CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets.

UNLABELLED: Promoter capture Hi-C (PCHi-C) allows the genome-wide interrogation of physical interactions between distal DNA regulatory elements and gene promoters in multiple tissue contexts. Visual integration of the resultant chromosome interaction maps with other sources of genomic annotations can provide insight into underlying regulatory mechanisms. We have developed Capture HiC Plotter (CHiCP), a web-based tool that allows interactive exploration of PCHi-C interaction maps and integration with both public and user-defined genomic datasets. AVAILABILITY AND IMPLEMENTATION: CHiCP is freely accessible from www.chicp.org and supports most major HTML5 compliant web browsers. Full source code and installation instructions are available from http://github.com/D-I-L/django-chicp CONTACT: [email protected] is the published version. It first appeared at http://bioinformatics.oxfordjournals.org/content/early/2016/04/26/bioinformatics.btw173

Countercurrent chromatography in analytical chemistry (IUPAC technical report)

© 2009 IUPACCountercurrent chromatography (CCC) is a generic term covering all forms of liquid-liquid chromatography that use a support-free liquid stationary phase held in place by a simple centrifugal or complex centrifugal force field. Biphasic liquid systems are used with one liquid phase being the stationary phase and the other being the mobile phase. Although initiated almost 30 years ago, CCC lacked reliable columns. This is changing now, and the newly designed centrifuges appearing on the market make excellent CCC columns. This review focuses on the advantages of a liquid stationary phase and addresses the chromatographic theory of CCC. The main difference with classical liquid chromatography (LC) is the variable volume of the stationary phase. There are mainly two different ways to obtain a liquid stationary phase using centrifugal forces, the hydrostatic way and the hydrodynamic way. These two kinds of CCC columns are described and compared. The reported applications of CCC in analytical chemistry and comparison with other separation and enrichment methods show that the technique can be successfully used in the analysis of plants and other natural products, for the separation of biochemicals and pharmaceuticals, for the separation of alkaloids from medical herbs, in food analysis, etc. On the basis of the studies of the last two decades, recommendations are also given for the application of CCC in trace inorganic analysis and in radioanalytical chemistry

Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters.

Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of 'epromoters': dual-action CRMs with promoter and distal enhancer activity

A systematic, large-scale comparison of transcription factor binding site models

Background The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified “real” in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. Results While the area- under-curve was low for most of the tested models with only 47 % reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. Conclusions Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM (http:/mutationtaster.charite.de/ePOSSUM/) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites

Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era

Abstract Background Genome-scale studies of psoriasis have been used to identify genes of potential relevance to disease mechanisms. For many identified genes, however, the cell type mediating disease activity is uncertain, which has limited our ability to design gene functional studies based on genomic findings. Methods We identified differentially expressed genes (DEGs) with altered expression in psoriasis lesions (n = 216 patients), as well as candidate genes near susceptibility loci from psoriasis GWAS studies. These gene sets were characterized based upon their expression across 10 cell types present in psoriasis lesions. Susceptibility-associated variation at intergenic (non-coding) loci was evaluated to identify sites of allele-specific transcription factor binding. Results Half of DEGs showed highest expression in skin cells, although the dominant cell type differed between psoriasis-increased DEGs (keratinocytes, 35%) and psoriasis-decreased DEGs (fibroblasts, 33%). In contrast, psoriasis GWAS candidates tended to have highest expression in immune cells (71%), with a significant fraction showing maximal expression in neutrophils (24%, P < 0.001). By identifying candidate cell types for genes near susceptibility loci, we could identify and prioritize SNPs at which susceptibility variants are predicted to influence transcription factor binding. This led to the identification of potentially causal (non-coding) SNPs for which susceptibility variants influence binding of AP-1, NF-κB, IRF1, STAT3 and STAT4. Conclusions These findings underscore the role of innate immunity in psoriasis and highlight neutrophils as a cell type linked with pathogenetic mechanisms. Assignment of candidate cell types to genes emerging from GWAS studies provides a first step towards functional analysis, and we have proposed an approach for generating hypotheses to explain GWAS hits at intergenic loci.http://deepblue.lib.umich.edu/bitstream/2027.42/109537/1/12920_2013_Article_485.pd

Lineage-specific dynamic and pre-established enhancer–promoter contacts cooperate in terminal differentiation

