MMASS: an optimized array-based method for assessing CpG island methylation

We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.     

Bio-microarray fabrication techniques--a review

Microarrays with biomolecules (e.g., DNA and proteins), cells, and tissues immobilized on solid substrates are important tools for biological research, including genomics, proteomics, and cell analysis. In this paper, the current state of microarray fabrication is reviewed. According to spot formation techniques, methods are categorized as "contact printing" and "non-contact printing." Contact printing is a widely used technology, comprising methods such as contact pin printing and microstamping. These methods have many advantages, including reproducibility of printed spots and facile maintenance, as well as drawbacks, including low-throughput fabrication of arrays. Non-contact printing techniques are newer and more varied, comprising photochemistry-based methods, laser writing, electrospray deposition, and inkjet technologies. These technologies emerged from other applications and have the potential to increase microarray fabrication throughput; however, there are several challenges in applying them to microarray fabrication, including interference from satellite drops and biomolecule denaturization.     

A flexible Bayesian method for detecting allelic imbalance in RNA-seq data

Background

One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data.

Results

Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test.

Conclusions

To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-920) contains supplementary material, which is available to authorized users.     

DNA methylation: a profile of methods and applications

Ever since methylcytosine was found in genomic DNA, this epigenetic alteration has become a center of scientific attraction, especially because of its relation to gene silencing in disease. There is currently a wide range of methods designed to yield quantitative and qualitative information on genomic DNA methylation. The earliest approaches were concentrated on the study of overall levels of methylcytosine, but more recent efforts havefocused on the study ofthe methylation status of specific DNA sequences. Particularly, optimization of the methods based on bisulfite modification of DNA permits the analysis of limited CpGs in restriction enzyme sites (e.g., combined bisulfite restriction analyses and methylation-sensitive single nucleotide primer extension) and the overall characterization based on differential methylation states (e.g., methylation-specific PCR, MethyLight, and methylation-sensitive single-stranded conformational polymorphism) and allows very specific patterns of methylation to be revealed (bisulfite DNA sequencing). In addition, novel methods designed to search for new methylcytosine hot spots have yielded further data without requiring prior knowledge of the DNA sequence. We hope this review will be a valuable tool in selecting the best techniques to address particular questions concerning the cytosine methylation status of genomic DNA.     

Baggasse Preservation: A Need for a Biotechnological Approach

ABSTRACT

Microarrays with biomolecules (e.g., DNA and proteins), cells, and tissues immobilized on solid substrates are important tools for biological research, including genomics, proteomics, and cell analysis. In this paper, the current state of microarray fabrication is reviewed. According to spot formation techniques, methods are categorized as “contact printing” and “non-contact printing.” Contact printing is a widely used technology, comprising methods such as contact pin printing and microstamping. These methods have many advantages, including reproducibility of printed spots and facile maintenance, as well as drawbacks, including low-throughput fabrication of arrays. Non-contact printing techniques are newer and more varied, comprising photochemistry-based methods, laser writing, electrospray deposition, and inkjet technologies. These technologies emerged from other applications and have the potential to increase microarray fabrication throughput; however, there are several challenges in applying them to microarray fabrication, including interference from satellite drops and biomolecule denaturization.     

Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray

The Illumina Infinium HumanMethylation27 BeadChip (Illumina 27k) microarray is a high-throughput platform capable of interrogating the human DNA methylome. In a search for autosomal sex-specific DNA methylation using this microarray, we discovered autosomal CpG loci showing significant methylation differences between the sexes. However, we found that the majority of these probes cross-reacted with sequences from sex chromosomes. Moreover, we determined that 6-10% of the microarray probes are non-specific and map to highly homologous genomic sequences. Using probes targeting different CpGs that are exact duplicates of each other, we investigated the precision of these repeat measurements and concluded that the overall precision of this microarray is excellent. In addition, we identified a small number of probes targeting CpGs that include single-nucleotide polymorphisms. Overall, our findings address several technical issues associated with the Illumina 27k microarray that, once considered, will enhance the analysis and interpretation of data generated from this platform.     

