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A Genome-Wide Screen for Genetic Variants That Modify the Recruitment of REST to Its Target Genes

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      Increasing numbers of human diseases are being linked to genetic variants, but our understanding of the mechanistic links leading from DNA sequence to disease phenotype is limited. The majority of disease-causing nucleotide variants fall within the non-protein-coding portion of the genome, making it likely that they act by altering gene regulatory sequences. We hypothesised that SNPs within the binding sites of the transcriptional repressor REST alter the degree of repression of target genes. Given that changes in the effective concentration of REST contribute to several pathologies—various cancers, Huntington's disease, cardiac hypertrophy, vascular smooth muscle proliferation—these SNPs should alter disease-susceptibility in carriers. We devised a strategy to identify SNPs that affect the recruitment of REST to target genes through the alteration of its DNA recognition element, the RE1. A multi-step screen combining genetic, genomic, and experimental filters yielded 56 polymorphic RE1 sequences with robust and statistically significant differences of affinity between alleles. These SNPs have a considerable effect on the the functional recruitment of REST to DNA in a range of in vitro, reporter gene, and in vivo analyses. Furthermore, we observe allele-specific biases in deeply sequenced chromatin immunoprecipitation data, consistent with predicted differenes in RE1 affinity. Amongst the targets of polymorphic RE1 elements are important disease genes including NPPA, PTPRT, and CDH4. Thus, considerable genetic variation exists in the DNA motifs that connect gene regulatory networks. Recently available ChIP–seq data allow the annotation of human genetic polymorphisms with regulatory information to generate prior hypotheses about their disease-causing mechanism.

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      Common human diseases such as cancer, heart disease, or epilepsy have a genetic component that predisposes particular individuals to suffer from them. Huge sums have been invested to map the regions of the human genome where small DNA variations, or SNPs (“single-nucleotide polymorphisms”), determine the probability of developing these diseases. A major problem with this approach, however, is that, once the culprit SNPs are discovered, we know very little about how they cause disease—which is critical if we are to use this information to develop drugs and therapies. In this study, we demonstrate a new approach, employing functional maps of the human genome that have recently been published. We begin with regions of the genome recognised by a gene repressor protein—REST—that is involved in a number of important human diseases. Using information on where REST binds in the human genome, we predict and validate common DNA variations that increase or decrease this binding. By affecting how much REST is recruited to important genes, these variations may predispose or protect individuals from a number of diseases. Studies like this show how we can use genomic information to gain a deeper understanding of the genetics behind human disease.

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      Model-based Analysis of ChIP-Seq (MACS)

