Brad Chapman

Biopython: freely available Python tools for computational molecular biology and bioinformatics.

Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/_Mailing_listspeter.cock@scri.ac.uk.

Unravelling angiosperm genome evolution by phylogenetic analysis of chromosomal duplication events

Conservation of gene order in vertebrates is evident after hundreds of millions of years of divergence, but comparisons of the Arabidopsis thaliana sequence to partial gene orders of other angiosperms (flowering plants) sharing common ancestry ∼170–235 million years ago yield conflicting results. This difference may be largely due to the propensity of angiosperms to undergo chromosomal duplication (‘polyploidization’) and subsequent gene loss (‘diploidization’); these evolutionary mechanisms have profound consequences for comparative biology. Here we integrate a phylogenetic approach (relating chromosomal duplications to the tree of life) with a genomic approach (mitigating information lost to diploidization) to show that a genome-wide duplication post-dates the divergence of Arabidopsis from most dicots. We also show that an inferred ancestral gene order for Arabidopsis reveals more synteny with other dicots (exemplified by cotton), and that additional, more ancient duplication events affect more distant taxonomic comparisons. By using partial sequence data for many diverse taxa to better relate the evolutionary history of completely sequenced genomes to the tree of life, we foster comparative approaches to the study of genome organization, consequences of polyploidy, and the molecular basis of quantitative traits.

Clonal dynamics of native haematopoiesis.

It is currently thought that life-long blood cell production is driven by the action of a small number of multipotent haematopoietic stem cells. Evidence supporting this view has been largely acquired through the use of functional assays involving transplantation. However, whether these mechanisms also govern native non-transplant haematopoiesis is entirely unclear. Here we have established a novel experimental model in mice where cells can be uniquely and genetically labelled in situ to address this question. Using this approach, we have performed longitudinal analyses of clonal dynamics in adult mice that reveal unprecedented features of native haematopoiesis. In contrast to what occurs following transplantation, steady-state blood production is maintained by the successive recruitment of thousands of clones, each with a minimal contribution to mature progeny. Our results demonstrate that a large number of long-lived progenitors, rather than classically defined haematopoietic stem cells, are the main drivers of steady-state haematopoiesis during most of adulthood. Our results also have implications for understanding the cellular origin of haematopoietic disease.

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

The genomic binding sites of a noncoding RNA

Long noncoding RNAs (lncRNAs) have important regulatory roles and can function at the level of chromatin. To determine where lncRNAs bind to chromatin, we developed capture hybridization analysis of RNA targets (CHART), a hybridization-based technique that specifically enriches endogenous RNAs along with their targets from reversibly cross-linked chromatin extracts. CHART was used to enrich the DNA and protein targets of endogenous lncRNAs from flies and humans. This analysis was extended to genome-wide mapping of roX2, a well-studied ncRNA involved in dosage compensation in Drosophila. CHART revealed that roX2 binds at specific genomic sites that coincide with the binding sites of proteins from the male-specific lethal complex that affects dosage compensation. These results reveal the genomic targets of roX2 and demonstrate how CHART can be used to study RNAs in a manner analogous to chromatin immunoprecipitation for proteins.

Galaxy CloudMan: delivering cloud compute clusters

BackgroundWidespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is “cloud computing”, which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate “as is” use by experimental biologists.ResultsWe present a cloud resource management system that makes it possible for individual researchers to compose and control an arbitrarily sized compute cluster on Amazon’s EC2 cloud infrastructure without any informatics requirements. Within this system, an entire suite of biological tools packaged by the NERC Bio-Linux team (http://nebc.nerc.ac.uk/tools/bio-linux) is available for immediate consumption. The provided solution makes it possible, using only a web browser, to create a completely configured compute cluster ready to perform analysis in less than five minutes. Moreover, we provide an automated method for building custom deployments of cloud resources. This approach promotes reproducibility of results and, if desired, allows individuals and labs to add or customize an otherwise available cloud system to better meet their needs.ConclusionsThe expected knowledge and associated effort with deploying a compute cluster in the Amazon EC2 cloud is not trivial. The solution presented in this paper eliminates these barriers, making it possible for researchers to deploy exactly the amount of computing power they need, combined with a wealth of existing analysis software, to handle the ongoing data deluge.

GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations

Modern DNA sequencing technologies enable geneticists to rapidly identify genetic variation among many human genomes. However, isolating the minority of variants underlying disease remains an important, yet formidable challenge for medical genetics. We have developed GEMINI (GEnome MINIng), a flexible software package for exploring all forms of human genetic variation. Unlike existing tools, GEMINI integrates genetic variation with a diverse and adaptable set of genome annotations (e.g., dbSNP, ENCODE, UCSC, ClinVar, KEGG) into a unified database to facilitate interpretation and data exploration. Whereas other methods provide an inflexible set of variant filters or prioritization methods, GEMINI allows researchers to compose complex queries based on sample genotypes, inheritance patterns, and both pre-installed and custom genome annotations. GEMINI also provides methods for ad hoc queries and data exploration, a simple programming interface for custom analyses that leverage the underlying database, and both command line and graphical tools for common analyses. We demonstrate GEMINIs utility for exploring variation in personal genomes and family based genetic studies, and illustrate its ability to scale to studies involving thousands of human samples. GEMINI is designed for reproducibility and flexibility and our goal is to provide researchers with a standard framework for medical genomics.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

BackgroundA steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure.ResultsCloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tools functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds.ConclusionsCloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Compaction of chromatin by diverse Polycomb group proteins requires localized regions of high charge

Polycomb group (PcG) proteins are required for the epigenetic maintenance of developmental genes in a silent state. Proteins in the Polycomb-repressive complex 1 (PRC1) class of the PcG are conserved from flies to humans and inhibit transcription. One hypothesis for PRC1 mechanism is that it compacts chromatin, based in part on electron microscopy experiments demonstrating that Drosophila PRC1 compacts nucleosomal arrays. We show that this function is conserved between Drosophila and mouse PRC1 complexes and requires a region with an overrepresentation of basic amino acids. While the active region is found in the Posterior Sex Combs (PSC) subunit in Drosophila, it is unexpectedly found in a different PRC1 subunit, a Polycomb homolog called M33, in mice. We provide experimental support for the general importance of a charged region by predicting the compacting capability of PcG proteins from species other than Drosophila and mice and by testing several of these proteins using solution assays and microscopy. We infer that the ability of PcG proteins to compact chromatin in vitro can be predicted by the presence of domains of high positive charge and that PRC1 components from a variety of species conserve this highly charged region. This supports the hypothesis that compaction is a key aspect of PcG function.

Targeted exome sequencing of suspected mitochondrial disorders

Objective: To evaluate the utility of targeted exome sequencing for the molecular diagnosis of mitochondrial disorders, which exhibit marked phenotypic and genetic heterogeneity. Methods: We considered a diverse set of 102 patients with suspected mitochondrial disorders based on clinical, biochemical, and/or molecular findings, and whose disease ranged from mild to severe, with varying age at onset. We sequenced the mitochondrial genome (mtDNA) and the exons of 1,598 nuclear-encoded genes implicated in mitochondrial biology, mitochondrial disease, or monogenic disorders with phenotypic overlap. We prioritized variants likely to underlie disease and established molecular diagnoses in accordance with current clinical genetic guidelines. Results: Targeted exome sequencing yielded molecular diagnoses in established disease loci in 22% of cases, including 17 of 18 (94%) with prior molecular diagnoses and 5 of 84 (6%) without. The 5 new diagnoses implicated 2 genes associated with canonical mitochondrial disorders (NDUFV1, POLG2), and 3 genes known to underlie other neurologic disorders (DPYD, KARS, WFS1), underscoring the phenotypic and biochemical overlap with other inborn errors. We prioritized variants in an additional 26 patients, including recessive, X-linked, and mtDNA variants that were enriched 2-fold over background and await further support of pathogenicity. In one case, we modeled patient mutations in yeast to provide evidence that recessive mutations in ATP5A1 can underlie combined respiratory chain deficiency. Conclusion: The results demonstrate that targeted exome sequencing is an effective alternative to the sequential testing of mtDNA and individual nuclear genes as part of the investigation of mitochondrial disease. Our study underscores the ongoing challenge of variant interpretation in the clinical setting.