Showing papers by "Richard Durbin published in 2009"

The Sequence Alignment/Map format and SAMtools

Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: [email protected]

Fast and accurate short read alignment with Burrows–Wheeler transform

Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

Population genomics of domestic and wild yeasts

Abstract: Since the completion of the genome sequence of Saccharomyces cerevisiae in 1996 (refs 1, 2), there has been a large increase in complete genome sequences, accompanied by great advances in our understanding of genome evolution. Although little is known about the natural and life histories of yeasts in the wild, there are an increasing number of studies looking at ecological and geographic distributions, population structure and sexual versus asexual reproduction. Less well understood at the whole genome level are the evolutionary processes acting within populations and species that lead to adaptation to different environments, phenotypic differences and reproductive isolation. Here we present one- to fourfold or more coverage of the genome sequences of over seventy isolates of the baker's yeast S. cerevisiae and its closest relative, Saccharomyces paradoxus. We examine variation in gene content, single nucleotide polymorphisms, nucleotide insertions and deletions, copy numbers and transposable elements. We find that phenotypic variation broadly correlates with global genome-wide phylogenetic relationships. S. paradoxus populations are well delineated along geographic boundaries, whereas the variation among worldwide S. cerevisiae isolates shows less differentiation and is comparable to a single S. paradoxus population. Rather than one or two domestication events leading to the extant baker's yeasts, the population structure of S. cerevisiae consists of a few well-defined, geographically isolated lineages and many different mosaics of these lineages, supporting the idea that human influence provided the opportunity for cross-breeding and production of new combinations of pre-existing variations.

EnsemblCompara GeneTrees: Complete, duplication-aware phylogenetic trees in vertebrates

Abstract: The use of phylogenetic trees to describe the evolution of biological processes was established in the 1950s (Hennig 1952) and remains a fundamental approach to understanding the evolution of individual genes through to complete genomes; for example, in the mouse (Mouse Genome Sequencing Consortium 2002), rat (Gibbs et al. 2004), chicken (International Chicken Genome Sequencing Consortium 2004), and monodelphis (Mikkelsen et al. 2007) genome papers, and numerous papers on individual sequences. Now routine, the determination of vertebrate genome sequences provides a rich data source to understand evolution, and using phylogenetic trees of the genes is one of the best ways to organize these data. However, the increased set of genomes makes the compute and engineering tasks to form all the gene trees progressively more complex and harder for individual groups to use. The Ensembl project provides an accurate and consistent protein-coding gene set for all vertebrate genomes (International Human Genome Sequencing Consortium 2001; Dehal et al. 2002; Mouse Genome Sequencing Consortium 2002; Gibbs et al. 2004; Xie et al. 2005; Mikkelsen et al. 2007; Rhesus Macaque Genome Sequencing and Analysis Consortium 2007). Previously (until April 2006), Ensembl provided a basic method for tracing orthologs via the Best Reciprocal BLAST method, similar to approaches used in other genome analyses, such as Drosophila melanogaster (Adams et al. 2000) or human (International Human Genome Sequencing Consortium 2001). In June 2006 (Hubbard et al. 2007), we replaced this system with a phylogenetically sound, gene tree-based approach, providing a complete set of phylogenetic trees spanning 91% of genes across vertebrates. In addition to the vertebrates we have included a few important non-vertebrate species (fly, worm, and yeast) to act both as out groups and provide links to these model organisms. In this paper we provide the motivation, implementation, and benchmarking of this method and document the display and access methods for these trees. There have been a number of methods proposed for routine generation of genomewide orthology descriptions, including Inparanoid (Remm et al. 2001), MSOAR (Fu et al. 2007), OrthoMCL (Li et al. 2003), HomoloGene (Wheeler et al. 2008), TreeFam (Li et al. 2006), PhyOP (Goodstadt and Ponting 2006), and PhiGs (Dehal and Boore 2006). The first four, Inparanoid, MSOAR, OrthoMCL, and HomoloGene, focus on providing clusters (or linked clusters) of genes, without an explicit tree topology. PhyOP (Goodstadt and Ponting 2006) uses a tree-based method, but between pairs of closely related species, resolving paralogs accurately by using neutral substitution (as measured by d S, the synonymous substitution rate). TreeFam provides an explicit gene tree across multiple species, using both d S, d N (nonsynonymous substitution rate), nucleotide and protein distance measures, and the standard species tree to balance duplications vs. deletions to inform the tree construction, using the program TreeBeST (http://treesoft.sourceforge.net/treebest.shtml; L. Heng, A.J. Vilella, E. Birney, and R. Durbin, in prep.). The PhiGs method (Dehal and Boore 2006) is a leading phylogenetic-based method that produced a comprehensive phylogenetic resource for the genomes at the time it was run, and the basic outline of its analysis, which was clustering of protein sequences, followed by phylogenetic trees, is similar to the method presented here. However, the PhiGs resource covered a smaller number of species (23 vs. 45) and has been difficult to keep up to date with the advances in gene sets and genomes. Another major difference between PhiG-based phylogenetic trees and the phylogenetic trees presented here is that the former was calculated using a single maximum likelihood method based on protein evolution. In contrast, the Ensembl gene trees are calculated using a new method, TreeBeST, which integrates multiple tree topologies, in particular both DNA level and protein level models and combines this with a species-tree aware penalization of topologies, which are inconsistent with known species relationships. We show in this paper that this method produces trees that are more consistent with synteny relationships and less anomalous topologies than single protein-based phylogenetic methods. There are also many single phylogenetic tree-building approaches, many of them based on maximum likelihood methods; one leading method is PhyML (Guindon and Gascuel 2003). It is unclear what is the best method to use, in particular in the context of genome-wide tree building with constraints on computational costs and the need to robustly handle many complex scenarios usually involving large families with heterogeneous phylogenetic depths. In this paper, we benchmark in vertebrates the tree programs TreeBeST and PhyML, and the resulting trees to basic best reciprocal hit (BRH) methods, and cluster frameworks, in particular Inparanoid and HomoloGene. We also benchmark to a recent PhyOP data set. The PhyOP pipeline has recently switched to use the same tree-building program (TreeBeST) that we use, but differs in its input clusters. Although we adopted this same tree-building method, we describe here considerable novel engineering in the deployment of these methods across all vertebrates. Similar to the PhiGs resource, we have used the dense coverage of genomes to provide topologically based timings (i.e., the standard use of outgroups vs. subsequent lineages to bracket a duplication), in order to label duplication events.

The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes

Abstract: Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions.

Prepublication data sharing.

Abstract: Rapid release of prepublication data has served the field of genomics well. Attendees at a workshop in Toronto recommend extending the practice to other biological data sets.

Deep short-read sequencing of chromosome 17 from the mouse strains A/J and CAST/Ei identifies significant germline variation and candidate genes that regulate liver triglyceride levels

Abstract: Genome sequences are essential tools for comparative and mutational analyses. Here we present the short read sequence of mouse chromosome 17 from the Mus musculus domesticus derived strain A/J, and the Mus musculus castaneus derived strain CAST/Ei. We describe approaches for the accurate identification of nucleotide and structural variation in the genomes of vertebrate experimental organisms, and show how these techniques can be applied to help prioritize candidate genes within quantitative trait loci.