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Prenatal adversity or stress can have long-term consequences on developmental trajectories and health outcomes. Although the biological mechanisms underlying these effects are poorly understood, epigenetic modifications, such as DNA methylation, have the potential to link early-life environments to alterations in physiological systems, with long-term functional implications. We investigated the consequences of two prenatal insults, prenatal alcohol exposure (PAE) and food-related stress, on DNA methylation profiles of the rat brain during early development. As these insults can have sex-specific effects on biological outcomes, we analyzed epigenome-wide DNA methylation patterns in prefrontal cortex, a key brain region involved in cognition, executive function, and behavior, of both males and females. We found sex-dependent and sex-concordant influences of these insults on epigenetic patterns. These alterations occurred in genes and pathways related to brain development and immune function, suggesting that PAE and food-related stress may reprogram neurobiological/physiological systems partly through central epigenetic changes, and may do so in a sex-dependent manner. Such epigenetic changes may reflect the sex-specific effects of prenatal insults on long-term functional and health outcomes and have important implications for understanding possible mechanisms underlying fetal alcohol spectrum disorder and other neurodevelopmental disorders.

Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to map complete DNA methylomes. These include whole-genome bisulfite sequencing (WGBS, MethylC-seq, or BS-seq), reduced-representation bisulfite sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. These methods yield largely comparable results but differ significantly in extent of genomic CpG coverage, resolution, quantitative accuracy, and cost, at least while using current algorithms to interrogate the data. None of these existing methods provides single-CpG resolution, comprehensive genome-wide coverage, and cost feasibility for a typical laboratory. We introduce methylCRF, a novel conditional random fields-based algorithm that integrates methylated DNA immunoprecipitation (MeDIP-seq) and methylation-sensitive restriction enzyme (MRE-seq) sequencing data to predict DNA methylation levels at single-CpG resolution. Our method is a combined computational and experimental strategy to produce DNA methylomes of all 28 million CpGs in the human genome for a fraction (<10%) of the cost of whole-genome bisulfite sequencing methods. methylCRF was benchmarked for accuracy against Infinium arrays, RRBS, WGBS sequencing, and locus-specific bisulfite sequencing performed on the same human embryonic stem cell line. methylCRF transformation of MeDIP-seq/MRE-seq was equivalent to a biological replicate of WGBS in quantification, coverage, and resolution. We used conventional bisulfite conversion, PCR, cloning, and sequencing to validate loci where our predictions do not agree with whole-genome bisulfite data, and in 11 out of 12 cases, methylCRF predictions of methylation level agree better with validated results than does whole-genome bisulfite sequencing. Therefore, methylCRF transformation of MeDIP-seq/MRE-seq data provides an accurate, inexpensive, and widely accessible strategy to create full DNA methylomes.

DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. This includes one of the most widely applied technologies for measuring DNA methylation: methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been unavailable. Here, we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes, and combines MeDIP-seq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MRE-seq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities.

Biologists possess the detailed knowledge critical for extracting biological insight from genome-wide data resources, and yet they are increasingly faced with nontrivial computational analysis challenges posed by genome-scale methodologies. To lower this computational barrier, particularly in the early data exploration phases, we have developed an interactive pattern discovery and visualization approach, Spark, designed with epigenomic data in mind. Here we demonstrate Spark's ability to reveal both known and novel epigenetic signatures, including a previously unappreciated binding association between the YY1 transcription factor and the corepressor CTBP2 in human embryonic stem cells.

DNA rearrangements such as sister chromatid exchanges (SCEs) are sensitive indicators of genomic stress and instability, but they are typically masked by single-cell sequencing techniques. We developed Strand-seq to independently sequence parental DNA template strands from single cells, making it possible to map SCEs at orders-of-magnitude greater resolution than was previously possible. On average, murine embryonic stem (mES) cells exhibit eight SCEs, which are detected at a resolution of up to 23 bp. Strikingly, Strand-seq of 62 single mES cells predicts that the mm 9 mouse reference genome assembly contains at least 17 incorrectly oriented segments totaling nearly 1% of the genome. These misoriented contigs and fragments have persisted through several iterations of the mouse reference genome and have been difficult to detect using conventional sequencing techniques. The ability to map SCE events at high resolution and fine-tune reference genomes by Strand-seq dramatically expands the scope of single-cell sequencing.

Castrate-resistant prostate cancer (CRPC) and neuroendocrine carcinoma of the prostate are invariably fatal diseases for which only palliative therapies exist. As part of a prostate tumor sequencing program, a patient tumor was analyzed using Illumina genome sequencing and a matched renal capsule tumor xenograft was generated. Both tumor and xenograft had a homozygous 9p21 deletion spanning the MTAP, CDKN2, and ARF genes. It is rare for this deletion to occur in primary prostate tumors, yet approximately 10% express decreased levels of methylthioadenosine phosphorylase (MTAP) mRNA. Decreased MTAP expression is a prognosticator for poor outcome. Moreover, it seems that this deletion is more common in CRPC than in primary prostate cancer. We show for the first time that treatment with methylthioadenosine and high dose 6-thioguanine causes marked inhibition of a patient-derived neuroendocrine xenograft growth while protecting the host from 6-thioguanine toxicity. This therapeutic approach can be applied to other MTAP-deficient human cancers as deletion or hypermethylation of the MTAP gene occurs in a broad spectrum of tumors at high frequency. The combination of genome sequencing and patient-derived xenografts can identify candidate therapeutic agents and evaluate them for personalized oncology.