      Background The determination of the 'cistrome', the genome-wide set of in vivo cis-elements bound by trans-factors [1], is necessary to determine the genes that are directly regulated by those trans-factors. Chromatin immunoprecipitation (ChIP) [2] coupled with genome tiling microarrays (ChIP-chip) [3,4] and sequencing (ChIP-Seq) [5-8] have become popular techniques to identify cistromes. Although early ChIP-Seq efforts were limited by sequencing throughput and cost [2,9], tremendous progress has been achieved in the past year in the development of next generation massively parallel sequencing. Tens of millions of short tags (25-50 bases) can now be simultaneously sequenced at less than 1% the cost of traditional Sanger sequencing methods. Technologies such as Illumina's Solexa or Applied Biosystems' SOLiD™ have made ChIP-Seq a practical and potentially superior alternative to ChIP-chip [5,8]. While providing several advantages over ChIP-chip, such as less starting material, lower cost, and higher peak resolution, ChIP-Seq also poses challenges (or opportunities) in the analysis of data. First, ChIP-Seq tags represent only the ends of the ChIP fragments, instead of precise protein-DNA binding sites. Although tag strand information and the approximate distance to the precise binding site could help improve peak resolution, a good tag to site distance estimate is often unknown to the user. Second, ChIP-Seq data exhibit regional biases along the genome due to sequencing and mapping biases, chromatin structure and genome copy number variations [10]. These biases could be modeled if matching control samples are sequenced deeply enough. However, among the four recently published ChIP-Seq studies [5-8], one did not have a control sample [5] and only one of the three with control samples systematically used them to guide peak finding [8]. That method requires peaks to contain significantly enriched tags in the ChIP sample relative to the control, although a small ChIP peak region often contains too few control tags to robustly estimate the background biases. Here, we present Model-based Analysis of ChIP-Seq data, MACS, which addresses these issues and gives robust and high resolution ChIP-Seq peak predictions. We conducted ChIP-Seq of FoxA1 (hepatocyte nuclear factor 3α) in MCF7 cells for comparison with FoxA1 ChIP-chip [1] and identification of features unique to each platform. When applied to three human ChIP-Seq datasets to identify binding sites of FoxA1 in MCF7 cells, NRSF (neuron-restrictive silencer factor) in Jurkat T cells [8], and CTCF (CCCTC-binding factor) in CD4+ T cells [5] (summarized in Table S1 in Additional data file 1), MACS gives results superior to those produced by other published ChIP-Seq peak finding algorithms [8,11,12]. Results Modeling the shift size of ChIP-Seq tags ChIP-Seq tags represent the ends of fragments in a ChIP-DNA library and are often shifted towards the 3' direction to better represent the precise protein-DNA interaction site. The size of the shift is, however, often unknown to the experimenter. Since ChIP-DNA fragments are equally likely to be sequenced from both ends, the tag density around a true binding site should show a bimodal enrichment pattern, with Watson strand tags enriched upstream of binding and Crick strand tags enriched downstream. MACS takes advantage of this bimodal pattern to empirically model the shifting size to better locate the precise binding sites. Given a sonication size (bandwidth) and a high-confidence fold-enrichment (mfold), MACS slides 2bandwidth windows across the genome to find regions with tags more than mfold enriched relative to a random tag genome distribution. MACS randomly samples 1,000 of these high-quality peaks, separates their Watson and Crick tags, and aligns them by the midpoint between their Watson and Crick tag centers (Figure 1a) if the Watson tag center is to the left of the Crick tag center. The distance between the modes of the Watson and Crick peaks in the alignment is defined as 'd', and MACS shifts all the tags by d/2 toward the 3' ends to the most likely protein-DNA interaction sites. Figure 1 MACS model for FoxA1 ChIP-Seq. (a,b) The 5' ends of strand-separated tags from a random sample of 1,000 model peaks, aligned by the center of their Watson and Crick peaks (a) and by the FKHR motif (b). (c) The tag count in ChIP versus control in 10 kb windows across the genome. Each dot represents a 10 kb window; red dots are windows containing ChIP peaks and black dots are windows containing control peaks used for FDR calculation. (d) Tag density profile in control samples around FoxA1 ChIP-Seq peaks. (e,f) MACS improves the motif occurrence in the identified peak centers (e) and the spatial resolution (f) for FoxA1 ChIP-Seq through tag shifting and λlocal. Peaks are ranked by p-value. The motif occurrence is calculated as the percentage of peaks with the FKHR motif within 50 bp of the peak summit. The spatial resolution is calculated as the average distance from the summit to the nearest FKHR motif. Peaks with no FKHR motif within 150 bp of the peak summit are removed from the spatial resolution calculation. When applied to FoxA1 ChIP-Seq, which was sequenced with 3.9 million uniquely mapped tags, MACS estimates the d to be only 126 bp (Figure 1a; suggesting a tag shift size of 63 bp), despite a sonication size (bandwidth) of around 500 bp and Solexa size-selection of around 200 bp. Since the FKHR motif sequence dictates the precise FoxA1 binding location, the true distribution of d could be estimated by aligning the tags by the FKHR motif (122 bp; Figure 1b), which gives a similar result to the MACS model. When applied to NRSF and CTCF ChIP-Seq, MACS also estimates a reasonable d solely from the tag distribution: for NRSF ChIP-Seq the MACS model estimated d as 96 bp compared to the motif estimate of 70 bp; applied to CTCF ChIP-Seq data the MACS model estimated a d of 76 bp compared to the motif estimate of 62 bp. Peak detection For experiments with a control, MACS linearly scales the total control tag count to be the same as the total ChIP tag count. Sometimes the same tag can be sequenced repeatedly, more times than expected from a random genome-wide tag distribution. Such tags might arise from biases during ChIP-DNA amplification and sequencing library preparation, and are likely to add noise to the final peak calls. Therefore, MACS removes duplicate tags in excess of what is warranted by the sequencing depth (binomial distribution p-value 2). Among the remaining 34.6% ChIP-Seq unique peaks, 1,045 (13.3%) were not tiled or only partially tiled on the arrays due to the array design. Therefore, only 21.4% of ChIP-Seq peaks are indeed specific to the sequencing platform. Furthermore, ChIP-chip targets with higher fold-enrichments are more likely to be reproducibly detected by ChIP-Seq with a higher tag count (Figure 3b). Meanwhile, although the signals of array probes at the ChIP-Seq specific peak regions are below the peak-calling cutoff, they show moderate signal enrichments that are significantly higher than the genomic background (Wilcoxon p-value 2) and ChIP-Seq (MACS; FDR <1%). Shown are the numbers of regions detected by both platforms (that is, having at least 1 bp in common) or unique to each platform. (b) The distributions of ChIP-Seq tag number and ChIP-chip MATscore [13] for FoxA1 binding sites identified by both platforms. (c) MATscore distributions of FoxA1 ChIP-chip at ChIP-Seq/chip overlapping peaks, ChIP-Seq unique peaks, and genome background. For each peak, the mean MATscore for all probes within the 300 bp region centered at the ChIP-Seq peak summit is used. Genome background is based on MATscores of all array probes in the FoxA1 ChIP-chip data. (d) Width distributions of FoxA1 ChIP-Seq/chip overlapping peaks and ChIP-Seq unique peaks at different fold-enrichments (less than 25, 25 to 50, and larger than 50). (e) Spatial resolution for FoxA1 ChIP-chip and ChIP-Seq peaks. The Wilcoxon test was used to calculate the p-values for (d) and (e). (f) Motif occurrence within the central 200 bp regions for FoxA1 ChIP-Seq/chip overlapping peaks and platform unique peaks. Error bars showing standard deviation were calculated from random sampling of 500 peaks ten times for each category. Background motif occurrences are based on 100,000 randomly selected 200 bp regions in the human genome, excluding regions in genome assembly gaps (containing 'N'). Comparing the difference between ChIP-chip and ChIP-Seq peaks, we find that the average peak width from ChIP-chip is twice as large as that from ChIP-Seq. The average distance from peak summit to motif is significantly smaller in ChIP-Seq than ChIP-chip (Figure 3e), demonstrating the superior resolution of ChIP-Seq. Under the same 1% FDR cutoff, the FKHR