A computational study of Shewanella oneidensis MR-1: structural prediction and functional inference of hypothetical proteins

The genomes of many organisms have been sequenced in the last 5 years. Typically about 30% of predicted genes from a newly sequenced genome cannot be given functional assignments using sequence comparison methods. In these situations three-dimensional structural predictions combined with a suite of computational tools can suggest possible functions for these hypothetical proteins. Suggesting functions may allow better interpretation of experimental data (e.g., microarray data and mass spectroscopy data) and help experimentalists design new experiments. In this paper, we focus on three hypothetical proteins of Shewanella oneidensis MR-1 that are potentially related to iron transport/metabolism based on microarray experiments. The threading program PROSPECT was used for protein structural predictions and functional annotation, in conjunction with literature search and other computational tools. Computational tools were used to perform transmembrane domain predictions, coiled coil predictions, signal peptide predictions, sub-cellular localization predictions, motif prediction, and operon structure evaluations. Combined computational results from all tools were used to predict roles for the hypothetical proteins. This method, which uses a suite of computational tools that are freely available to academic users, can be used to annotate hypothetical proteins in general.     

Inferring speciation rates from phylogenies

Abstract It is possible to estimate the rate of diversification of clades from phylogenies with a temporal dimension. First, I present several methods for constructing confidence intervals for the speciation rate under the simple assumption of a pure birth process. I discuss the relationships among these methods in the hope of clarifying some fundamental theory in this area. Their performances are compared in a simulation study and one is recommended for use as a result. A variety of other questions that may, in fact, be the questions of primary interest (e.g., Has the rate of cladogenesis been declining?) are then recast as biological variants of the purely statistical question—Is the birth process model appropriate for my data? Seen in this way, a preexisting arsenal of statistical techniques is opened up for use in this area: in particular, techniques developed for the analysis of Poisson processes and the analysis of survival data. These two approaches start from different representations of the data—the branch lengths in the tree—and I explicitly relate the two. Aiming for a synoptic account of useful theory in this area, I briefly discuss some important results from the analysis of two distinct birth‐death processes: the one introduced into this area by Hey (1992) is refitted with some powerful statistical tools.     

Gene discovery in neuropharmacological and behavioral studies using Affymetrix microarray data

We describe methods and software tools for doing data analysis based on Affymetrix microarray data, emphasizing often neglected issues. In our experience with neuroscience studies, experimental design and quality assessment are vital. We also describe in detail the pre-processing methods we have found useful for Affymetrix data. Finally, we summarize the statistical literature and describe some pitfalls in the post-processing analysis.     

Fingerprinting closely related xanthomonas pathovars with random nonamer oligonucleotide microarrays

Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.     

Quality control and statistical modeling for environmental epigenetics: A study on in utero lead exposure and DNA methylation at birth

DNA methylation data assayed using pyrosequencing techniques are increasingly being used in human cohort studies to investigate associations between epigenetic modifications at candidate genes and exposures to environmental toxicants and to examine environmentally-induced epigenetic alterations as a mechanism underlying observed toxicant-health outcome associations. For instance, in utero lead (Pb) exposure is a neurodevelopmental toxicant of global concern that has also been linked to altered growth in human epidemiological cohorts; a potential mechanism of this association is through alteration of DNA methylation (e.g., at growth-related genes). However, because the associations between toxicants and DNA methylation might be weak, using appropriate quality control and statistical methods is important to increase reliability and power of such studies. Using a simulation study, we compared potential approaches to estimate toxicant-DNA methylation associations that varied by how methylation data were analyzed (repeated measures vs. averaging all CpG sites) and by method to adjust for batch effects (batch controls vs. random effects). We demonstrate that correcting for batch effects using plate controls yields unbiased associations, and that explicitly modeling the CpG site-specific variances and correlations among CpG sites increases statistical power. Using the recommended approaches, we examined the association between DNA methylation (in LINE-1 and growth related genes IGF2, H19 and HSD11B2) and 3 biomarkers of Pb exposure (Pb concentrations in umbilical cord blood, maternal tibia, and maternal patella), among mother-infant pairs of the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort (n = 247). Those with 10 μg/g higher patella Pb had, on average, 0.61% higher IGF2 methylation (P = 0.05). Sex-specific trends between Pb and DNA methylation (P < 0.1) were observed among girls including a 0.23% increase in HSD11B2 methylation with 10 μg/g higher patella Pb.     

