The proliferation of large-scale DNA-sequencing projects in recent years has driven a search for alternative methods to reduce time and cost. Here we describe a scalable, highly parallel sequencing system with raw throughput significantly greater than that of state-of-the-art capillary electrophoresis instruments. The apparatus uses a novel fibre-optic slide of individual wells and is able to sequence 25 million bases, at 99% or better accuracy, in one four-hour run. To achieve an approximately 100-fold increase in throughput over current Sanger sequencing technology, we have developed an emulsion method for DNA amplification and an instrument for sequencing by synthesis using a pyrosequencing protocol optimized for solid support and picolitre-scale volumes. Here we show the utility, throughput, accuracy and robustness of this system by shotgun sequencing and de novo assembly of the Mycoplasma genitalium genome with 96% coverage at 99.96% accuracy in one run of the machine.DNA sequencing has markedly changed the nature of biomedical research and medicine. Reductions in the cost, complexity and time required to sequence large amounts of DNA, including improvements in the ability to sequence bacterial and eukaryotic genomes, will have significant scientific, economic and cultural impact. Largescale sequencing projects, including whole-genome sequencing, have usually required the cloning of DNA fragments into bacterial vectors, amplification and purification of individual templates, followed by Sanger sequencing 1 using fluorescent chain-terminating nucleotide analogues 2 and either slab gel or capillary electrophoresis. Current estimates put the cost of sequencing a human genome between $10 million and $25 million 3 . Alternative sequencing methods have been described 4-8 ; however, no technology has displaced the use of bacterial vectors and Sanger sequencing as the main generators of sequence information.Here we describe an integrated system whose throughput routinely enables applications requiring millions of bases of sequence information, including whole-genome sequencing. Our focus has been on the co-development of an emulsion-based method 9-11 to isolate and amplify DNA fragments in vitro, and of a fabricated substrate and instrument that performs pyrophosphate-based sequencing (pyrosequencing 5,12 ) in picolitre-sized wells.In a typical run we generate over 25 million bases with a Phred quality score of 20 or better (predicted to have an accuracy of 99% or higher). Although this Phred 20 quality throughput is significantly higher than that of Sanger sequencing by capillary electrophoresis, it is currently at the cost of substantially shorter reads and lower average individual read accuracy. Sanger-based capillary electrophoresis sequencing systems produce up to 700 bases of sequence information from each of 96 DNA templates at an average read accuracy of 99.4% in 1 h, or 67,000 bases per hour, with substantially all of the bases having Phred 20 or better quality 23 . We further characterize the performance ...

Drosophila melanogaster is a proven model system for many aspects of human biology. Here we present a two-hybrid-based protein-interaction map of the fly proteome. A total of 10,623 predicted transcripts were isolated and screened against standard and normalized complementary DNA libraries to produce a draft map of 7048 proteins and 20,405 interactions. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short-range organization, presumably corresponding to multiprotein complexes, and a more global organization, presumably corresponding to intercomplex connections. The network recapitulated known pathways, extended pathways, and uncovered previously unknown pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms, including humans.

DNA pooling is a practical way to reduce the cost of large-scale association studies to identify susceptibility loci for common diseases. Pooling allows allele frequencies in groups of individuals to be measured using far fewer PCR reactions and genotyping assays than are used when genotyping individuals. Here, we discuss recent developments in quantitative genotyping assays and in the design and analysis of pooling studies. Sophisticated pooling designs are being developed that can take account of hidden population stratification, confounders and inter-loci interactions, and that allow the analysis of haplotypes.

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We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.

The time correlation function for a harmonic quantum mechanical system can be related to the time correlation function for a corresponding classical system. Although straightforward to derive and well known in other contexts, this relationship has been unappreciated in the context of vibrational relaxation, where time correlation functions obtained from classical molecular dynamics have been used to predict relaxation rates for a quantum solute in a classical solvent. This inconsistent treatment—quantum solute, classical solvent—predicts a relaxation rate which is slower than if the entire system, both solute and solvent, were treated classically. We demonstrate that if the classical time correlation functions are rescaled to account for the ratio of quantum to classical fluctuations, providing a quantum mechanical treatment for the solute and the solvent, the relaxation rates and the entire absorption spectrum are the same as for a purely classical treatment. Our conclusions are valid when the solute and solvent can be described by a set of effective harmonic normal modes, and can also be valid when anharmonicities are present.

Study of mutant phenotypes is a fundamental method for understanding gene function. The construction of a near-complete collection of yeast knockouts (YKO) and the unique molecular barcodes (or TAGs) that identify each strain has enabled quantitative functional profiling of Saccharomyces cerevisiae. By using these TAGs and the SGA reporter, MFA1pr-HIS3, which facilitates conversion of heterozygous diploid YKO strains into haploid mutants, we have developed a set of highly efficient microarray-based techniques, collectively referred as dSLAM (diploid-based synthetic lethality analysis on microarrays), to probe genome-wide gene-chemical and gene-gene interactions. Direct comparison revealed that these techniques are more robust than existing methods in functional profiling of the yeast genome. Widespread application of these tools will elucidate a comprehensive yeast genetic network.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.