Clare Sloggett

Tracking the origins and drivers of subclonal metastatic expansion in prostate cancer

Tumour heterogeneity in primary prostate cancer is a well-established phenomenon. However, how the subclonal diversity of tumours changes during metastasis and progression to lethality is poorly understood. Here we reveal the precise direction of metastatic spread across four lethal prostate cancer patients using whole-genome and ultra-deep targeted sequencing of longitudinally collected primary and metastatic tumours. We find one case of metastatic spread to the surgical bed causing local recurrence, and another case of cross-metastatic site seeding combining with dynamic remoulding of subclonal mixtures in response to therapy. By ultra-deep sequencing end-stage blood, we detect both metastatic and primary tumour clones, even years after removal of the prostate. Analysis of mutations associated with metastasis reveals an enrichment of TP53 mutations, and additional sequencing of metastases from 19 patients demonstrates that acquisition of TP53 mutations is linked with the expansion of subclones with metastatic potential which we can detect in the blood.

Colorectal cancer cell lines are representative models of the main molecular subtypes of primary cancer

Human colorectal cancer cell lines are used widely to investigate tumor biology, experimental therapy, and biomarkers. However, to what extent these established cell lines represent and maintain the genetic diversity of primary cancers is uncertain. In this study, we profiled 70 colorectal cancer cell lines for mutations and DNA copy number by whole-exome sequencing and SNP microarray analyses, respectively. Gene expression was defined using RNA-Seq. Cell line data were compared with those published for primary colorectal cancers in The Cancer Genome Atlas. Notably, we found that exome mutation and DNA copy-number spectra in colorectal cancer cell lines closely resembled those seen in primary colorectal tumors. Similarities included the presence of two hypermutation phenotypes, as defined by signatures for defective DNA mismatch repair and DNA polymerase ε proofreading deficiency, along with concordant mutation profiles in the broadly altered WNT, MAPK, PI3K, TGFβ, and p53 pathways. Furthermore, we documented mutations enriched in genes involved in chromatin remodeling (ARID1A, CHD6, and SRCAP) and histone methylation or acetylation (ASH1L, EP300, EP400, MLL2, MLL3, PRDM2, and TRRAP). Chromosomal instability was prevalent in nonhypermutated cases, with similar patterns of chromosomal gains and losses. Although paired cell lines derived from the same tumor exhibited considerable mutation and DNA copy-number differences, in silico simulations suggest that these differences mainly reflected a preexisting heterogeneity in the tumor cells. In conclusion, our results establish that human colorectal cancer lines are representative of the main subtypes of primary tumors at the genomic level, further validating their utility as tools to investigate colorectal cancer biology and drug responses.

BioBlend: automating pipeline analyses within Galaxy and CloudMan

UNLABELLED We present BioBlend, a unified API in a high-level language (python) that wraps the functionality of Galaxy and CloudMan APIs. BioBlend makes it easy for bioinformaticians to automate end-to-end large data analysis, from scratch, in a way that is highly accessible to collaborators, by allowing them to both provide the required infrastructure and automate complex analyses over large datasets within the familiar Galaxy environment. AVAILABILITY AND IMPLEMENTATION http://bioblend.readthedocs.org/. Automated installation of BioBlend is available via PyPI (e.g. pip install bioblend). Alternatively, the source code is available from the GitHub repository (https://github.com/afgane/bioblend) under the MIT open source license. The library has been tested and is working on Linux, Macintosh and Windows-based systems.

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

Background Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. Results We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. Conclusions This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.

Electron correlations in two-dimensional small quantum dots

We consider circular and elliptic quantum dots with parabolic external confinement, containing 0\char21{}22 electrons and with values of

Utility scripts for data manipulation in Galaxy

{r}_{s}

Electron correlations and the conductance of a quantum point contact at finite temperature

in the range

Community-driven development for computational biology at Sprints, Hackathons and Codefests.

0l{r}_{s}l3

Correlated electron current and temperature dependence of the conductance of a quantum point contact

. We perform restricted and unrestricted Hartree-Fock calculations, and further take into account electron correlations using second-order perturbation theory. We demonstrate that in many cases correlations qualitatively change the spin structure of the ground state from that obtained under Hartree-Fock and spin-density-functional calculations. In some cases the correlation effects destroy Hunds rule. We also demonstrate that the correlations destroy static spin-density waves observed in Hartree-Fock and spin-density-functional calculations.

Path integral Monte Carlo approach to the U(1) lattice gauge theory in 2+1 dimensions

Galaxy has an excellent system for the storage, indexing and handling of large sets of reference genome data that are required by various tools. This works particularly well for eukaryotic genomes such as human and mouse etc. However, in the case of microorganisms there are many thousands of reference genomes. These genomes are quite small and so are very easily indexed on the fly as required. We have developed refseq_to_library.py to take bacterial genomes and build galaxy data libraries of them by genus and/or species. The data libraries can be created on a local or remote machine easily. Another script directory_to_library.py takes an arbitrary directory structure and creates data libraries based upon it. These scripts can also control user permissions on the resultant libraries. The third script galaxy-fuse.py is a file system creation script that makes the Galaxy histories of a particular user available on the local file system in a matching directory structure. It uses the Galaxy users api key and bio-blend access to the Galaxy database to name the files.