"Same difference": comprehensive evaluation of four DNA methylation measurement platforms.

BACKGROUND: DNA methylation in CpG context is fundamental to the epigenetic regulation of gene expression in higher eukaryotes. Changes in methylation patterns are implicated in many diseases, cellular differentiation, imprinting, and other biological processes. Techniques that enrich for biologically relevant genomic regions with high CpG content are desired, since, depending on the size of an organism's methylome, the depth of sequencing required to cover all CpGs can be prohibitively expensive. Currently, restriction enzyme-based reduced representation bisulfite sequencing and its modified protocols are widely used to study methylation differences. Recently, Agilent Technologies, Roche NimbleGen, and Illumina have ventured to both reduce sequencing costs and capture CpGs of known biological relevance by marketing in-solution custom-capture hybridization platforms. We aimed to evaluate the similarities and differences of these four methods considering each platform targets approximately 10-13% of the human methylome. RESULTS: Overall, the regions covered per platform were as expected: targeted capture-based methods covered > 95% of their designed regions, whereas the restriction enzyme-based method covered > 70% of the expected fragments. While the total number of CpG loci shared by all methods was low, ~ 24% of any platform, the methylation levels of CpGs covered by all platforms were concordant. Annotation of CpG loci with genomic features revealed roughly the same proportions of feature annotations across the four platforms. Targeted capture methods comprise similar types and coverage of annotations and, relative to the targeted methods, the restriction enzyme method covers fewer promoters (~ 9%), CpG shores (~ 8%) and unannotated loci (~ 11%). CONCLUSIONS: Although all methods are largely consistent in terms of covered CpG loci, the commercially available capture methods result in covering nearly all CpG sites in their target regions with few off-target loci and covering similar proportions of annotated CpG loci, the restriction-based enrichment results in more off-target and unannotated CpG loci. Quality of DNA is very important for restriction-based enrichment and starting material can be low. Conversely, quality of the starting material is less important for capture methods, and at least twice the amount of starting material is required. Pricing is marginally less for restriction-based enrichment, and the number of samples that can be prepared is not restricted to the number of capture reactions a kit supports. However, the advantage of capture libraries is the ability to custom design areas of interest. The choice of the technique would be decided by the number of samples, the quality and quantity of DNA available and the biological areas of interest since comparable data are obtained from all platforms.

CryoPause: A New Method to Immediately Initiate Experiments after Cryopreservation of Pluripotent Stem Cells.

Human pluripotent stem cells (PSCs) provide an unlimited cell source for cell therapies and disease modeling. Despite their enormous power, technical aspects have hampered reproducibility. Here, we describe a modification of PSC workflows that eliminates a major variable for nearly all PSC experiments: the quality and quantity of the PSC starting material. Most labs continually passage PSCs and use small quantities after expansion, but the "just-in-time" nature of these experiments means that quality control rarely happens before use. Lack of quality control could compromise PSC quality, sterility, and genetic integrity, which creates a variable that might affect results. This method, called CryoPause, banks PSCs as single-use, cryopreserved vials that can be thawed and immediately used in experiments. Each CryoPause bank provides a consistent source of PSCs that can be pre-validated before use to reduce the possibility that high levels of spontaneous differentiation, contamination, or genetic integrity will compromise an experiment.

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.

Multiplexing of ChIP-Seq Samples in an Optimized Experimental Condition Has Minimal Impact on Peak Detection.

Multiplexing samples in sequencing experiments is a common approach to maximize information yield while minimizing cost. In most cases the number of samples that are multiplexed is determined by financial consideration or experimental convenience, with limited understanding on the effects on the experimental results. Here we set to examine the impact of multiplexing ChIP-seq experiments on the ability to identify a specific epigenetic modification. We performed peak detection analyses to determine the effects of multiplexing. These include false discovery rates, size, position and statistical significance of peak detection, and changes in gene annotation. We found that, for histone marker H3K4me3, one can multiplex up to 8 samples (7 IP + 1 input) at ~21 million single-end reads each and still detect over 90% of all peaks found when using a full lane for sample (~181 million reads). Furthermore, there are no variations introduced by indexing or lane batch effects and importantly there is no significant reduction in the number of genes with neighboring H3K4me3 peaks. We conclude that, for a well characterized antibody and, therefore, model IP condition, multiplexing 8 samples per lane is sufficient to capture most of the biological signal.

Enhanced reduced representation bisulfite sequencing for assessment of DNA methylation at base pair resolution.

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine methylation patterns in cells. Reduced representation of whole genome bisulfite sequencing was developed to detect quantitative base pair resolution cytosine methylation patterns at GC-rich genomic loci. This is accomplished by combining the use of a restriction enzyme followed by bisulfite conversion. Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) increases the biologically relevant genomic loci covered and has been used to profile cytosine methylation in DNA from human, mouse and other organisms. ERRBS initiates with restriction enzyme digestion of DNA to generate low molecular weight fragments for use in library preparation. These fragments are subjected to standard library construction for next generation sequencing. Bisulfite conversion of unmethylated cytosines prior to the final amplification step allows for quantitative base resolution of cytosine methylation levels in covered genomic loci. The protocol can be completed within four days. Despite low complexity in the first three bases sequenced, ERRBS libraries yield high quality data when using a designated sequencing control lane. Mapping and bioinformatics analysis is then performed and yields data that can be easily integrated with a variety of genome-wide platforms. ERRBS can utilize small input material quantities making it feasible to process human clinical samples and applicable in a range of research applications. The video produced demonstrates critical steps of the ERRBS protocol.

Comparative microbial modules resource: generation and visualization of multi-species biclusters.

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures - results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation.

Multi-species integrative biclustering.

We describe an algorithm, multi-species cMonkey, for the simultaneous biclustering of heterogeneous multiple-species data collections and apply the algorithm to a group of bacteria containing Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes. The algorithm reveals evolutionary insights into the surprisingly high degree of conservation of regulatory modules across these three species and allows data and insights from well-studied organisms to complement the analysis of related but less well studied organisms.