motif occurrence within the central 200 bp from ChIP-chip or ChIP-Seq specific peaks is comparable with that from the overlapping peaks (Figure 3f). This suggests that most of the platform-specific peaks are genuine binding sites. A comparison between NRSF ChIP-Seq and ChIP-chip (Figure S3 in Additional data file 1) yields similar results, although the overlapping peaks for NRSF are of much better quality than the platform-specific peaks. Discussion ChIP-Seq users are often curious as to whether they have sequenced deep enough to saturate all the binding sites. In principle, sequencing saturation should be dependent on the fold-enrichment, since higher-fold peaks are saturated earlier than lower-fold ones. In addition, due to different cost and throughput considerations, different users might be interested in recovering sites at different fold-enrichment cutoffs. Therefore, MACS produces a saturation table to report, at different fold-enrichments, the proportion of sites that could still be detected when using 90% to 20% of the tags. Such tables produced for FoxA1 (3.9 million tags) and NRSF (2.2 million tags) ChIP-Seq data sets (Figure S4 in Additional data file 1; CTCF does not have a control to robustly estimate fold-enrichment) show that while peaks with over 60-fold enrichment have been saturated, deeper sequencing could still recover more sites less than 40-fold enriched relative to the chromatin input DNA. As sequencing technologies improve their throughput, researchers are gradually increasing their sequencing depth, so this question could be revisited in the future. For now, we leave it up to individual users to make an informed decision on whether to sequence more based on the saturation at different fold-enrichment levels. The d modeled by MACS suggests that some short read sequencers such as Solexa may preferentially sequence shorter fragments in a ChIP-DNA pool. This may contribute to the superior resolution observed in ChIP-Seq data, especially for activating transcription and epigenetic factors in open chromatin. However, for repressive factors targeting relatively compact chromatin, the target regions might be harder to sonicate into the soluble extract. Furthermore, in the resulting ChIP-DNA, the true targets may tend to be longer than the background DNA in open chromatin, making them unfavorable for size-selection and sequencing. This implies that epigenetic markers of closed chromatin may be harder to ChIP, and even harder to ChIP-Seq. To assess this potential bias, examining the histone mark ChIP-Seq results from Mikkelsen et al. [7], we find that while the ChIP-Seq efficiency of the active mark H3K4me3 remains high as pluripotent cells differentiate, that of repressive marks H3K27me3 and H3K9me3 becomes lower with differentiation (Table S2 in Additional data file 1), even though it is likely that there are more targets for these repressive marks as cells differentiate. We caution ChIP-Seq users to adopt measures to compensate for this bias when ChIPing repressive marks, such as more vigorous sonication, size-selecting slightly bigger fragments for library preparation, or sonicating the ChIP-DNA further between decrosslinking and library preparation. MACS calculates the FDR based on the number of peaks from control over ChIP that are called at the same p-value cutoff. This FDR estimate is more robust than calculating the FDR from randomizing tags along the genome. However, we notice that when tag counts from ChIP and controls are not balanced, the sample with more tags often gives more peaks even though MACS normalizes the total tag counts between the two samples (Figure S5 in Additional data file 1). While we await more available ChIP-Seq data with deeper coverage to understand and overcome this bias, we suggest to ChIP-Seq users that if they sequence more ChIP tags than controls, the FDR estimate of their ChIP peaks might be overly optimistic. Conclusion As developments in sequencing technology popularize ChIP-Seq, we propose a novel algorithm, MACS, for its data analysis. MACS offers four important utilities for predicting protein-DNA interaction sites from ChIP-Seq. First, MACS improves the spatial resolution of the predicted sites by empirically modeling the distance d and shifting tags by d/2. Second, MACS uses a dynamic λlocal parameter to capture local biases in the genome and improves the robustness and specificity of the prediction. It is worth noting that in addition to ChIP-Seq, λlocal can potentially be applied to other high throughput sequencing applications, such as copy number variation and digital gene expression, to capture regional biases and estimate robust fold-enrichment. Third, MACS can be applied to ChIP-Seq experiments without controls, and to those with controls with improved performance. Last but not least, MACS is easy to use and provides detailed information for each peak, such as genome coordinates, p-value, FDR, fold_enrichment, and summit (peak center). Materials and methods Dataset ChIP-Seq data for three factors, NRSF, CTCF, and FoxA1, were used in this study. ChIP-chip and ChIP-Seq (2.2 million ChIP and 2.8 million control uniquely mapped reads, simplified as 'tags') data for NRSF in Jurkat T cells were obtained from Gene Expression Omnibus (GSM210637) and Johnson et al. [8], respectively. ChIP-Seq (2.9 million ChIP tags) data for CTCF in CD4+ T cells were derived from Barski et al. [5]. ChIP-chip data for FoxA1 and controls in MCF7 cells were previously published [1], and their corresponding ChIP-Seq data were generated specifically for this study. Around 3 ng FoxA1 ChIP DNA and 3 ng control DNA were used for library preparation, each consisting of an equimolar mixture of DNA from three independent experiments. Libraries were prepared as described in [8] using a PCR preamplification step and size selection for DNA fragments between 150 and 400 bp. FoxA1 ChIP and control DNA were each sequenced with two lanes by the Illumina/Solexa 1G Genome Analyzer, and yielded 3.9 million and 5.2 million uniquely mapped tags, respectively. Software implementation MACS is implemented in Python and freely available with an open source Artistic License at [16]. It runs from the command line and takes the following parameters: -t for treatment file (ChIP tags, this is the ONLY required parameter for MACS) and -c for control file containing mapped tags; --format for input file format in BED or ELAND (output) format (default BED); --name for name of the run (for example, FoxA1, default NA); --gsize for mappable genome size to calculate λBG from tag count (default 2.7G bp, approximately the mappable human genome size); --tsize for tag size (default 25); --bw for bandwidth, which is half of the estimated sonication size (default 300); --pvalue for p-value cutoff to call peaks (default 1e-5); --mfold for high-confidence fold-enrichment to find model peaks for MACS modeling (default 32); --diag for generating the table to evaluate sequence saturation (default off). In addition, the user has the option to shift tags by an arbitrary number (--shiftsize) without the MACS model (--nomodel), to use a global lambda (--nolambda) to call peaks, and to show debugging and warning messages (--verbose). If a user has replicate files for ChIP or control, it is recommended to concatenate all replicates into one input file. The output includes one BED file containing the peak chromosome coordinates, and one xls file containing the genome coordinates, summit, p-value, fold_enrichment and FDR (if control is available) of each peak. For FoxA1 ChIP-Seq in MCF7 cells with 3.9 million and 5.2 million ChIP and control tags, respectively, it takes MACS 15 seconds to model the ChIP-DNA size distribution and less than 3 minutes to detect peaks on a 2 GHz CPU Linux computer with 2 GB of RAM. Figure S6 in Additional data file 1 illustrates the whole process with a flow chart. Abbreviations ChIP, chromatin immunoprecipitation; CTCF, CCCTC-binding factor; FDR, false discovery rate; FoxA1, hepatocyte nuclear factor 3α; MACS, Model-based Analysis of ChIP-Seq data; NRSF, neuron-restrictive silencer factor. Authors' contributions XSL, WL and YZ conceived the project and wrote the paper. YZ, TL and CAM designed the algorithm, performed the research and implemented the software. JE, DSJ, BEB, CN, RMM and MB performed FoxA1 ChIP-Seq experiments and contributed to ideas. All authors read and approved the final manuscript. Additional data files The following additional data are available. Additional data file 1 contains supporting Figures S1-S6, and supporting Tables S1 and S2. Supplementary Material Additional data file 1 Figures S1-S6, and Tables S1 and S2. Click here for file
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        Galaxy: a platform for interactive large-scale genome analysis.

