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A novel approach to analyse high-depth Hi-C data provides a comprehensive chromatin interaction map at approximately 5–10 kb resolution in human fibroblasts; this reveals that TNF-α-responsive enhancers are already in contact with target promoters before signalling and that this chromatin looping is a strong predictor of gene induction. Chromatin interactions in human fibroblasts Hi-C is a genomic technology based on chromosome conformation capture (3C) that can identify long-range looping interactions of chromatin throughout the genome in an unbiased fashion. Bing Ren and colleagues have developed a novel analysis pipeline for Hi-C data sets that offers much improved resolution so that interactions between cis -regulatory elements such as enhancers and promoters can be defined. Applying it to study dynamic chromatin interactions during NF-κB signalling in human fibroblasts, they find that the majority of interactions between enhancers and promoters have already formed prior to the binding of sequence-specific transcription factors to enhancers. The regulatory targets of the transcription factor thus appear to have been hardwired into the chromatin architecture. A large number of cis -regulatory sequences have been annotated in the human genome 1 , 2 , but defining their target genes remains a challenge 3 . One strategy is to identify the long-range looping interactions at these elements with the use of chromosome conformation capture (3C)-based techniques 4 . However, previous studies lack either the resolution or coverage to permit a whole-genome, unbiased view of chromatin interactions. Here we report a comprehensive chromatin interaction map generated in human fibroblasts using a genome-wide 3C analysis method (Hi-C) 5 . We determined over one million long-range chromatin interactions at 5–10-kb resolution, and uncovered general principles of chromatin organization at different types of genomic features. We also characterized the dynamics of promoter–enhancer contacts after TNF-α signalling in these cells. Unexpectedly, we found that TNF-α-responsive enhancers are already in contact with their target promoters before signalling. Such pre-existing chromatin looping, which also exists in other cell types with different extracellular signalling, is a strong predictor of gene induction. Our observations suggest that the three-dimensional chromatin landscape, once established in a particular cell type, is relatively stable and could influence the selection or activation of target genes by a ubiquitous transcription activator in a cell-specific manner.

Whole-genome resequencing of dogs and wolves helps identify genomic regions that are likely to represent targets for selection during dog domestication. When dogs homed in on humans Whole-genome resequencing of dogs and wolves has been used to identify genomic regions likely to represent targets for selection during dog domestication. Of 36 genes identified, more than half are brain-related including some linked to behavioural changes thought central to dog domestication. Surprisingly, ten genes that show signals of selection are important in starch digestion and fat metabolism — and modern dogs fare better than carnivorous wolves on a diet rich in starch. This evidence of dietary change suggests that dogs may have found a new ecological niche, scavenging waste from human settlements established during the agricultural revolution. The domestication of dogs was an important episode in the development of human civilization. The precise timing and location of this event is debated 1 , 2 , 3 , 4 , 5 and little is known about the genetic changes that accompanied the transformation of ancient wolves into domestic dogs. Here we conduct whole-genome resequencing of dogs and wolves to identify 3.8 million genetic variants used to identify 36 genomic regions that probably represent targets for selection during dog domestication. Nineteen of these regions contain genes important in brain function, eight of which belong to nervous system development pathways and potentially underlie behavioural changes central to dog domestication 6 . Ten genes with key roles in starch digestion and fat metabolism also show signals of selection. We identify candidate mutations in key genes and provide functional support for an increased starch digestion in dogs relative to wolves. Our results indicate that novel adaptations allowing the early ancestors of modern dogs to thrive on a diet rich in starch, relative to the carnivorous diet of wolves, constituted a crucial step in the early domestication of dogs.

Whereas large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the practical impact of variation is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 faecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short insertions/deletions, and 1,051 structural variants. The average ratio of non-synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This indicates that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake. A framework for metagenomic variation analysis to explore variation in the human microbiome is developed; the study describes SNPs, short indels and structural variants in 252 faecal metagenomes of 207 individuals from Europe and North America. Gene variation in human gut microbes A collaboration between members of the European MetaHIT and American NIH Human Microbiome projects has led to the development of a framework for metagenomic variation analysis, which is used to analyse single nucleotide polymorphisms, short indels and structural variants in 252 faecal metagenomes of 207 individuals from Europe and North America. Variation patterns suggest that individuals might have unique metagenomic genotypes that could provide data relevant to personalized dietary or drug choices.

Host genotype and gender are among the factors that influence the composition of gut microbiota. We studied the population structure of gut microbiota in two lines of chickens maintained under the same husbandry and dietary regimes. The lines, which originated from a common founder population, had undergone 54 generations of selection for high (HW) or low (LW) 56-day body weight and now differ by more than 10-fold in body weight at selection age. Of 190 microbiome species, 68 were affected by genotype (line), gender and genotype by gender interactions. Fifteen of the 68 species belong to Lactobacillus . Species affected by genotype, gender and the genotype by gender interaction, were 29, 48 and 12, respectively. Species affected by gender were 30 and 17 in the HW and LW lines, respectively. Thus, under a common diet and husbandry host quantitative genotype and gender influenced gut microbiota composite.

