Carcinogenesis is accompanied by widespread DNA methylation changes within the cell. These changes are characterized by a globally hypomethylated genome with focal hypermethylation of numerous 5’-cytosine-phosphate-guanine-3’ (CpG) islands, often spanning gene promoters and first exons. Many of these epigenetic changes occur early in tumorigenesis and are highly pervasive across a tumor type. This allows DNA methylation cancer biomarkers to be suitable for early detection and also to have utility across a range of areas relevant to cancer detection and treatment. Such tests are also simple in construction, as only one or a few loci need to be targeted for good test coverage. These properties make cancer-associated DNA methylation changes very attractive for development of cancer biomarker tests with substantive clinical utility. Across the patient journey from initial detection, to treatment and then monitoring, there are several points where DNA methylation assays can inform clinical practice. Assays on surgically removed tumor tissue are useful to determine indicators of treatment resistance, prognostication of outcome, or to molecularly characterize, classify, and determine the tissue of origin of a tumor. Cancer-associated DNA methylation changes can also be detected with accuracy in the cell-free DNA present in blood, stool, urine, and other biosamples. Such tests hold great promise for the development of simple, economical, and highly specific cancer detection tests suitable for population-wide screening, with several successfully translated examples already. The ability of circulating tumor DNA liquid biopsy assays to monitor cancer in situ also allows for the ability to monitor response to therapy, to detect minimal residual disease and as an early biomarker for cancer recurrence. This review will summarize existing DNA methylation cancer biomarkers used in clinical practice across the application domains above, discuss what makes a suitable DNA methylation cancer biomarker, and identify barriers to translation. We discuss technical factors such as the analytical performance and product-market fit, factors that contribute to successful downstream investment, including geography, and how this impacts intellectual property, regulatory hurdles, and the future of the marketplace and healthcare system.

Background: The development of colorectal cancer (CRC) is accompanied by extensive epigenetic changes, including frequent regional hypermethylation particularly of gene promoter regions. Specific genes, including SEPT9, VIM1 and TMEFF2 become methylated in a high fraction of cancers and diagnostic assays for detection of cancer-derived methylated DNA sequences in blood and/or fecal samples are being developed. There is considerable potential for the development of new DNA methylation biomarkers or panels to improve the sensitivity and specificity of current cancer detection tests. Methods: Combined epigenomic methods – activation of gene expression in CRC cell lines following DNA demethylating treatment, and two novel methods of genome-wide methylation assessment – were used to identify candidate genes methylated in a high fraction of CRCs. Multiplexed amplicon sequencing of PCR products from bisulfite-treated DNA of matched CRC and non-neoplastic tissue as well as healthy donor peripheral blood was performed using Roche 454 sequencing. Levels of DNA methylation in colorectal tissues and blood were determined by quantitative methylation specific PCR (qMSP). Results: Combined analyses identified 42 candidate genes for evaluation as DNA methylation biomarkers. DNA methylation profiles of 24 of these genes were characterised by multiplexed bisulfite-sequencing in ten matched tumor/normal tissue samples; differential methylation in CRC was confirmed for 23 of these genes. qMSP assays were developed for 32 genes, including 15 of the sequenced genes, and used to quantify methylation in tumor, adenoma and non-neoplastic colorectal tissue and from healthy donor peripheral blood. 24 of the 32 genes were methylated in >50% of neoplastic samples, including 11 genes that were methylated in 80% or more CRCs and a similar fraction of adenomas. Conclusions: This study has characterised a panel of 23 genes that show elevated DNA methylation in >50% of CRC tissue relative to non-neoplastic tissue. Six of these genes (SOX21, SLC6A15, NPY, GRASP, ST8SIA1 and ZSCAN18) show very low methylation in non-neoplastic colorectal tissue and are candidate biomarkers for stool-based assays, while 11 genes (BCAT1, COL4A2, DLX5, FGF5, FOXF1, FOXI2, GRASP, IKZF1, IRF4, SDC2 and SOX21) have very low methylation in peripheral blood DNA and are suitable for further evaluation as blood-based diagnostic markers.

Energy homeostasis plays a significant role in food consumption and body weight regulation with fat intake being an area of particular interest due to its palatability and high energy density. Increasing evidence from humans and animal studies indicate the existence of a taste modality responsive to fat via its breakdown product fatty acids. These studies implicate multiple candidate receptors and ion channels for fatty acid taste detection, indicating a complex peripheral physiology that is currently not well understood. Additionally, a limited number of studies suggest a reduced ability to detect fatty acids is associated with obesity and a diet high in fat reduces an individual's ability to detect fatty acids. To support this, genetic variants within candidate fatty acid receptors are also associated with obesity reduced ability to detect fatty acids. Understanding oral peripheral fatty acid transduction mechanisms and the association with fat consumption may provide the basis of novel approaches to control development of obesity.

