miR-124 is a highly conserved microRNA (miRNA) whose in vivo function is poorly understood. Here, we identify miR-124 targets based on the analysis of the first mir-124 mutant in any organism. We find that miR-124 is expressed in many sensory neurons in Caenorhabditis elegans and onset of expression coincides with neuronal morphogenesis. We analyzed the transcriptome of miR-124 expressing and nonexpressing cells from wild-type and mir-124 mutants. We observe that many targets are co-expressed with and actively repressed by miR-124. These targets are expressed at reduced relative levels in sensory neurons compared to the rest of the animal. Our data from mir-124 mutant animals show that this effect is due to a large extent to the activity of miR-124. Genes with nonconserved target sites show reduced absolute expression levels in sensory neurons. In contrast, absolute expression levels of genes with conserved sites are comparable to control genes, suggesting a tuning function for many of these targets. We conclude that miR-124 contributes to defining cell-type-specific gene activity by repressing a diverse set of co-expressed genes.

A key stage for all microarray analyses is the extraction of feature-intensities from an image. If this step goes wrong, then subsequent preprocessing and processing stages will stand little chance of rectifying the matter. Illumina employ random construction of their BeadArrays, making feature-intensity extraction even more important for the Illumina platform than for other technologies. In this paper we show that using raw Illumina data it is possible to identify, control, and perhaps correct for a range of spatial-related phenomena that affect feature-intensity extraction.

The pathophysiology of endometriosis remains unclear but involves a complex interaction between ectopic endometrium and host peritoneal tissues. We hypothesized that disruption of this interaction would suppress endometriotic lesion formation. We hoped to delineate the molecular and cellular dialogue between ectopic human endometrium and peritoneal tissues in nude mice as a first step toward testing this hypothesis. Human endometrium was xenografted into nude mice, and the resulting lesions were analyzed using microarrays. A novel technique was developed that unambiguously determined whether RNA transcripts identified via microarray analyses originated from human cells (endometrium) or mouse cells (mesothelium). Four key pathways (ubiquitin/proteasome, inflammation, tissue remodeling/repair, and ras-mediated oncogenesis) were revealed, demonstrating communication between host mesothelial cells and ectopic endometrium. Morphometric analysis of nude mouse lesions confirmed that necrosis, inflammation, healing and repair, and cell proliferation occurred during xenograft development. These processes were entirely consistent with the molecular networks revealed by the microarray data. The transcripts detected in the xenografts overlapped with differentially expressed transcripts in a comparison between paired eutopic and ectopic endometria from human endometriotic patients. For the first time, components of the interaction between ectopic endometrium and peritoneal stromal tissues are revealed. Targeted disruption of this dialogue is likely to inhibit endometriotic tissue formation and may prove to be an effective therapeutic strategy for endometriosis.

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.

Maintaining quiescent cells in G0 phase is achieved in part through the multiprotein subunit complex known as DREAM, and in human cell lines the transcription factor E2F4 directs this complex to its cell cycle targets. We found that E2F4 binds a highly overlapping set of human genes among three diverse primary tissues and an asynchronous cell line, which suggests that tissue-specific binding partners and chromatin structure have minimal influence on E2F4 targeting. To investigate the conservation of these transcription factor binding events, we identified the mouse genes bound by E2f4 in seven primary mouse tissues and a cell line. E2f4 bound a set of mouse genes that was common among mouse tissues, but largely distinct from the genes bound in human. The evolutionarily conserved set of E2F4 bound genes is highly enriched for functionally relevant regulatory interactions important for maintaining cellular quiescence. In contrast, we found minimal mRNA expression perturbations in this core set of E2f4 bound genes in the liver, kidney, and testes of E2f4 null mice. Thus, the regulatory mechanisms maintaining quiescence are robust even to complete loss of conserved transcription factor binding events.

Mutational processes acting on cancer genomes can be traced by investigating mutational signatures. Because high sequencing costs limit current studies to small numbers of good-quality samples, we propose a robust, cost- and time-effective method, called mutREAD, to detect mutational signatures from small quantities of DNA, including degraded samples. We show that mutREAD recapitulates mutational signatures identified by whole genome sequencing, and will ultimately allow the study of mutational signatures in larger cohorts and, by compatibility with formalin-fixed paraffin-embedded samples, in clinical settings.

Cancer genome sequencing studies have identified numerous driver genes, but the relative timing of mutations in carcinogenesis remains unclear. The gradual progression from premalignant Barrett's esophagus to esophageal adenocarcinoma (EAC) provides an ideal model to study the ordering of somatic mutations. We identified recurrently mutated genes and assessed clonal structure using whole-genome sequencing and amplicon resequencing of 112 EACs. We next screened a cohort of 109 biopsies from 2 key transition points in the development of malignancy: benign metaplastic never-dysplastic Barrett's esophagus (NDBE; n=66) and high-grade dysplasia (HGD; n=43). Unexpectedly, the majority of recurrently mutated genes in EAC were also mutated in NDBE. Only TP53 and SMAD4 mutations occurred in a stage-specific manner, confined to HGD and EAC, respectively. Finally, we applied this knowledge to identify high-risk Barrett's esophagus in a new non-endoscopic test. In conclusion, mutations in EAC driver genes generally occur exceptionally early in disease development with profound implications for diagnostic and therapeutic strategies.

Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) is a valuable tool for epigenetic studies. Analysis of the data arising from ChIP-seq experiments often requires implicit or explicit statistical modeling of the read counts. The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data. Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model. Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution. We apply the Constrained Newton Method (CNM), which suggests the Negative Binomial - Negative Binomial (NB-NB) mixture model as a candidate for modeling ChIP-seq data. We illustrate fitting the NB-NB model with an accelerated EM algorithm on four data sets from three species. Zero-inflated models have been suggested as an approach to improve model fit for ChIP-seq data. We show that the NB-NB mixture model requires no zero-inflation and suggest that in some cases the need for zero inflation is driven by the model's inability to cope with both artifactual large read counts and the frequently observed very low read counts. We see that the CNM-based approach is a useful diagnostic for the assessment of model fit and inference in ChIP-seq data and beyond. Use of the suggested NB-NB mixture model will be of value not only when calling peaks or otherwise modeling ChIP-seq data, but also when simulating data or constructing blacklists de novo.

Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

Forkhead box (FOX) transcription factors regulate a wide variety of cellular functions in higher eukaryotes, including cell cycle control and developmental regulation. In Saccharomyces cerevisiae, Forkhead proteins Fkh1 and Fkh2 perform analogous functions, regulating genes involved in cell cycle control, while also regulating mating-type silencing and switching involved in gamete development. Recently, we revealed a novel role for Fkh1 and Fkh2 in the regulation of replication origin initiation timing, which, like donor preference in mating-type switching, appears to involve long-range chromosomal interactions, suggesting roles for Fkh1 and Fkh2 in chromatin architecture and organization. To elucidate how Fkh1 and Fkh2 regulate their target DNA elements and potentially regulate the spatial organization of the genome, we undertook a genome-wide analysis of Fkh1 and Fkh2 chromatin binding by ChIP-chip using tiling DNA microarrays. Our results confirm and extend previous findings showing that Fkh1 and Fkh2 control the expression of cell cycle-regulated genes. In addition, the data reveal hundreds of novel loci that bind Fkh1 only and exhibit a distinct chromatin structure from loci that bind both Fkh1 and Fkh2. The findings also show that Fkh1 plays the predominant role in the regulation of a subset of replication origins that initiate replication early, and that Fkh1/2 binding to these loci is cell cycle-regulated. Finally, we demonstrate that Fkh1 and Fkh2 bind proximally to a variety of genetic elements, including centromeres and Pol III-transcribed snoRNAs and tRNAs, greatly expanding their potential repertoire of functional targets, consistent with their recently suggested role in mediating the spatial organization of the genome.

The numbers of oncogenic mutations required for transformation are uncertain but may be inferred from how cancer frequencies increase with aging. Cancers requiring more mutations will tend to appear later in life. This type of approach may be confounded by biologic heterogeneity because different cancer subtypes may require different numbers of mutations. For example, a sporadic cancer should require at least one more somatic mutation relative to its hereditary counterpart.

The exploration of quantitative variation in human populations has become one of the major priorities for medical genetics. The successful identification of variants that contribute to complex traits is highly dependent on reliable assays and genetic maps. We have performed a genome-wide quantitative trait analysis of 630 genes in 60 unrelated Utah residents with ancestry from Northern and Western Europe using the publicly available phase I data of the International HapMap project. The genes are located in regions of the human genome with elevated functional annotation and disease interest including the ENCODE regions spanning 1% of the genome, Chromosome 21 and Chromosome 20q12-13.2. We apply three different methods of multiple test correction, including Bonferroni, false discovery rate, and permutations. For the 374 expressed genes, we find many regions with statistically significant association of single nucleotide polymorphisms (SNPs) with expression variation in lymphoblastoid cell lines after correcting for multiple tests. Based on our analyses, the signal proximal (cis-) to the genes of interest is more abundant and more stable than distal and trans across statistical methodologies. Our results suggest that regulatory polymorphism is widespread in the human genome and show that the 5-kb (phase I) HapMap has sufficient density to enable linkage disequilibrium mapping in humans. Such studies will significantly enhance our ability to annotate the non-coding part of the genome and interpret functional variation. In addition, we demonstrate that the HapMap cell lines themselves may serve as a useful resource for quantitative measurements at the cellular level.

