Clinical applicability of methylome sequencing in a pilot study of ischemic stroke individuals

Despite recent progress in screening for genetic causes of cardiovascular disease using genome-wide association studies, identifying causative polymorphisms has not met initial expectations. This has led to interest in exploring the contribution of non-genetic factors in disease etiology. Elevated plasma homocysteine is an independent risk factor for cardiovascular disease but the mechanism for increased risk remains poorly understood. This study evaluates the clinical applicability of screening for genome-wide CpG methylation differences using methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq). Peripheral blood genomic DNA methylation in 8 Singaporean-Chinese ischemic stroke patients (4 male, 4 female) was profiled. Differential methylation of genes implicated in hyperhomocysteinemia was observed in males correlating with homocysteine; namely CBS (cystathionine-beta-synthase) and MTHFR (methylenetetrahydrofolate reductase). In females, hypomethylation of the LDLR (low density lipoprotein receptor) and CELSR1 (cadherin, EGF LAG seven-pass G-type receptor 1) genes were observed in the hypertensive group (2 normal and 2 hypertensive individuals). While the number of clinical samples analysed is small, the findings of this evaluation suggest that MBD-seq is a suitable and sufficiently sensitive technology to determine methylation variability. The results presented warrant an expanded case-control study to determine the pathophysiological implications of DNA methylation for hyper-homocysteinemia.

A Review of Cloud Computing Bioinformatics Solutions for Next-Gen Sequencing Data Analysis and Research

Research in biology has entered a digital era, where next-generation sequencing instruments generate multiple terabytes of data but are equipped with minimal computational and storage capacity that is not sufficient for large-scale, post-sequencing data analysis. Therefore, scientific value cannot be obtained from investment in a sequencing instrument, unless it is also combined with a significant expense for informatics infrastructure. An alternative option for laboratories is to outsource their informatics infrastructure, by leasing computational cycles and storage capacity from cloud computing services. Development of cloud-based bioinformatics tool suites can provide users with access to pre-configured software and on-demand computing resources for genomic data analysis, while at the same time lower the barrier for working with sequencing datasets, leading to broader adoption of genomic technologies for basic biological research. We conclude that along with the democratization of genome sequencing through the availability of lowcost, bench-top sequencers, cloud computing can in turn democratize access to computational capacity and informatics infrastructures required for sequencing data analysis.

An improved protocol for small RNA library construction using High Definition adapters

Next generation sequencing of small RNA (sRNA) libraries is widely used for studying sRNAs in various biological systems. However, cDNA libraries of sRNAs are biased for molecules that are ligated to adapters more or less efficiently than other molecules. One approach to reduce this ligation bias is to use a pool of adapters instead of a single adapter sequence, which allows many sRNAs to be ligated efficiently. We previously developed High Definition (HD) adapters for the Illumina sequencing platform, which contain degenerate nucleotides at the ligating ends of the adapters. However, the current commercial kits produced a large amount of 5’ adapter – 3’ adapter ligation product without the cDNA insert when HD adapters were used to replace the kit adapters. Here, we report a protocol to generate sRNA libraries using HD adapters with greatly reduced proportion of adapter-adapter products due to the degradation of nonligated 3’ adapters. The libraries can be completed within two days and can be used for various biological and clinical samples. As examples for using this protocol, we constructed sRNA libraries using total RNA extracted from cultured mammalian cells and plant leaf tissue.