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Toxoplasma gondii (T. gondii) is an obligate intracellular parasite that must monitor for changes in the host environment and respond accordingly; however, it is still not fully known which genetic or epigenetic factors are involved in regulating virulence traits of T. gondii. There are on-going efforts to elucidate the mechanisms regulating the stage transition process via the application of high-throughput epigenomics, genomics and proteomics techniques. Given the range of experimental conditions and the typical yield from such high-throughput techniques, a new challenge arises: how to effectively collect, organize and disseminate the generated data for subsequent data analysis. Here, we describe toxoMine, which provides a powerful interface to support sophisticated integrative exploration of high-throughput experimental data and metadata, providing researchers with a more tractable means toward understanding how genetic and/or epigenetic factors play a coordinated role in determining pathogenicity of T. gondii. As a data warehouse, toxoMine allows integration of high-throughput data sets with public T. gondii data. toxoMine is also able to execute complex queries involving multiple data sets with straightforward user interaction. Furthermore, toxoMine allows users to define their own parameters during the search process that gives users near-limitless search and query capabilities. The interoperability feature also allows users to query and examine data available in other InterMine systems, which would effectively augment the search scope beyond what is available to toxoMine. toxoMine complements the major community database ToxoDB by providing a data warehouse that enables more extensive integrative studies for T. gondii. Given all these factors, we believe it will become an indispensable resource to the greater infectious disease research community., (© The Author(s) 2015. Published by Oxford University Press.)

Motivation: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters., Results: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis., Contact: cathal.seoighe@nuigalway.ie, Supplementary Information: Supplementary data are available at Bioinformatics online.

Journal clubs are well regarded as a highly effective means of engaging graduate students with the contemporary research literature, where individual students prepare and deliver presentations on selected research articles to their peers, followed by a group discussion. Regular journal clubs have the advantage of enhancing student scientific reading, assessment and communication skills as well as developing a better understanding of the field. We developed a flipped journal club program as part of the one semester module 'Genomics Research Methods' with the goal of enhancing—and quantifying—individual student ability to engage with the genomics scientific literature. This involves all students and faculty reviewing a given manuscript, with the former submitting research relevant questions they would wish to ask the presenting student at the journal club, and the latter grading them. These questions are then ranked based on their median grade, and subsequently discussed in class. This cycle repeats weekly until all students have presented. Our analysis of question grade data over three consecutive years demonstrated clear improvements in student performance for all students between the start and end of the module. While no difference in performance was noted based on gender over the full semester, improvement in performance was significantly evident for the female cohort between the start and end of the module. Our results are consistent with module survey feedback of overall reported enhanced research self‐efficacy. This demonstrates that this flipped journal club implementation is a highly effective means of both assessing and improving individual student learning in genomics research ability. The involvement of the teaching faculty furthermore offers a means to foster a dynamic research community for all participants involved. This methodology is easily transferable to other bioscience graduate/undergraduate programs seeking to effectively teach essential research ability skills and enhance student self‐efficacy. [ABSTRACT FROM AUTHOR]

Massively-parallel sequencing (MPS) technologies and their diverse applications in genomics and epigenomics research have yielded enormous new insights into the physiology and pathophysiology of the human genome. The biggest hurdle remains the magnitude and diversity of the datasets generated, compromising our ability to manage, organize, process and ultimately analyse data. The Wiki-based Automated Sequence Processor (WASP), developed at the Albert Einstein College of Medicine (hereafter Einstein), uniquely manages to tightly couple the sequencing platform, the sequencing assay, sample metadata and the automated workflows deployed on a heterogeneous high performance computing cluster infrastructure that yield sequenced, quality-controlled and 'mapped' sequence data, all within the one operating environment accessible by a web-based GUI interface. WASP at Einstein processes 4-6 TB of data per week and since its production cycle commenced it has processed ~ 1 PB of data overall and has revolutionized user interactivity with these new genomic technologies, who remain blissfully unaware of the data storage, management and most importantly processing services they request. The abstraction of such computational complexity for the user in effect makes WASP an ideal middleware solution, and an appropriate basis for the development of a grid-enabled resource - the Einstein Genome Gateway - as part of the Extreme Science and Engineering Discovery Environment (XSEDE) program. In this paper we discuss the existing WASP system, its proposed middleware role, and its planned interaction with XSEDE to form the Einstein Genome Gateway. [ABSTRACT FROM AUTHOR]

Background: Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. Results: This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. Conclusions: While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods. [ABSTRACT FROM AUTHOR]

Abstract: The challenges associated with the management, analysis and interpretation of assays based on massively-parallel sequencing (MPS) are both individually complex and numerous. We describe what we believe to be the appropriate solution, one that represents a departure from traditional computational biology approaches. The Wasp System is an open source, distributed package written in Spring/J2EE that creates a foundation for development of an end-to-end solution for MPS-based experiments or clinical tests. Recognizing that one group will be unable to solve these challenges in isolation, we describe a nurtured open source development model that will allow the software to be collectively used, shared and developed. The ultimate goal is to emulate resources such as the Virtual Observatory of the astrophysics community, enabling computationally-inexpert scientists and clinicians to explore and interpret their MPS data. Here we describe the current implementation and development of the Wasp System and the roadmap for its community development. [Copyright &y& Elsevier]