A comparison of existing global DNA methylation assays to low-coverage whole-genome bisulfite sequencing for epidemiological studies

DNA methylation is an epigenetic mark at the interface of genetic and environmental factors relevant to human disease. Quantitative assessments of global DNA methylation levels have therefore become important tools in epidemiology research, particularly for understanding effects of environmental exposures in complex diseases. Among the available methods of quantitative DNA methylation measurements, bisulfite sequencing is considered the gold standard, but whole-genome bisulfite sequencing (WGBS) has previously been considered too costly for epidemiology studies with high sample numbers. Pyrosequencing of repetitive sequences within bisulfite-treated DNA has been routinely used as a surrogate for global DNA methylation, but a comparison of pyrosequencing to WGBS for accuracy and reproducibility of methylation levels has not been performed. This study compared the global methylation levels measured from uniquely mappable (non-repetitive) WGBS sequences to pyrosequencing assays of several repeat sequences and repeat assay-matched WGBS data and determined uniquely mappable WGBS data to be the most reproducible and accurate measurement of global DNA methylation levels. We determined sources of variation in repetitive pyrosequencing assays to be PCR amplification bias, PCR primer selection bias in methylation levels of targeted sequences, and inherent variability in methylation levels of repeat sequences. Low-coverage, uniquely mappable WGBS showed the strongest correlation between replicates of all assays. By using multiplexing by indexed bar codes, the cost of WGBS can be lowered significantly to improve the accuracy of global DNA methylation assessments for human studies.     

Quality control and statistical modeling for environmental epigenetics: A study on in utero lead exposure and DNA methylation at birth

DNA methylation data assayed using pyrosequencing techniques are increasingly being used in human cohort studies to investigate associations between epigenetic modifications at candidate genes and exposures to environmental toxicants and to examine environmentally-induced epigenetic alterations as a mechanism underlying observed toxicant-health outcome associations. For instance, in utero lead (Pb) exposure is a neurodevelopmental toxicant of global concern that has also been linked to altered growth in human epidemiological cohorts; a potential mechanism of this association is through alteration of DNA methylation (e.g., at growth-related genes). However, because the associations between toxicants and DNA methylation might be weak, using appropriate quality control and statistical methods is important to increase reliability and power of such studies. Using a simulation study, we compared potential approaches to estimate toxicant-DNA methylation associations that varied by how methylation data were analyzed (repeated measures vs. averaging all CpG sites) and by method to adjust for batch effects (batch controls vs. random effects). We demonstrate that correcting for batch effects using plate controls yields unbiased associations, and that explicitly modeling the CpG site-specific variances and correlations among CpG sites increases statistical power. Using the recommended approaches, we examined the association between DNA methylation (in LINE-1 and growth related genes IGF2, H19 and HSD11B2) and 3 biomarkers of Pb exposure (Pb concentrations in umbilical cord blood, maternal tibia, and maternal patella), among mother-infant pairs of the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort (n = 247). Those with 10 μg/g higher patella Pb had, on average, 0.61% higher IGF2 methylation (P = 0.05). Sex-specific trends between Pb and DNA methylation (P < 0.1) were observed among girls including a 0.23% increase in HSD11B2 methylation with 10 μg/g higher patella Pb.     

Peptide-oligonucleotide conjugates form stable and selective complexes with antibody and DNA

The present work demonstrates that the relatively low molecular weight synthetic peptide-oligonucleotide conjugates are capable of stable and selective three-component complex formation with complementary 72-100mer DNA oligonucleotides and a cardiac troponin I monoclonal antibody. Neither the Watson-Crick-type interaction between peptide-oligonucleotide conjugate and DNA nor the conjugate-antibody interaction dramatically hampers the other. These interactions remain selective and specific in the presence of several other conjugates not specific to cardiac troponin I monoclonal antibody as well as in the presence of control 100mer DNA oligonucleotides. The data herein demonstrate the feasibility of the synthetic peptide-oligonucleotide conjugates as convenient molecular tools, e.g., for antibody epitope mapping.     

Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples

The bootstrap is a tool that allows for efficient evaluation of prediction performance of statistical techniques without having to set aside data for validation. This is especially important for high-dimensional data, e.g., arising from microarrays, because there the number of observations is often limited. For avoiding overoptimism the statistical technique to be evaluated has to be applied to every bootstrap sample in the same manner it would be used on new data. This includes a selection of complexity, e.g., the number of boosting steps for gradient boosting algorithms. Using the latter, we demonstrate in a simulation study that complexity selection in conventional bootstrap samples, drawn with replacement, is severely biased in many scenarios. This translates into a considerable bias of prediction error estimates, often underestimating the amount of information that can be extracted from high-dimensional data. Potential remedies for this complexity selection bias, such as alternatively using a fixed level of complexity or of using sampling without replacement are investigated and it is shown that the latter works well in many settings. We focus on high-dimensional binary response data, with bootstrap .632+ estimates of the Brier score for performance evaluation, and censored time-to-event data with .632+ prediction error curve estimates. The latter, with the modified bootstrap procedure, is then applied to an example with microarray data from patients with diffuse large B-cell lymphoma.