Oesophageal adenocarcinoma (OAC) provides an ideal case study to characterize large-scale rearrangements. Using whole genome short-read sequencing of 383 cases, for which 214 had matched whole transcriptomes, we observed structural variations (SV) with a predominance of deletions, tandem duplications and inter-chromosome junctions that could be identified as LINE-1 mobile element (ME) insertions. Complex clusters of rearrangements resembling breakage-fusion-bridge cycles or extrachromosomal circular DNA accounted for 22% of complex SVs affecting known oncogenes. Counting SV events affecting known driver genes substantially increased the recurrence rates of these drivers. After excluding fragile sites, we identified 51 candidate new drivers in genomic regions disrupted by SVs, including ETV5, KAT6B and CLTC. RUNX1 was the most recurrently altered gene (24%), with many deletions inactivating the RUNT domain but preserved the reading frame, suggesting an altered protein product. These findings underscore the importance of identification of SV events in OAC with implications for targeted therapies.

Mobile elements are active in the human genome, both in the germline and cancers, where they can mutate driver genes.

High-throughput measurement of allele-specific expression (ASE) is a relatively new and exciting application area for array-based technologies. In this paper, we explore several data sets which make use of Illumina's GoldenGate BeadArray technology to measure ASE. This platform exploits coding SNPs to obtain relative expression measurements for alleles at approximately 1500 positions in the genome.

Imprinted genes show expression from one parental allele only and are important for development and behaviour. This extreme mode of allelic imbalance has been described for approximately 56 human genes. Imprinting status is often disrupted in cancer and dysmorphic syndromes. More subtle variation of gene expression, that is not parent-of-origin specific, termed 'allele-specific gene expression' (ASE) is more common and may give rise to milder phenotypic differences. Using two allele-specific high-throughput technologies alongside bioinformatics predictions, normal term human placenta was screened to find new imprinted genes and to ascertain the extent of ASE in this tissue.

Many tumors have highly rearranged genomes, but a major unknown is the relative importance and timing of genome rearrangements compared to sequence-level mutation. Chromosome instability might arise early, be a late event contributing little to cancer development, or happen as a single catastrophic event. Another unknown is which of the point mutations and rearrangements are selected. To address these questions we show, using the breast cancer cell line HCC1187 as a model, that we can reconstruct the likely history of a breast cancer genome. We assembled probably the most complete map to date of a cancer genome, by combining molecular cytogenetic analysis with sequence data. In particular, we assigned most sequence-level mutations to individual chromosomes by sequencing of flow sorted chromosomes. The parent of origin of each chromosome was assigned from SNP arrays. We were then able to classify most of the mutations as earlier or later according to whether they occurred before or after a landmark event in the evolution of the genome, endoreduplication (duplication of its entire genome). Genome rearrangements and sequence-level mutations were fairly evenly divided earlier and later, suggesting that genetic instability was relatively constant throughout the life of this tumor, and chromosome instability was not a late event. Mutations that caused chromosome instability would be in the earlier set. Strikingly, the great majority of inactivating mutations and in-frame gene fusions happened earlier. The non-random timing of some of the mutations may be evidence that they were selected.

Illumina BeadArrays are among the most popular and reliable platforms for gene expression profiling. However, little external scrutiny has been given to the design, selection and annotation of BeadArray probes, which is a fundamental issue in data quality and interpretation. Here we present a pipeline for the complete genomic and transcriptomic re-annotation of Illumina probe sequences, also applicable to other platforms, with its output available through a Web interface and incorporated into Bioconductor packages. We have identified several problems with the design of individual probes and we show the benefits of probe re-annotation on the analysis of BeadArray gene expression data sets. We discuss the importance of aspects such as probe coverage of individual transcripts, alternative messenger RNA splicing, single-nucleotide polymorphisms, repeat sequences, RNA degradation biases and probes targeting genomic regions with no known transcription. We conclude that many of the Illumina probes have unreliable original annotation and that our re-annotation allows analyses to focus on the good quality probes, which form the majority, and also to expand the scope of biological information that can be extracted.

The accurate and high resolution mapping of DNA copy number aberrations has become an important tool by which to gain insight into the mechanisms of tumourigenesis. There are various commercially available platforms for such studies, but there remains no general consensus as to the optimal platform. There have been several previous platform comparison studies, but they have either described older technologies, used less-complex samples, or have not addressed the issue of the inherent biases in such comparisons. Here we describe a systematic comparison of data from four leading microarray technologies (the Affymetrix Genome-wide SNP 5.0 array, Agilent High-Density CGH Human 244A array, Illumina HumanCNV370-Duo DNA Analysis BeadChip, and the Nimblegen 385 K oligonucleotide array). We compare samples derived from primary breast tumours and their corresponding matched normals, well-established cancer cell lines, and HapMap individuals. By careful consideration and avoidance of potential sources of bias, we aim to provide a fair assessment of platform performance.

Several interventions increase lifespan in model organisms, including reduced insulin/insulin-like growth factor-like signaling (IIS), FOXO transcription factor activation, dietary restriction, and superoxide dismutase (SOD) over-expression. One question is whether these manipulations function through different mechanisms, or whether they intersect on common processes affecting aging.