        Accessing and analyzing the exponentially expanding genomic sequence and functional data pose a challenge for biomedical researchers. Here we describe an interactive system, Galaxy, that combines the power of existing genome annotation databases with a simple Web portal to enable users to search remote resources, combine data from independent queries, and visualize the results. The heart of Galaxy is a flexible history system that stores the queries from each user; performs operations such as intersections, unions, and subtractions; and links to other computational tools. Galaxy can be accessed at
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          Genome-wide mapping of in vivo protein-DNA interactions.

          In vivo protein-DNA interactions connect each transcription factor with its direct targets to form a gene network scaffold. To map these protein-DNA interactions comprehensively across entire mammalian genomes, we developed a large-scale chromatin immunoprecipitation assay (ChIPSeq) based on direct ultrahigh-throughput DNA sequencing. This sequence census method was then used to map in vivo binding of the neuron-restrictive silencer factor (NRSF; also known as REST, for repressor element-1 silencing transcription factor) to 1946 locations in the human genome. The data display sharp resolution of binding position [+/-50 base pairs (bp)], which facilitated our finding motifs and allowed us to identify noncanonical NRSF-binding motifs. These ChIPSeq data also have high sensitivity and specificity [ROC (receiver operator characteristic) area >/= 0.96] and statistical confidence (P <10(-4)), properties that were important for inferring new candidate interactions. These include key transcription factors in the gene network that regulates pancreatic islet cell development.

