DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally employed long (400–800 bp) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intra-species genetic variation. We report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high quality sequence. We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from >30x average depth of paired 35-base reads. We characterise four million SNPs and four hundred thousand structural variants, many of which are previously unknown. Our approach is effective for accurate, rapid and economical whole genome re-sequencing and many other biomedical applications.

All cancers carry somatic mutations. A subset of these somatic alterations, termed driver mutations, confer selective growth advantage and are implicated in cancer development, whereas the remainder are passengers. Here we have sequenced the genomes of a malignant melanoma and a lymphoblastoid cell line from the same person, providing the first comprehensive catalogue of somatic mutations from an individual cancer. The catalogue provides remarkable insights into the forces that have shaped this cancer genome. The dominant mutational signature reflects DNA damage due to ultraviolet light exposure, a known risk factor for malignant melanoma, whereas the uneven distribution of mutations across the genome, with a lower prevalence in gene footprints, indicates that DNA repair has been preferentially deployed towards transcribed regions. The results illustrate the power of a cancer genome sequence to reveal traces of the DNA damage, repair, mutation and selection processes that were operative years before the cancer became symptomatic.

Background Mycobacterium tuberculosis complex (MTBC), the causative agent of tuberculosis (TB), is characterized by low sequence diversity making this bacterium one of the classical examples of a genetically monomorphic pathogen. Because of this limited DNA sequence variation, routine genotyping of clinical MTBC isolates for epidemiological purposes relies on highly discriminatory DNA fingerprinting methods based on mobile and repetitive genetic elements. According to the standard view, isolates exhibiting the same fingerprinting pattern are considered direct progeny of the same bacterial clone, and most likely reflect ongoing transmission or disease relapse within individual patients.Methodology/Principal FindingsHere we further investigated this assumption and used massively parallel whole-genome sequencing to compare one drug-susceptible (K-1) and one multidrug resistant (MDR) isolate (K-2) of a rapidly spreading M. tuberculosis Beijing genotype clone from a high incidence region (Karakalpakstan, Uzbekistan). Both isolates shared the same IS6110 RFLP pattern and the same allele at 23 out of 24 MIRU-VNTR loci.We generated 23.9 million (K-1) and 33.0 million (K-2) paired 50 bp purity filtered reads corresponding to a mean coverage of 483.5 fold and 656.1 fold respectively. Compared with the laboratory strain H37Rv both Beijing isolates shared 1,209 SNPs. The two Beijing isolates differed by 130 SNPs and one large deletion. The susceptible isolate had 55 specific SNPs, while the MDR variant had 75 specific SNPs, including the five known resistance-conferring mutations.ConclusionsOur results suggest that M. tuberculosis isolates exhibiting identical DNA fingerprinting patterns can harbour substantial genomic diversity. Because this heterogeneity is not captured by traditional genotyping of MTBC, some aspects of the transmission dynamics of tuberculosis could be missed or misinterpreted. Furthermore, a valid differentiation between disease relapse and exogenous reinfection might be impossible using standard genotyping tools if the overall diversity of circulating clones is limited. These findings have important implications for clinical trials of new anti-tuberculosis drugs.

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80-90% accurate in jackknife testing experiments for bacteria and 90-99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.