sequenced, without including biases inherent in the cloning and assembly procedure used by the publicly funded group. This brought the effective coverage in the assemblies to eightfold, reducing the number and size of gaps in the final assembly over what would be obtained with 5.11-fold coverage. The two assembly strategies yielded very similar results that largely agree with independent mapping data. The assemblies effectively cover the euchromatic regions of the human chromosomes. More than 90% of the genome is in scaffold assemblies of 100,000 bp or more, and 25% of the genome is in scaffolds of 10 million bp or larger. Analysis of the genome sequence revealed 26,588 protein-encoding transcripts for which there was strong corroborating evidence and an additional ∼12,000 computationally derived genes with mouse matches or other weak supporting evidence. Although gene-dense clusters are obvious, almost half the genes are dispersed in low G+C sequence separated by large tracts of apparently noncoding sequence. Only 1.1% of the genome is spanned by exons, whereas 24% is in introns, with 75% of the genome being intergenic DNA. Duplications of segmental blocks, ranging in size up to chromosomal lengths, are abundant throughout the genome and reveal a complex evolutionary history. Comparative genomic analysis indicates vertebrate expansions of genes associated with neuronal function, with tissue-specific developmental regulation, and with the hemostasis and immune systems. DNA sequence comparisons between the consensus sequence and publicly funded genome data provided locations of 2.1 million single-nucleotide polymorphisms (SNPs). A random pair of human haploid genomes differed at a rate of 1 bp per 1250 on average, but there was marked heterogeneity in the level of polymorphism across the genome. Less than 1% of all SNPs resulted in variation in proteins, but the task of determining which SNPs have functional consequences remains an open challenge.

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The chloroplast proteome contains many proteins that are of unknown function and not predicted to localize to the chloroplast. Expression of nuclear-encoded chloroplast genes is regulated at multiple levels in a pathway-dependent context. The combined shotgun proteomics and RNA profiling approach is of high potential value to predict metabolic pathway prevalence and to define regulatory levels of gene expression on a pathway scale.

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Achieving high accuracy in orthology inference is essential for many comparative, evolutionary and functional genomic analyses, yet the true evolutionary history of genes is generally unknown and orthologs are used for very different applications across phyla, requiring different precision-recall trade-offs. As a result, it is difficult to assess the performance of orthology inference methods. here, we present a community effort to establish standards and an automated web-based service to facilitate orthology benchmarking. using this service, we characterize 15 well-established inference methods and resources on a battery of 20 different benchmarks. standardized benchmarking provides a way for users to identify the most effective methods for the problem at hand, sets a minimum requirement for new tools and resources, and guides the development of more accurate orthology inference methods.Evolutionarily related genes (homologs) across different species are often divided into gene pairs that originated through speciation events (orthologs) and gene pairs that originated through duplication events (paralogs) 1 . This distinction is useful in a broad range of contexts, including phylogenetic tree inference, genome annotation, comparative genomics and gene function prediction 2-4 . Accordingly, dozens of methods 5 and resources 6-8 for orthology inference have been developed.Because the true evolutionary history of genes is typically unknown, assessing the performance of these orthology inference methods is not straightforward. Several indirect approaches have been proposed. Based on the notion that orthologs tend to be functionally more similar than paralogs (a notion now referred to as the ortholog conjecture 9-12 ), Hulsen et al. 13 used several measures of functional conservation (coexpression levels, protein-protein interactions and protein domain conservation) to benchmark orthology inference methods. Chen et al. 14 proposed an unsupervised learning approach based on consensus among different orthology methods. Altenhoff and Dessimoz 15 introduced a phylogenetic benchmark measuring the concordance between gene trees reconstructed from putative orthologs and undisputed species trees. More recently, several 'gold standard' reference sets, either manually curated 16,17 or derived from trusted resources 18 , have been used as benchmarks. Finally, Dalquen et al. 19 used simulated genomes to assess orthology inference in the presence of varying amounts of duplication, lateral gene transfer and sequencing artifacts.This wide array of benchmarking approaches poses considerable challenges to developers and users of orthology methods. Conceptually, the choice of an appropriate benchmark strongly depends on the application at hand. Practically, most methods are not available as stand-alone programs and thus cannot easily be