Summary: ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. Availability: Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art Contact:weichun.huang@nih.gov; gabor.marth@bc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. Methods: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. Results: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. Availability: The method is available at http://dir.niehs.nih.gov/microarray/datamining Contact: LI3@niehs.nih.gov

Oct4 and Sox2 are transcription factors required for pluripotency during early embryogenesis and for the maintenance of embryonic stem cell (ESC) identity. Functional mechanisms contributing to pluripotency are expected to be associated with genes transcriptionally activated by these factors. Here, we show that Oct4 and Sox2 bind to a conserved promoter region of miR-302, a cluster of eight microRNAs expressed specifically in ESCs and pluripotent cells. The expression of miR-302a is dependent on Oct4/Sox2 in human ESCs (hESCs), and miR-302a is expressed at the same developmental stages and in the same tissues as Oct4 during embryogenesis. miR-302a is predicted to target many cell cycle regulators, and the expression of miR-302a in primary and transformed cell lines promotes an increase in S-phase and a decrease in G₁-phase cells, reminiscent of an ESC-like cell cycle profile. Correspondingly, the inhibition of miR-302 causes hESCs to accumulate in G₁ phase. Moreover, we show that miR-302a represses the productive translation of an important G₁ regulator, cyclin D1, in hESCs. The transcriptional activation of miR-302 and the translational repression of its targets, such as cyclin D1, may provide a link between Oct4/Sox2 and cell cycle regulation in pluripotent cells.

The Illumina HumanMethylation450 BeadChip is increasingly utilized in epigenome-wide association studies, however, this array-based measurement of DNA methylation is subject to measurement variation. Appropriate data preprocessing to remove background noise is important for detecting the small changes that may be associated with disease. We developed a novel background correction method, ENmix, that uses a mixture of exponential and truncated normal distributions to flexibly model signal intensity and uses a truncated normal distribution to model background noise. Depending on data availability, we employ three approaches to estimate background normal distribution parameters using (i) internal chip negative controls, (ii) out-of-band Infinium I probe intensities or (iii) combined methylated and unmethylated intensities. We evaluate ENmix against other available methods for both reproducibility among duplicate samples and accuracy of methylation measurement among laboratory control samples. ENmix out-performed other background correction methods for both these measures and substantially reduced the probe-design type bias between Infinium I and II probes. In reanalysis of existing EWAS data we show that ENmix can identify additional CpGs, and results in smaller P-value estimates for previously-validated CpGs. We incorporated the method into R package ENmix, which is freely available from Bioconductor website.