Motivation: Bioinformatics tools, such as assemblers and aligners, are expected to produce more accurate results when given better quality sequence data as their starting point. This expectation has led to the development of stand-alone tools whose sole purpose is to detect and remove sequencing errors. A good error-correcting tool would be a transparent component in a bioinformatics pipeline, simply taking sequence data in any of the standard formats and producing a higher quality version of the same data containing far fewer errors. It should not only be able to correct all of the types of errors found in real sequence data (substitutions, insertions, deletions and uncalled bases), but it has to be both fast enough and scalable enough to be usable on the large datasets being produced by current sequencing technologies, and work on data derived from both haploid and diploid organisms. Results: This article presents Blue, an error-correction algorithm based on k-mer consensus and context. Blue can correct substitution, deletion and insertion errors, as well as uncalled bases. It accepts both FASTQ and FASTA formats, and corrects quality scores for corrected bases. Blue also maintains the pairing of reads, both within a file and between pairs of files, making it compatible with downstream tools that depend on read pairing. Blue is memory efficient, scalable and faster than other published tools, and usable on large sequencing datasets. On the tests undertaken, Blue also proved to be generally more accurate than other published algorithms, resulting in more accurately aligned reads and the assembly of longer contigs containing fewer errors. One significant feature of Blue is that its k-mer consensus table does not have to be derived from the set of reads being corrected. This decoupling makes it possible to correct one dataset, such as small set of 454 mate-pair reads, with the consensus derived from another dataset, such as Illumina reads derived from the same DNA sample. Such cross-correction can greatly improve the quality of small (and expensive) sets of long reads, leading to even better assemblies and higher quality finished genomes. Availability and implementation: The code for Blue and its related tools are available from http://www.bioinformatics.csiro.au/Blue . These programs are written in C# and run natively under Windows and under Mono on Linux. Contact:paul.greenfield@csiro.au Supplementary information: Supplementary data are available at Bioinformatics online.

Background: Batch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tests resulting in actual effects going unidentified. At their worst, they constitute confounds and render datasets useless. Attempting to remove batch effects will result in some of the biologically meaningful component of the measurement (i.e. signal) being lost. We present and benchmark a novel technique, calledHarman. Harman maximises the removal of batch noise with the constraint that the risk of also losing biologically meaningful component of the measurement is kept to a fraction which is set by the user.Results: Analyses of three independent publically available datasets reveal that Harman removes more batch noise and preserves more signal at the same time, than the current leading technique. Results also show that Harman is able to identify and remove batch effects no matter what their relative size compared to other sources of variation in the dataset. Of particular advantage for meta-analyses and data integration is Harman’s superior consistency in achieving comparable noise suppression - signal preservation trade-offs across multiple datasets, with differing number of treatments, replicates and processing batches.Conclusion: Harman’s ability to better remove batch noise, and better preserve biologically meaningful signal simultaneously within a single study, and maintain the user-set trade-off between batch noise rejection and signal preservation across different studies makes it an effective alternative method to deal with batch effects in high-throughput genomic datasets. Harman is flexible in terms of the data types it can process. It is available publically as an R package (https://bioconductor.org/packages/release/bioc/html/Harman.html), as well as a compiled Matlab package (http://www.bioinformatics.csiro.au/harman/) which does not require a Matlab license to run.

A growing number of studies in recent years have highlighted the importance of molecular nutrition as a potential determinant of health and disease. In particular, the ability of micronutrients to regulate the final expression of gene products via modulation of transcription and translation is now being recognised. Modulation of microRNA (miRNA) by nutrients is one pathway by which nutrition may mediate gene expression. miRNA, a class of non-coding RNA, can directly regulate gene expression post-transcriptionally. In addition, miRNA are able to indirectly influence gene expression potential at the transcriptional level via modulation of the function of components of the epigenetic machinery (DNA methylation and histone modifications). These mechanisms interact to form a complex, bi-directional regulatory circuit modulating gene expression. Disease-specific miRNA profiles have been identified in multiple disease states, including those with known dietary risk factors. Therefore, the role that nutritional components, in particular, vitamins and minerals, play in the modulation of miRNA profiles, and consequently health and disease, is increasingly being investigated, and as such is a timely subject for review. The recently posited potential for viable exogenous miRNA to enter human blood circulation from food sources adds another interesting dimension to the potential for dietary miRNA to contribute to gene modulation.