Chromosome conformation is an important feature of metazoan gene regulation; however, enhancer–promoter contact remodeling during cellular differentiation remains poorly understood. To address this, genome-wide promoter capture Hi-C (CHi-C) was performed during epidermal differentiation. Two classes of enhancer–promoter contacts associated with differentiation-induced genes were identified. The first class ('gained') increased in contact strength during differentiation in concert with enhancer acquisition of the H3K27ac activation mark. The second class ('stable') were pre-established in undifferentiated cells, with enhancers constitutively marked by H3K27ac. The stable class was associated with the canonical conformation regulator cohesin, whereas the gained class was not, implying distinct mechanisms of contact formation and regulation. Analysis of stable enhancers identified a new, essential role for a constitutively expressed, lineage-restricted ETS-family transcription factor, EHF, in epidermal differentiation. Furthermore, neither class of contacts was observed in pluripotent cells, suggesting that lineage-specific chromatin structure is established in tissue progenitor cells and is further remodeled in terminal differentiation

Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments

Recent advances in technology have led to a dramatic increase in the number of available transcription factor ChIP-seq and ChIP-chip data sets. Understanding the motif content of these data sets is an important step in understanding the underlying mechanisms of regulation. Here we provide a systematic motif analysis for 427 human ChIP-seq data sets using motifs curated from the literature and also discovered de novo using five established motif discovery tools. We use a systematic pipeline for calculating motif enrichment in each data set, providing a principled way for choosing between motif variants found in the literature and for flagging potentially problematic data sets. Our analysis confirms the known specificity of 41 of the 56 analyzed factor groups and reveals motifs of potential cofactors. We also use cell type-specific binding to find factors active in specific conditions. The resource we provide is accessible both for browsing a small number of factors and for performing large-scale systematic analyses. We provide motif matrices, instances and enrichments in each of the ENCODE data sets. The motifs discovered here have been used in parallel studies to validate the specificity of antibodies, understand cooperativity between data sets and measure the variation of motif binding across individuals and species.National Institutes of Health (U.S.) (HG004037)National Institutes of Health (U.S.) (HG007000)National Institutes of Health (U.S.) (HG006991

Stochastic EM-based TFBS motif discovery with MITSU

Motivation: The Expectation–Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the application of sEM to motif discovery has not been fully explored. Results: We present MITSU (Motif discovery by ITerative Sampling and Updating), a novel algorithm for motif discovery, which combines sEM with an improved approximation to the likelihood function, which is unconstrained with regard to the distribution of motif occurrences within the input dataset. The algorithm is evaluated quantitatively on realistic synthetic data and several collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternative sEM-based algorithm, particularly in terms of site-level positive predictive value. Availability and implementation: Java executable available for download at http://www.sourceforge.net/p/mitsu-motif/, supported on Linux/OS X. Contact: [email protected]

The impact of chromatin modifiers on the timing of locus replication in mouse embryonic stem cells

A panel of mutant embryonic stem (ES) cell lines lacking important chromatin modifiers was used to dissect the relationship between chromatin structure and replication timing, revealing the importance of several chromatin modifiers for maintaining correct replication of satellite sequences in pluripotent ES cells

Disease-relevant transcriptional signatures identified in individual smooth muscle cells from healthy mouse vessels.

Vascular smooth muscle cells (VSMCs) show pronounced heterogeneity across and within vascular beds, with direct implications for their function in injury response and atherosclerosis. Here we combine single-cell transcriptomics with lineage tracing to examine VSMC heterogeneity in healthy mouse vessels. The transcriptional profiles of single VSMCs consistently reflect their region-specific developmental history and show heterogeneous expression of vascular disease-associated genes involved in inflammation, adhesion and migration. We detect a rare population of VSMC-lineage cells that express the multipotent progenitor marker Sca1, progressively downregulate contractile VSMC genes and upregulate genes associated with VSMC response to inflammation and growth factors. We find that Sca1 upregulation is a hallmark of VSMCs undergoing phenotypic switching in vitro and in vivo, and reveal an equivalent population of Sca1-positive VSMC-lineage cells in atherosclerotic plaques. Together, our analyses identify disease-relevant transcriptional signatures in VSMC-lineage cells in healthy blood vessels, with implications for disease susceptibility, diagnosis and prevention.BH