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Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.

Efforts to unravel the mechanisms underlying taste sensation (gustation) have largely focused on rodents. Here we present the first comprehensive characterization of gene expression in primate taste buds. Our findings reveal unique new insights into the biology of taste buds. We generated a taste bud gene expression database using laser capture microdissection (LCM) procured fungiform (FG) and circumvallate (CV) taste buds from primates. We also used LCM to collect the top and bottom portions of CV taste buds. Affymetrix genome wide arrays were used to analyze gene expression in all samples. Known taste receptors are preferentially expressed in the top portion of taste buds. Genes associated with the cell cycle and stem cells are preferentially expressed in the bottom portion of taste buds, suggesting that precursor cells are located there. Several chemokines including CXCL14 and CXCL8 are among the highest expressed genes in taste buds, indicating that immune system related processes are active in taste buds. Several genes expressed specifically in endocrine glands including growth hormone releasing hormone and its receptor are also strongly expressed in taste buds, suggesting a link between metabolism and taste. Cell type-specific expression of transcription factors and signaling molecules involved in cell fate, including KIT, reveals the taste bud as an active site of cell regeneration, differentiation, and development. IKBKAP, a gene mutated in familial dysautonomia, a disease that results in loss of taste buds, is expressed in taste cells that communicate with afferent nerve fibers via synaptic transmission. This database highlights the power of LCM coupled with transcriptional profiling to dissect the molecular composition of normal tissues, represents the most comprehensive molecular analysis of primate taste buds to date, and provides a foundation for further studies in diverse aspects of taste biology.

Alternative mRNA splicing adds a layer of regulation to the expression of thousands of genes in Drosophila melanogaster. Not all alternative splicing results in functional protein; it can also yield mRNA isoforms with premature stop codons that are degraded by the nonsense-mediated mRNA decay (NMD) pathway. This coupling of alternative splicing and NMD provides a mechanism for gene regulation that is highly conserved in mammals. NMD is also active in Drosophila, but its effect on the repertoire of alternative splice forms has been unknown, as has the mechanism by which it recognizes targets. Here, we have employed a custom splicing-sensitive microarray to globally measure the effect of alternative mRNA processing and NMD on Drosophila gene expression. We have developed a new algorithm to infer the expression change of each mRNA isoform of a gene based on the microarray measurements. This method is of general utility for interpreting splicing-sensitive microarrays and high-throughput sequence data. Using this approach, we have identified a high-confidence set of 45 genes where NMD has a differential effect on distinct alternative isoforms, including numerous RNA–binding and ribosomal proteins. Coupled alternative splicing and NMD decrease expression of these genes, which may in turn have a downstream effect on expression of other genes. The NMD–affected genes are enriched for roles in translation and mitosis, perhaps underlying the previously observed role of NMD factors in cell cycle progression. Our results have general implications for understanding the NMD mechanism in fly. Most notably, we found that the NMD–target mRNAs had significantly longer 3′ untranslated regions (UTRs) than the nontarget isoforms of the same genes, supporting a role for 3′ UTR length in the recognition of NMD targets in fly., Author Summary A gene can be processed into multiple mRNAs through alternative splicing. Alternative splicing increases the number of proteins encoded by the genome, but not all alternative mRNAs produce protein. Instead, some are degraded by nonsense-mediated mRNA decay (NMD), a surveillance system that was originally identified as a means of clearing the cell of mRNAs with nonsense, or stop codon, mutations. Alternative splicing that introduces early stop codons will lead to NMD, offering a way for the cell to down-regulate gene expression after a gene has been transcribed. In this paper, we have developed a new analysis method to study the combined effect of alternative splicing and degradation in the fruit fly Drosophila melanogaster using microarrays. We have found a stringently defined set of 45 genes that can be spliced either into an mRNA that encodes a protein or into an mRNA that is degraded by NMD, down-regulating the overall gene expression. The affected genes include a number that are central to the cell's regulatory processes, including translation, RNA splicing, and cell cycle progression. Our results also help shed light on how NMD determines whether a stop codon is premature, and thus whether to target an mRNA for degradation.

Meiotic mapping of quantitative trait loci regulating expression (eQTLs) has allowed the construction of gene networks. However, the limited mapping resolution of these studies has meant that genotype data are largely ignored, leading to undirected networks that fail to capture regulatory hierarchies. Here we use high resolution mapping of copy number eQTLs (ceQTLs) in a mouse-hamster radiation hybrid (RH) panel to construct directed genetic networks in the mammalian cell. The RH network covering 20,145 mouse genes had significant overlap with, and similar topological structures to, existing biological networks. Upregulated edges in the RH network had significantly more overlap than downregulated. This suggests repressive relationships between genes are missed by existing approaches, perhaps because the corresponding proteins are not present in the cell at the same time and therefore unlikely to interact. Gene essentiality was positively correlated with connectivity and betweenness centrality in the RH network, strengthening the centrality-lethality principle in mammals. Consistent with their regulatory role, transcription factors had significantly more outgoing edges (regulating) than incoming (regulated) in the RH network, a feature hidden by conventional undirected networks. Directed RH genetic networks thus showed concordance with pre-existing networks while also yielding information inaccessible to current undirected approaches., Author Summary An important problem in systems biology is to map gene networks, which help identify gene functions and discover critical disease pathways. Current methods for constructing gene networks have identified a number of biologically significant functional modules. However, these networks do not reveal directionality, that is, which gene regulates which, an important aspect of gene regulation. Radiation hybrid panels are a venerable method for high resolution genetic mapping. Recently we have used radiation hybrids to map loci based on their effects on gene expression. Because these regulatory loci are finely mapped, we can identify which gene turns on another gene, that is, directionality. In this paper, we constructed directed networks from radiation hybrid expression data. We found the radiation hybrid networks concordant with available datasets but also demonstrate that they can reveal information inaccessible to existing approaches. Importantly, directionality can help dissect cause and effect in genetic networks, aiding in understanding and ultimately rational intervention.