            Author and article information

            [1 ]Stem Cell and Developmental Biology Group, Genome Institute of Singapore, Singapore, Singapore
            [2 ]Laboratory of Structural Biochemistry, Genome Institute of Singapore, Singapore, Singapore
            [3 ]Bioinformatics and Genomics Group, Centre for Genomic Regulation (CRG) and UPF, Barcelona, Spain
            [4 ]Department of Biological Sciences, National University of Singapore, Singapore, Singapore
            Friedrich Miescher Institute for Biomedical Research, Switzerland
            Author notes

            ¤a: Current address: Bioinformatics and Genomics Group, Centre for Genomic Regulation (CRG) and UPF, Barcelona, Spain

            ¤b: Current address: Barcelona Supercomputing Center, Barcelona, Spain

            Conceived and designed the experiments: R Johnson, R Jauch, LW Stanton. Performed the experiments: N Richter, A Bhinge, SW Teng, SH Choo, LO Andrieux, C De Benedictis. Analyzed the data: R Johnson, GK Bogu, R Jauch. Wrote the paper: R Johnson, LW Stanton.

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            PLoS Genet
            PLoS Genet
            PLoS Genetics
            Public Library of Science (San Francisco, USA )
            April 2012
            April 2012
            5 April 2012
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            Johnson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Pages: 13
            Research Article
            Computational Biology
            Clinical Genetics



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