BackgroundHigh-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.ResultsWe demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences.ConclusionsThe Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

High-throughput RNA sequencing (RNA-seq) dramatically expands the potential for novel genomics discoveries, but the wide variety of platforms, protocols and performance has created the need for comprehensive reference data. Here we describe the Association of Biomolecular Resource Facilities next-generation sequencing (ABRF-NGS) study on RNA-seq. We tested replicate experiments across 15 laboratory sites using reference RNA standards to test four protocols (polyA-selected, ribo-depleted, size-selected and degraded) on five sequencing platforms (Illumina HiSeq, Life Technologies’ PGM and Proton, Pacific Biosciences RS and Roche’s 454). The results show high intra-platform and inter-platform concordance for expression measures across the deep-count platforms, but highly variable efficiency and cost for splice junction and variant detection between all platforms. These data also demonstrate that ribosomal RNA depletion can both enable effective analysis of degraded RNA samples and be readily compared to polyA-enriched fractions. This study provides a broad foundation for cross-platform standardization, evaluation and improvement of RNA-seq.

Endometriosis is a gynecological disease defined by the extrauterine growth of endometrial-like cells that cause chronic pain and infertility. The disease is limited to primates that exhibit spontaneous decidualization, and diseased cells are characterized by significant defects in the steroid-dependent genetic pathways that typify this process. Altered DNA methylation may underlie these defects, but few regions with differential methylation have been implicated in the disease. We mapped genome-wide differences in DNA methylation between healthy human endometrial and endometriotic stromal cells and correlated this with gene expression using an interaction analysis strategy. We identified 42,248 differentially methylated CpGs in endometriosis compared to healthy cells. These extensive differences were not unidirectional, but were focused intragenically and at sites distal to classic CpG islands where methylation status was typically negatively correlated with gene expression. Significant differences in methylation were mapped to 403 genes, which included a disproportionally large number of transcription factors. Furthermore, many of these genes are implicated in the pathology of endometriosis and decidualization. Our results tremendously improve the scope and resolution of differential methylation affecting the HOX gene clusters, nuclear receptor genes, and intriguingly the GATA family of transcription factors. Functional analysis of the GATA family revealed that GATA2 regulates key genes necessary for the hormone-driven differentiation of healthy stromal cells, but is hypermethylated and repressed in endometriotic cells. GATA6, which is hypomethylated and abundant in endometriotic cells, potently blocked hormone sensitivity, repressed GATA2, and induced markers of endometriosis when expressed in healthy endometrial cells. The unique epigenetic fingerprint in endometriosis suggests DNA methylation is an integral component of the disease, and identifies a novel role for the GATA family as key regulators of uterine physiology–aberrant DNA methylation in endometriotic cells correlates with a shift in GATA isoform expression that facilitates progesterone resistance and disease progression.

A major class of bacterial small, noncoding RNAs (sRNAs) acts by base-pairing with mRNAs to alter the translation from and/or stability of the transcript. Our laboratory has shown that Hfq, the chaperone that mediates the interaction of many sRNAs with their targets, is required for the virulence of the enteropathogen Yersinia pseudotuberculosis. This finding suggests that sRNAs play a critical role in the regulation of virulence in this pathogen, but these sRNAs are not known. Using a deep sequencing approach, we identified the global set of sRNAs expressed in vitro by Y. pseudotuberculosis. Sequencing of RNA libraries from bacteria grown at 26°C and 37°C resulted in the identification of 150 unannotated sRNAs. The majority of these sRNAs are Yersinia specific, without orthologs in either Escherichia coli or Salmonella typhimurium. Six sRNAs are Y. pseudotuberculosis specific and are absent from the genome of the closely related species Yersinia pestis. We found that the expression of many sRNAs conserved between Y. pseudotuberculosis and Y. pestis differs in both timing and dependence on Hfq, suggesting evolutionary changes in posttranscriptional regulation between these species. Deletion of multiple sRNAs in Y. pseudotuberculosis leads to attenuation of the pathogen in a mouse model of yersiniosis, as does the inactivation in Y. pestis of a conserved, Yersinia-specific sRNA in a mouse model of pneumonic plague. Finally, we determined the regulon controlled by one of these sRNAs, revealing potential virulence determinants in Y. pseudotuberculosis that are regulated in a posttranscriptional manner.Ysr29 | RybB | stress | Illumina-Solexa | 2D-DIGE