With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies—SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu., Author Summary In genome-wide association studies, it is important to account for the fact that a large number of genetic variants are tested in order to adequately control for false positives. The simplest way to correct for multiple hypothesis testing is the Bonferroni correction, which multiplies the p-values by the number of markers assuming the markers are independent. Since the markers are correlated due to linkage disequilibrium, this approach leads to a conservative estimate of false positives, thus adversely affecting statistical power. The permutation test is considered the gold standard for accurate multiple testing correction, but is often computationally impractical for large association studies. We propose a method that efficiently and accurately corrects for multiple hypotheses in genome-wide association studies by fully accounting for the local correlation structure between markers. Our method also corrects for the departure of the true distribution of test statistics from the asymptotic distribution, which dramatically improves the accuracy, particularly when many rare variants are included in the tests. Our method shows a near identical accuracy to permutation and shows greater computational efficiency than previously suggested methods. We also provide a method to accurately and efficiently estimate the statistical power of genome-wide association studies.

Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request., Author Summary One of the fundamental challenges in biology in the post-genomics era is understanding the complex regulatory mechanisms that govern how genes are turned on and off. In a single organism where the functions of individual genes in a population do not differ much, many of the differences between individuals including physical phenotypes, susceptibility to disease, and response to drugs can be attributed to how genes are regulated. Previous studies have largely focused on identifying regulator and target genes whose expressions are linked to genetic variations in a population. We present work that focuses on considering a specific set of regulators called transcription factors whose targets can be verified from experiments and whose interactions with those targets have been well studied and modeled. In this setting, we can begin to understand how genetic variations perturb the concentrations and promoter affinities of active transcription factors to induce differential expression of the targets. Understanding the effects of these perturbations is important to understanding the fundamental biology of gene regulation and can help us to design and assess therapeutics and treatments for complex diseases.

A complete description of the transcriptome of an organism is crucial for a comprehensive understanding of how it functions and how its transcriptional networks are controlled, and may provide insights into the organism's evolution. Despite the status of Saccharomyces cerevisiae as arguably the most well-studied model eukaryote, we still do not have a full catalog or understanding of all its genes. In order to interrogate the transcriptome of S. cerevisiae for low abundance or rapidly turned over transcripts, we deleted elements of the RNA degradation machinery with the goal of preferentially increasing the relative abundance of such transcripts. We then used high-resolution tiling microarrays and ultra high–throughput sequencing (UHTS) to identify, map, and validate unannotated transcripts that are more abundant in the RNA degradation mutants relative to wild-type cells. We identified 365 currently unannotated transcripts, the majority presumably representing low abundance or short-lived RNAs, of which 185 are previously unknown and unique to this study. It is likely that many of these are cryptic unstable transcripts (CUTs), which are rapidly degraded and whose function(s) within the cell are still unclear, while others may be novel functional transcripts. Of the 185 transcripts we identified as novel to our study, greater than 80 percent come from regions of the genome that have lower conservation scores amongst closely related yeast species than 85 percent of the verified ORFs in S. cerevisiae. Such regions of the genome have typically been less well-studied, and by definition transcripts from these regions will distinguish S. cerevisiae from these closely related species., Author Summary The budding yeast Saccharomyces cerevisiae, because of the relative ease of its genetic manipulation and its ease of handling in the laboratory, has long served as a model on which studies in higher organisms have been based. To more fully understand how eukaryotic cells express their genomes, we sought to identify RNA species that are transcribed at very low levels or that are rapidly degraded. We created mutants deficient in the ability to degrade RNA, with the expectation that this would increase the relative abundance of such RNAs, and then used high-resolution microarrays and sequencing technologies to locate and identify from where these RNAs are transcribed. Using this approach, we have identified 365 transcripts that do not appear in the most current list of annotated S. cerevisiae RNA transcripts; of these, 185 are unique to our study. Many of these novel transcripts derive from regions of the genome that are poorly conserved between S. cerevisiae and other closely related yeast species, suggesting that these RNAs may play an important role in the divergent microevolution of S. cerevisiae.

