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.

BackgroundWhole exome capture sequencing allows researchers to cost-effectively sequence the coding regions of the genome. Although the exome capture sequencing methods have become routine and well established, there is currently a lack of tools specialized for variant calling in this type of data.ResultsUsing statistical models trained on validated whole-exome capture sequencing data, the Atlas2 Suite is an integrative variant analysis pipeline optimized for variant discovery on all three of the widely used next generation sequencing platforms (SOLiD, Illumina, and Roche 454). The suite employs logistic regression models in conjunction with user-adjustable cutoffs to accurately separate true SNPs and INDELs from sequencing and mapping errors with high sensitivity (96.7%).ConclusionWe have implemented the Atlas2 Suite and applied it to 92 whole exome samples from the 1000 Genomes Project. The Atlas2 Suite is available for download at http://sourceforge.net/projects/atlas2/. In addition to a command line version, the suite has been integrated into the Genboree Workbench, allowing biomedical scientists with minimal informatics expertise to remotely call, view, and further analyze variants through a simple web interface. The existing genomic databases displayed via the Genboree browser also streamline the process from variant discovery to functional genomics analysis, resulting in an off-the-shelf toolkit for the broader community.

BackgroundRare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency.ResultsThe 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants.ConclusionsThis study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variation.

BackgroundUntil recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues.ResultsWe successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set.ConclusionsWe find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms.

BackgroundIdentifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls.ResultsThis consensus exome INDEL call set features 7,210 INDELs, from 1,128 individuals across 13 populations included in the 1000 Genomes Phase 1 dataset, with a false discovery rate (FDR) of about 7.0%.ConclusionsIn our study we further characterize the patterns and distributions of these exonic INDELs with respect to density, allele length, and site frequency spectrum, as well as the potential mutagenic mechanisms of coding INDELs in humans.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1333-7) contains supplementary material, which is available to authorized users.

Abstract--Until recently, sequencing has primarily been carried out in large genome centers who also invested heavily in developing the computational infrastructure to enable post sequencing analysis. The recent advancements in sequencing technologies have lead to a wide dissemination of sequencing and we are now seeing many sequencing projects being undertaken in small laboratories. However, the limited accessibility to the computational infrastructure and high quality bioinformatic tools needed to enable analysis remains a serious road-block. The cloud computing and Software-as-a-Service (SaaS) technologies can help address this barrier. We deploy the Atlas2 Cloud Pipeline for personal genome analysis via the Genboree Workbench using software-as-a-service model. We report on a successful case study of personal genome analysis using this pipeline. BACKGROUNDThe revolutionary development of massively parallel DNA sequencing has enabled identification of biomedically relevant genomic variants via whole genome [1] or exome resequencing [2]. Information relevant for personalized medicine such as assessment of longitudinal disease risks, and personalized treatment [3] are now within reach. In a few very recent personal genomic studies, results have directly led to targeted treatment and dramatic improve- [4]. These examples are paving the way to soon turn genomic sequencing into a routine diagnostic procedure and to enable personalized medicine. Currently, analysis of sequencing data on a genomic scale requires bioinformatic expertise and access to extensive computational resources, presenting a significant barrier. Most cutting-edge genome analysis applications [5,6] are still limited to a command line interface and require at least moderate informatics expertise to operate. In addition, large scale genomic data analysis requires routine access to a high performance compute cluster. Such requirements are entirely unsuitable for the operational models of smaller research/diagnostic laboratories due to the excessive investment requirements of computing infrastructure and personnel. The deployment of genomic analysis software as a service within a cloud computing framework offers a unique solution for these problems. The concept behind cloud computing is to outsource computation to third-party servers or clusters at a remote location. This allows small laboratories to take advantage of external computational resources without having to maintain an in-house compute cluster. This software as a service model removes the upfront investment requirement and any delays associated with building local computing infrastructure. Earlier solutions involved providing access to analysis pipeline on a large cloud service provider like Amazon EC2 [7]. Though this solution eliminates the need for access to large compute clusters, the user would still need to have considerable bioinformatic skills due to the steep learning curve involved in working with clusters.