Next generation sequencing has brought epigenomic studies to the forefront of current research. The power of massively parallel sequencing coupled to innovative molecular and computational techniques has allowed researchers to profile the epigenome at resolutions that were unimaginable only a few years ago. With early proof of concept studies published, the field is now moving into the next phase where the importance of method standardization and rigorous quality control are becoming paramount. In this review we will describe methodologies that have been developed to profile the epigenome using next generation sequencing platforms. We will discuss these in terms of library preparation, sequence platforms and analysis techniques.

We describe Trans-ABySS, a de novo short-read transcriptome assembly and analysis pipeline that addresses variation in local read densities by assembling read substrings with varying stringencies and then merging the resulting contigs before analysis. Analyzing 7.4 gigabases of 50-base-pair paired-end Illumina reads from an adult mouse liver poly(A) RNA library, we identified known, new and alternative structures in expressed transcripts, and achieved high sensitivity and specificity relative to reference-based assembly methods.

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

This unit provides a protocol for performing digital gene expression profiling on the Illumina Genome Analyzer sequencing platform. Tag sequencing (Tag-seq) is an implementation of the LongSAGE protocol on the Illumina sequencing platform that increases utility while reducing both the cost and time required to generate gene expression profiles. The ultra-high-throughput sequencing capability of the Illumina platform allows the cost-effective generation of libraries containing an average of 20 million tags, a 200-fold improvement over classical LongSAGE. Tag-seq has less sequence composition bias, leading to a better representation of AT-rich tag sequences, and allows a more accurate profiling of a subset of the transcriptome characterized by AT-rich genes expressed at levels below the threshold of detection of LongSAGE (Morrissy et al., 2009)., ((c) 2010 by John Wiley & Sons, Inc.)

Motivation: Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once aligned to a reference genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery., Results: We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem. The first models allelic counts as observations and infers SNVs and model parameters using an expectation maximization (EM) algorithm and is therefore capable of adjusting to deviation of allelic frequencies inherent in genomically unstable tumor genomes. The second models nucleotide and mapping qualities of the reads by probabilistically weighting the contribution of a read/nucleotide to the inference of a SNV based on the confidence we have in the base call and the read alignment. The third combines filtering out low-quality data in addition to probabilistic weighting of the qualities. We quantitatively evaluated these approaches on 16 ovarian cancer RNASeq datasets with matched genotyping arrays and a human breast cancer genome sequenced to >40x (haploid) coverage with ground truth data and show systematically that the SNVMix models outperform competing approaches., Availability: Software and data are available at http://compbio.bccrc.ca, Contact: sshah@bccrc.ca SUPPLEMANTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tag-seq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.

Transcriptome analysis has been a key area of biological inquiry for decades. Over the years, research in the field has progressed from candidate gene-based detection of RNAs using Northern blotting to high-throughput expression profiling driven by the advent of microarrays. Next-generation sequencing technologies have revolutionized transcriptomics by providing opportunities for multidimensional examinations of cellular transcriptomes in which high-throughput expression data are obtained at a single-base resolution.

Sequencing-by-synthesis technologies can reduce the cost of generating de novo genome assemblies. We report a method for assembling draft genome sequences of eukaryotic organisms that integrates sequence information from different sources, and demonstrate its effectiveness by assembling an approximately 32.5 Mb draft genome sequence for the forest pathogen Grosmannia clavigera, an ascomycete fungus. We also developed a method for assessing draft assemblies using Illumina paired end read data and demonstrate how we are using it to guide future sequence finishing. Our results demonstrate that eukaryotic genome sequences can be accurately assembled by combining Illumina, 454 and Sanger sequence data.

Background: High throughput sequencing-by-synthesis is an emerging technology that allows the rapid production of millions of bases of data. Although the sequence reads are short, they can readily be used for re-sequencing. By re-sequencing the mRNA products of a cell, one may rapidly discover polymorphisms and splice variants particular to that cell., Results: We present the utility of massively parallel sequencing by synthesis for profiling the transcriptome of a human prostate cancer cell-line, LNCaP, that has been treated with the synthetic androgen, R1881. Through the generation of approximately 20 megabases (MB) of EST data, we detect transcription from over 10,000 gene loci, 25 previously undescribed alternative splicing events involving known exons, and over 1,500 high quality single nucleotide discrepancies with the reference human sequence. Further, we map nearly 10,000 ESTs to positions on the genome where no transcription is currently predicted to occur. We also characterize various obstacles with using sequencing by synthesis for transcriptome analysis and propose solutions to these problems., Conclusion: The use of high-throughput sequencing-by-synthesis methods for transcript profiling allows the specific and sensitive detection of many of a cell's transcripts, and also allows the discovery of high quality base discrepancies, and alternative splice variants. Thus, this technology may provide an effective means of understanding various disease states, discovering novel targets for disease treatment, and discovery of novel transcripts.