The assembly methods used for whole-genome shotgun (WGS) data have a major impact on the quality of resulting draft genomes. We present a novel algorithm to generate a set of “reliable” overlaps based on identifying repeat k-mers. To demonstrate the benefits of using reliable overlaps, we have created a version of the Phrap assembly program that uses only overlaps from a specific list. We call this version PhrapUMD. Integrating PhrapUMD and our “reliable-overlap” algorithm with the Baylor College of Medicine assembler, Atlas, we assemble the BACs from the Rattus norvegicus genome project. Starting with the same data as the Nov. 2002 Atlas assembly, we compare our results and the Atlas assembly to the 4.3 Mb of rat sequence in the 21 BACs that have been finished. Our version of the draft assembly of the 21 BACs increases the coverage of finished sequence from 93.4% to 96.3%, while simultaneously reducing the base error rate from 4.5 to 1.1 errors per 10,000 bases. There are a number of ways of assessing the relative merits of assemblies when the finished sequence is available. If one views the overall quality of an assembly as proportional to the inverse of the product of the error rate and sequence missed, then the assembly presented here is seven times better. The UMD Overlapper with options for reliable overlaps is available from the authors at http://www.genome.umd.edu. We also provide the changes to the Phrap source code enabling it to use only the reliable overlaps.

Author(s): Smith, Theresa J; Hill, Karen K; Foley, Brian T; Detter, John C; Munk, A Christine; Bruce, David C; Doggett, Norman A; Smith, Leonard A; Marks, James D; Xie, Gary; Brettin, Thomas S | Abstract: BackgroundClostridium botulinum and related clostridial species express extremely potent neurotoxins known as botulinum neurotoxins (BoNTs) that cause long-lasting, potentially fatal intoxications in humans and other mammals. The amino acid variation within the BoNT is used to categorize the species into seven immunologically distinct BoNT serotypes (A-G) which are further divided into subtypes. The BoNTs are located within two generally conserved gene arrangements known as botulinum progenitor complexes which encode toxin-associated proteins involved in toxin stability and expression.Methodology/principal findingsBecause serotype A and B strains are responsible for the vast majority of human botulism cases worldwide, the location, arrangement and sequences of genes from eight different toxin complexes representing four different BoNT/A subtypes (BoNT/A1-Ba4) and one BoNT/B1 strain were examined. The bivalent Ba4 strain contained both the BoNT/A4 and BoNT/bvB toxin clusters. The arrangements of the BoNT/A3 and BoNT/A4 subtypes differed from the BoNT/A1 strains and were similar to those of BoNT/A2. However, unlike the BoNT/A2 subtype, the toxin complex genes of BoNT/A3 and BoNT/A4 were found within large plasmids and not within the chromosome. In the Ba4 strain, both BoNT toxin clusters (A4 and bivalent B) were located within the same 270 kb plasmid, separated by 97 kb. Complete genomic sequencing of the BoNT/B1 strain also revealed that its toxin complex genes were located within a 149 kb plasmid and the BoNT/A3 complex is within a 267 kb plasmid.Conclusions/significanceDespite their size differences and the BoNT genes they contain, the three plasmids containing these toxin cluster genes share significant sequence identity. The presence of partial insertion sequence (IS) elements, evidence of recombination/gene duplication events, and the discovery of the BoNT/A3, BoNT/Ba4 and BoNT/B1 toxin complex genes within plasmids illustrate the different mechanisms by which these genes move among diverse genetic backgrounds of C. botulinum.

Whole genome expression profiles are widely used to discover molecular subtypes of diseases. A remaining challenge is to identify the correspondence or commonality of subtypes found in multiple, independent data sets generated on various platforms. While model-based supervised learning is often used to make these connections, the models can be biased to the training data set and thus miss inherent, relevant substructure in the test data. Here we describe an unsupervised subclass mapping method (SubMap), which reveals common subtypes between independent data sets. The subtypes within a data set can be determined by unsupervised clustering or given by predetermined phenotypes before applying SubMap. We define a measure of correspondence for subtypes and evaluate its significance building on our previous work on gene set enrichment analysis. The strength of the SubMap method is that it does not impose the structure of one data set upon another, but rather uses a bi-directional approach to highlight the common substructures in both. We show how this method can reveal the correspondence between several cancer-related data sets. Notably, it identifies common subtypes of breast cancer associated with estrogen receptor status, and a subgroup of lymphoma patients who share similar survival patterns, thus improving the accuracy of a clinical outcome predictor.

Adaptive protein evolution is pervasive in Drosophila. Genomic studies, thus far, have analyzed each protein as a single entity. However, the targets of adaptive events may be localized to particular parts of proteins, such as protein domains or regions involved in protein folding. We compared the population genetic mechanisms driving sequence polymorphism and divergence in defined protein domains and non-domain regions. Interestingly, we find that non-domain regions of proteins are more frequent targets of directional selection. Protein domains are also evolving under directional selection, but appear to be under stronger purifying selection than non-domain regions. Non-domain regions of proteins clearly play a major role in adaptive protein evolution on a genomic scale and merit future investigations of their functional properties.