The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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.

Supplementary data are available at Bioinformatics online.

We describe an algorithm, SSAHA (Sequence Search and Alignment by Hashing Algorithm), for performing fast searches on databases containing multiple gigabases of DNA. Sequences in the database are preprocessed by breaking them into consecutive k-tuples of k contiguous bases and then using a hash table to store the position of each occurrence of each k-tuple. Searching for a query sequence in the database is done by obtaining from the hash table the "hits" for each k-tuple in the query sequence and then performing a sort on the results. We discuss the effect of the tuple length k on the search speed, memory usage, and sensitivity of the algorithm and present the results of computational experiments which show that SSAHA can be three to four orders of magnitude faster than BLAST or FASTA, while requiring less memory than suffix tree methods. The SSAHA algorithm is used for high-throughput single nucleotide polymorphism (SNP) detection and very large scale sequence assembly. Also, it provides Web-based sequence search facilities for Ensembl projects.There is an extensive literature on the topic of DNA and protein sequence search and alignment, but as the quantity of available sequence information continues to multiply the problem remains very much relevant today. The pre-eminent class of methods for aligning two sequences are those based on dynamic programming, a technique first brought to bear on the problem by Needleman and Wunsch (1970) and subject to many later refinements, most notably by Smith and Waterman (1981).The need for greater speed, especially when searching for matches within a large database of subject sequences, has led to the development of other software tools such as FASTA (Lipman and Pearson 1985;Pearson and Lipman 1988), BLAST (Altschul et al. 1990, 1997 and Cross_match (P. Green, unpubl.; see http://www.phrap.com). In each case a preliminary word-based search stage identifies likely candidate matches, which are then extended to full alignments.In recent years, suffix tree algorithms have been gaining favor as methods for sequence search; an extensive treatment of the subject is given by Gusfield (1997). Delcher and coworkers (1999) describe a suffix-tree-based tool that is capable of aligning genome length sequences and also locating SNPs, large inserts, significant repeats, tandem repeats, and reversals. Suffix tree methods are attractive because of their potentially fast search speed, but they can devour a large amount of memory: Delcher et al. quote an upper bound of 37 bytes per base of the subject sequence for their suffix tree implementation. For a sequence comparable in size to the human genome (around three gigabases), one would require a machine with an exceptionally large amount of memory (at least by current standards) to fit the associated suffix tree entirely into RAM.In this paper we describe a DNA sequence search algorithm that is based on organizing the DNA database into a hash table data structure. Other hash table based search methods have been described by Waterman (1995, Ch...