Clive Gavin Brown

Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing

Human cancers often carry many somatically acquired genomic rearrangements, some of which may be implicated in cancer development. However, conventional strategies for characterizing rearrangements are laborious and low-throughput and have low sensitivity or poor resolution. We used massively parallel sequencing to generate sequence reads from both ends of short DNA fragments derived from the genomes of two individuals with lung cancer. By investigating read pairs that did not align correctly with respect to each other on the reference human genome, we characterized 306 germline structural variants and 103 somatic rearrangements to the base-pair level of resolution. The patterns of germline and somatic rearrangement were markedly different. Many somatic rearrangements were from amplicons, although rearrangements outside these regions, notably including tandem duplications, were also observed. Some somatic rearrangements led to abnormal transcripts, including two from internal tandem duplications and two fusion transcripts created by interchromosomal rearrangements. Germline variants were predominantly mediated by retrotransposition, often involving AluY and LINE elements. The results demonstrate the feasibility of systematic, genome-wide characterization of rearrangements in complex human cancer genomes, raising the prospect of a new harvest of genes associated with cancer using this strategy.

Swift: primary data analysis for the Illumina Solexa sequencing platform

Motivation: Primary data analysis methods are of critical importance in second generation DNA sequencing. Improved methods have the potential to increase yield and reduce the error rates. Openly documented analysis tools enable the user to understand the primary data, this is important for the optimization and validity of their scientific work. Results: In this article, we describe Swift, a new tool for performing primary data analysis on the Illumina Solexa Sequencing Platform. Swift is the first tool, outside of the vendors own software, which completes the full analysis process, from raw images through to base calls. As such it provides an alternative to, and independent validation of, the vendor supplied tool. Our results show that Swift is able to increase yield by 13.8%, at comparable error rate. Availability and Implementation: Swift is implemented in C++and supported under Linux. It is supplied under an open source license (LGPL3), allowing researchers to build upon the platform. Swift is available from http://swiftng.sourceforge.net. Contact: new@sgenomics.org; nava.whiteford@nanoporetech.com Supplementary information: Supplementary data are available at Bioinformatics online.

Toward clinical proteomics on a next-generation sequencing platform.

We report the first next generation sequencing (NGS) application to identify and quantify proteins. Customization of protein specific aptamers enabled direct conversion of serum protein information into NGS read outs. The intrinsic ability of aptamer sequencing to highly multiplex protein detection and quantification, together with the prospect of DNA sequencing further evolving into a commodity technology, could constitute the core of a novel, universal diagnostics paradigm.

In vivo development of Theileria annulata : major changes in efferent lymph following infection with sporozoites or allogeneic schizont-infected mononuclear cells

The object of these experiments was to study the pathogenesis and kinetics of Theileria annulata infection in the efferent lymph of the draining lymph nodes of calves. Efferent lymphatics of calves were cannulated prior to infection with T. annulata sporozoite or an allogeneic schizont cell line. Potentially lethal sporozoite challenge induced cell shut-down from days 4-6 and then a massive increase in output of blasting cells (both infected and non-infected) in the efferent lymph. The rate of lymph flow and total cell output increased to 5 to 10-fold from day 6 onwards. Sporozoites were never isolated from the efferent lymph. However, large numbers of parasite-infected cells were seen in efferent lymph from the sixth day of infection. The animals inoculated with an allogeneic T. annulata-infected cell line exhibited only a small increase in flow rate and cell output. Parasite-infected cells of recipient origin were seen in efferent lymph from day 11 onwards. However, cells of donor origin were never isolated either from efferent lymph or peripheral blood. Thus the parasite transferred from the inoculated donor cell line to the cells of the recipient before schizonts appeared in efferent lymph.

Exploratory analysis and error modeling of a sequencing technology

Next generation DNA sequencing methods have created an unprecedented leap in sequence data generation, thus novel computational tools and statistical models are required to optimize and assess the resulting data. In this report, we explore underlying causes of error for the Illumina Genome Analyzer (IGA) sequencing technology and attempt to quantify their effects using a human bacterial artificial chromosome sequenced to 60,000 fold coverage. Seven potential error predictors are considered: Phred score, read entropy, tile coordinates, local tile density, base position within read, nucleotide call, and lane. With these parameters, logistic regression and log-linear models are constructed and used to show that each of the potential predictors contributes to error (P<1×10−4). With this additional information, we apply the logistic model and achieve a 3% improvement in both the sensitivity and specificity to detect IGA errors. Further, we demonstrate that these modeling approaches can be used as a feedback loop to inform laboratory methods and identify specific machine or run bias.