Type 2 diabetes mellitus (DM) is characterized by insulin resistance and pancreatic ␤ cell dysfunction. In high-risk subjects, the earliest detectable abnormality is insulin resistance in skeletal muscle. Impaired insulin-mediated signaling, gene expression, glycogen synthesis, and accumulation of intramyocellular triglycerides have all been linked with insulin resistance, but no specific defect responsible for insulin resistance and DM has been identified in humans. To identify genes potentially important in the pathogenesis of DM, we analyzed gene expression in skeletal muscle from healthy metabolically characterized nondiabetic (family history negative and positive for DM) and diabetic Mexican-American subjects. We demonstrate that insulin resistance and DM associate with reduced expression of multiple nuclear respiratory factor-1 (NRF-1)-dependent genes encoding key enzymes in oxidative metabolism and mitochondrial function. Although NRF-1 expression is decreased only in diabetic subjects, expression of both PPAR␥ coactivator 1-␣ and-␤ (PGC1-␣͞PPARGC1 and PGC1-␤͞PERC), coactivators of NRF-1 and PPAR␥-dependent transcription, is decreased in both diabetic subjects and family history-positive nondiabetic subjects. Decreased PGC1 expression may be responsible for decreased expression of NRF-dependent genes, leading to the metabolic disturbances characteristic of insulin resistance and DM.I nsulin resistance precedes and predicts the development of type 2 diabetes mellitus (DM) (1, 2). Defects in insulin signal transduction, gene expression, and muscle glycogen synthesis, and accumulation of intramyocellular triglycerides have all been identified as potential mediators of insulin resistance in high-risk individuals (1, 3-7). However, the molecular pathogenesis of DM remains unknown. Mouse data highlight the importance of glucose uptake into muscle but suggest a role for novel mechanisms, distinct from insulin signaling pathways (8). The importance of genetic risk factors is exemplified by the high concordance of DM in identical twins, the strong influence of family history and ethnicity on risk, and the identification of DNA sequence alterations in both rare and common forms of DM (9). Environmental factors, including obesity, inactivity, and aging, also play critical roles in DM risk. Because both genotype and environment converge to influence cellular function via gene and protein expression, we hypothesize that alterations in expression define a phenotype that parallels the metabolic evolution of DM and provides potential clues to pathogenesis. We used high-density oligonucleotide arrays to identify genes differentially expressed in skeletal muscle from nondiabetic and type 2 diabetic subjects. Because hyperglycemia per se can modulate expression, we also evaluated gene expression in insulin-resistant subjects at high risk for DM (''prediabetes'') on the basis of family history of DM and Mexican-American ethnicity (10). We demonstrate that prediabetic and diabetic muscle is characterized by decreased expressi...

Massively parallel DNA sequencing technologies are revolutionizing genomics by making it possible to generate billions of relatively short (∼100-base) sequence reads at very low cost. Whereas such data can be readily used for a wide range of biomedical applications, it has proven difficult to use them to generate high-quality de novo genome assemblies of large, repeat-rich vertebrate genomes. To date, the genome assemblies generated from such data have fallen far short of those obtained with the older (but much more expensive) capillary-based sequencing approach. Here, we report the development of an algorithm for genome assembly, ALLPATHS-LG, and its application to massively parallel DNA sequence data from the human and mouse genomes, generated on the Illumina platform. The resulting draft genome assemblies have good accuracy, short-range contiguity, long-range connectivity, and coverage of the genome. In particular, the base accuracy is high (≥99.95%) and the scaffold sizes (N50 size = 11.5 Mb for human and 7.2 Mb for mouse) approach those obtained with capillary-based sequencing. The combination of improved sequencing technology and improved computational methods should now make it possible to increase dramatically the de novo sequencing of large genomes. The ALLPATHS-LG program is available at http://www.broadinstitute.org/science/programs/ genome-biology/crd. T he high-quality assembly of a genome sequence is a critical foundation for understanding the biology of an organism, the genetic variation within a species, or the pathology of a tumor. High-quality assembly is particularly challenging for large, repeatrich genomes such as those of mammals. Among mammals, "finished" genome sequences have been completed for the human and the mouse (1, 2). However, for most large genomes, efforts have focused on using shotgun-sequencing data to produce highquality draft genome assemblies-with long-range contiguity in the range of 20-100 kb and long-range connectivity in the range of 10 Mb (e.g., refs. 3-5). Using traditional capillary-based sequencing, such assemblies have been produced for multiple mammals at a cost of tens of million dollars each.Recently, there has been a revolution in DNA sequencing technology. New massively parallel technologies can produce DNA sequence information at a per-base cost that is ∼100,000-fold lower than a decade ago (6, 7). In principle, this should make it possible to dramatically decrease the cost of generating highquality draft genome assemblies. In practice, however, this has been difficult because the new technology produces sequencing "reads" of only ∼100 bases in length (compared with >700 bases for capillary-based technology). These shorter reads are also less accurate. For both of these reasons, these data are more difficult to assemble into long contiguous and connected sequence. Excellent de novo assemblies using massively parallel sequence data have been reported for microbes with genomes up to 40 Mb (refs. 8-10 and many others). There have been some important pioneering e...

Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

BackgroundDNA sequencing technologies deviate from the ideal uniform distribution of reads. These biases impair scientific and medical applications. Accordingly, we have developed computational methods for discovering, describing and measuring bias.ResultsWe applied these methods to the Illumina, Ion Torrent, Pacific Biosciences and Complete Genomics sequencing platforms, using data from human and from a set of microbes with diverse base compositions. As in previous work, library construction conditions significantly influence sequencing bias. Pacific Biosciences coverage levels are the least biased, followed by Illumina, although all technologies exhibit error-rate biases in high- and low-GC regions and at long homopolymer runs. The GC-rich regions prone to low coverage include a number of human promoters, so we therefore catalog 1,000 that were exceptionally resistant to sequencing. Our results indicate that combining data from two technologies can reduce coverage bias if the biases in the component technologies are complementary and of similar magnitude. Analysis of Illumina data representing 120-fold coverage of a well-studied human sample reveals that 0.20% of the autosomal genome was covered at less than 10% of the genome-wide average. Excluding locations that were similar to known bias motifs or likely due to sample-reference variations left only 0.045% of the autosomal genome with unexplained poor coverage.ConclusionsThe assays presented in this paper provide a comprehensive view of sequencing bias, which can be used to drive laboratory improvements and to monitor production processes. Development guided by these assays should result in improved genome assemblies and better coverage of biologically important loci.