Genome sequencing and analysis of the Tasmanian devil and its transmissible cancer.

The Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations.

Canvas SPW: calling de novo copy number variants in pedigrees.

Motivation: Whole genome sequencing is becoming a diagnostics of choice for the identification of rare inherited and de novo copy number variants in families with various pediatric and late-onset genetic diseases. However, joint variant calling in pedigrees is hampered by the complexity of consensus breakpoint alignment across samples within an arbitrary pedigree structure. Results: We have developed a new tool, Canvas SPW, for the identification of inherited and de novo copy number variants from pedigree sequencing data. Canvas SPW supports a number of family structures and provides a wide range of scoring and filtering options to automate and streamline identification of de novo variants. Availability and implementation: Canvas SPW is available for download from https://github.com/Illumina/canvas. Contact: sivakhno@illumina.com. Supplementary information: Supplementary data are available at Bioinformatics online.

tHapMix: simulating tumour samples through haplotype mixtures.

MOTIVATION: Large-scale rearrangements and copy number changes combined with different modes of clonal evolution create extensive somatic genome diversity, making it difficult to develop versatile and scalable variant calling tools and create well-calibrated benchmarks. RESULTS: We developed a new simulation framework tHapMix that enables the creation of tumour samples with different ploidy, purity and polyclonality features. It easily scales to simulation of hundreds of somatic genomes, while re-use of real read data preserves noise and biases present in sequencing platforms. We further demonstrate tHapMix utility by creating a simulated set of 140 somatic genomes and showing how it can be used in training and testing of somatic copy number variant calling tools. AVAILABILITY AND IMPLEMENTATION: tHapMix is distributed under an open source license and can be downloaded from https://github.com/Illumina/tHapMix CONTACT: sivakhno@illumina.comSupplementary information: Supplementary data are available at Bioinformatics online.

GO-At: in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data.

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO-At (gene ontology prediction in A. thaliana), a method that combines five data types (co-expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two-step approach, GO-At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the 'best hit' from the first list, and achieves success rates of up to 79%. GO-At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO-At's ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi-associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web-based implementation of GO-At (http://www.bioinformatics.leeds.ac.uk/goat) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.

Small RNA analysis in Petunia hybrida identifies unusual tissue-specific expression patterns of conserved miRNAs and of a 24mer RNA.

Two pools of small RNAs were cloned from inflorescences of Petunia hybrida using a 5'-ligation dependent and a 5'-ligation independent approach. The two libraries were integrated into a public website that allows the screening of individual sequences against 359,769 unique clones. The library contains 15 clones with 100% identity and 53 clones with one mismatch to miRNAs described for other plant species. For two conserved miRNAs, miR159 and miR390, we find clear differences in tissue-specific distribution, compared with other species. This shows that evolutionary conservation of miRNA sequences does not necessarily include a conservation of the miRNA expression profile. Almost 60% of all clones in the database are 24-nucleotide clones. In accordance with the role of 24mers in marking repetitive regions, we find them distributed across retroviral and transposable element sequences but other 24mers map to promoter regions and to different transcript regions. For one target region we observe tissue-specific variation of matching 24mers, which demonstrates that, as for 21mers, 24mer concentrations are not necessarily identical in different tissues. Asymmetric distribution of a putative novel miRNA in the two libraries suggests that the cloning method can be selective for the representation of certain small RNAs in a collection.

Gene function prediction using semantic similarity clustering and enrichment analysis in the malaria parasite Plasmodium falciparum.

MOTIVATION: Functional genomics data provides a rich source of information that can be used in the annotation of the thousands of genes of unknown function found in most sequenced genomes. However, previous gene function prediction programs are mostly produced for relatively well-annotated organisms that often have a large amount of functional genomics data. Here, we present a novel method for predicting gene function that uses clustering of genes by semantic similarity, a naïve Bayes classifier and 'enrichment analysis' to predict gene function for a genome that is less well annotated but does has a severe effect on human health, that of the malaria parasite Plasmodium falciparum. RESULTS: Predictions for the molecular function, biological process and cellular component of P.falciparum genes were created from eight different datasets with a combined prediction also being produced. The high-confidence predictions produced by the combined prediction were compared to those produced by a simple K-nearest neighbour classifier approach and were shown to improve accuracy and coverage. Finally, two case studies are described, which investigate two biological processes in more detail, that of translation initiation and invasion of the host cell. AVAILABILITY: Predictions produced are available at http://www.bioinformatics.leeds.ac.uk/∼bio5pmrt/PAGODA.

PlasmoPredict: a gene function prediction website for Plasmodium falciparum.

The genome sequence of the malaria parasite Plasmodium falciparum was published in 2002 and revealed that approximately 60% of its genes could not be assigned a function. Eight years later the majority of P. falciparum proteins are still of unknown function. We therefore present PlasmoPredict, an easy-to-use online gene function prediction tool that integrates a wide range of functional genomics data for P. falciparum to aid in the annotation of these genes.

Endonuclease-sensitive regions of human spermatozoal chromatin are highly enriched in promoter and CTCF binding sequences.

During the haploid phase of mammalian spermatogenesis, nucleosomal chromatin is ultimately repackaged by small, highly basic protamines to generate an extremely compact, toroidal chromatin architecture that is critical to normal spermatozoal function. In common with several species, however, the human spermatozoon retains a small proportion of its chromatin packaged in nucleosomes. As nucleosomal chromatin in spermatozoa is structurally more open than protamine-packaged chromatin, we considered it likely to be more accessible to exogenously applied endonucleases. Accordingly, we have used this premise to identify a population of endonuclease-sensitive DNA sequences in human and murine spermatozoa. Our results show unequivocally that, in contrast to the endonuclease-resistant sperm chromatin packaged by protamines, regions of increased endonuclease sensitivity are closely associated with gene regulatory regions, including many promoter sequences and sequences recognized by CCCTC-binding factor (CTCF). Similar differential packaging of promoters is observed in the spermatozoal chromatin of both mouse and man. These observations imply the existence of epigenetic marks that distinguish gene regulatory regions in male germ cells and prevent their repackaging by protamines during spermiogenesis. The ontology of genes under the control of endonuclease-sensitive regulatory regions implies a role for this phenomenon in subsequent embryonic development.