The torrents of data flowing out of cancer research and treatment are yielding fresh insight into the disease.

To identify comprehensively factors involved in RNAi and microRNA-mediated gene expression regulation, this study performed a phylogenetic analysis of 86 eukaryotic species; the candidates this approach highlighted were subjected to Bayesian analysis with transcriptional and proteomic interaction data, identifying protein orthologues of already known RNAi silencing factors, as well as other hits involved in splicing, suggesting a connection between the two processes. Linkage between RNA splicing and gene silencing In order to identify factors involved in RNA interference (RNAi) and microRNA-mediated gene-expression regulation, Gary Ruvkun and colleagues performed a phylogenetic analysis of 86 eukaryotic species. The resulting candidates were subjected to Bayesian analysis with transcriptional and proteomic interaction data, to estimate the probability of their involvement in small RNA regulation. About half of the small RNA cofactors identified are required for RNAi silencing, and many of the others are involved in splicing, suggesting a connection between the two processes. Genetic and biochemical analyses of RNA interference (RNAi) and microRNA (miRNA) pathways have revealed proteins such as Argonaute and Dicer as essential cofactors that process and present small RNAs to their targets. Well-validated small RNA pathway cofactors such as these show distinctive patterns of conservation or divergence in particular animal, plant, fungal and protist species. We compared 86 divergent eukaryotic genome sequences to discern sets of proteins that show similar phylogenetic profiles with known small RNA cofactors. A large set of additional candidate small RNA cofactors have emerged from functional genomic screens for defects in miRNA- or short interfering RNA (siRNA)-mediated repression in Caenorhabditis elegans and Drosophila melanogaster 1 , 2 , and from proteomic analyses of proteins co-purifying with validated small RNA pathway proteins 3 , 4 . The phylogenetic profiles of many of these candidate small RNA pathway proteins are similar to those of known small RNA cofactor proteins. We used a Bayesian approach to integrate the phylogenetic profile analysis with predictions from diverse transcriptional coregulation and proteome interaction data sets to assign a probability for each protein for a role in a small RNA pathway. Testing high-confidence candidates from this analysis for defects in RNAi silencing, we found that about one-half of the predicted small RNA cofactors are required for RNAi silencing. Many of the newly identified small RNA pathway proteins are orthologues of proteins implicated in RNA splicing. In support of a deep connection between the mechanism of RNA splicing and small-RNA-mediated gene silencing, the presence of the Argonaute proteins and other small RNA components in the many species analysed strongly correlates with the number of introns in those species.

We present an approach for genome-wide association analysis with improved power on the Wellcome Trust data consisting of seven common phenotypes and shared controls. We achieved improved power by expanding the control set to include other disease cohorts, multiple races and closely related individuals. Within this setting, we conducted exhaustive univariate and epistatic interaction association analyses. Use of the expanded control set identified more known associations with Crohn's disease and potential new biology, including several plausible epistatic interactions in several diseases. Our work suggests that carefully combining data from large repositories could reveal many new biological insights through increased power. As a community resource, all results have been made available through an interactive web server.

In response to a concern raised by a reader about inconsistencies in our Letter between the results from the primary microarray screen and cell growth validation studies (Supplementary Table 2), we reviewed the methods described, and the subsequentanalytical and validation work.

Sequence alignments form the basis for many comparative and population genomic studies. Alignment tools provide a range of accuracies dependent on the divergence between the sequences and the alignment methods. Despite widespread use, there is no standard method for assessing the accuracy of a dataset and alignment strategy after resequencing. We present a framework and tool for determining the overall accuracies of an input read dataset, alignment and SNP-calling method providing an isolate in that dataset has a corresponding, or closely related reference sequence available. In addition to this tool for comparing False Discovery Rates (FDR), we include a method for determining homozygous and heterozygous positions from an alignment using binomial probabilities for an expected error rate. We benchmark this method against other SNP callers using our FDR method with three fungal genomes, finding that it was able achieve a high level of accuracy. These tools are available at http://cfdr.sourceforge.net/ .

Recent genome-wide profiling reveals highly complex regulation networks among ERα and its targets. We integrated estrogen (E2)-stimulated time-series ERα ChIP-seq and gene expression data to identify the ERα-centered transcription factor (TF) hubs and their target genes and inferred the time-variant hierarchical network structures using a Bayesian multivariate modeling approach. With its recurrent motif patterns, we determined three embedded regulatory modules from the ERα core transcriptional network. The GO analyses revealed the distinct biological function associated with each of three embedded modules. The survival analysis showed the genes in each module were able to render a significant survival correlation in breast cancer patient cohorts. In summary, our Bayesian statistical modeling and modularity analysis not only reveals the dynamic properties of the ERα-centered regulatory network and associated distinct biological functions, but also provides a reliable and effective genomic analytical approach for the analysis of dynamic regulatory network for any given TF.