AHT-ChIP-seq: a completely automated robotic protocol for high-throughput chromatin immunoprecipitation
- Sarah Aldridge†1,
- Stephen Watt†1,
- Michael A Quail2,
- Tim Rayner1,
- Margus Lukk1,
- Michael F Bimson3,
- Daniel Gaffney2 and
- Duncan T Odom1, 2Email author
© Aldridge et al.; licensee BioMed Central Ltd. 2013
Received: 22 May 2013
Accepted: 7 November 2013
Published: 7 November 2013
ChIP-seq is an established manually-performed method for identifying DNA-protein interactions genome-wide. Here, we describe a protocol for automated high-throughput (AHT) ChIP-seq. To demonstrate the quality of data obtained using AHT-ChIP-seq, we applied it to five proteins in mouse livers using a single 96-well plate, demonstrating an extremely high degree of qualitative and quantitative reproducibility among biological and technical replicates. We estimated the optimum and minimum recommended cell numbers required to perform AHT-ChIP-seq by running an additional plate using HepG2 and MCF7 cells. With this protocol, commercially available robotics can perform four hundred experiments in five days.
The ability to decipher regulatory information held in the genome and epigenome is essential to understanding how transcription is controlled or perturbed through natural genetic variation and in diseased states. Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) has become a widely used method to identify regulatory DNA sequence directly occupied by transcription factors, basal transcriptional machinery, and specifically covalently modified histones.
Previous large-scale ChIP studies required enormous manual experiments or large consortia. Genome-wide data sets for epigenetic information have demonstrated the role of chromatin organization in genome function in single cell types [1–3]. These chromatin maps display histone modifications that demarcate different regulatory regions of the genome such as promoters and gene bodies, or regulatory states such as active or repressed transcription. By contrast, to achieve the scale required for the ENCODE consortia, performing approximately 2,100 ChIP experiments required contributions from nine laboratories to profile the DNA binding patterns from numerous factors and multiple cell types . Further research into how this epigenetic layer of information correlates with genotype and phenotype will require hundreds to thousands more protein-DNA binding assays in diverse cell types and/or temporal studies during cell development [5–7].
A recent study implemented semi-automated analysis of multiple transcription factors and epigenetic marks in the transcriptional regulatory networks of a single immune cell type during pathogen response . The powerful method developed in that study  permitted characterization of 400 different protein-DNA binding interactions genome-wide, and demonstrated the increase in productivity that would be unlocked by developing completely automated protocols to minimize manual intervention and maximize throughput. Indeed, this landmark study would be the first of many, if ChIP experiments could be fully automated.
The scale afforded by full automation of ChIP experiments would enable disease genomics in patient cohorts that could connect genotype with cellular phenotype. For example, single nucleotide polymorphisms and small genetic aberrations found in natural human genetic variation have been shown to effect transcription factor binding and transcription itself in studies using ChIP-seq . The ability to map at high resolution targets within the genome for factors such as estrogen receptor-α, a major driver of cell growth in breast cancer, in individual tumors has demonstrated the link between a DNA binding protein, its effect on gene regulation, and disease outcome .
Finally, the ability to map hundreds of protein-DNA contacts genome-wide in a rapid, reproducible, and fully automated manner would revolutionize the scale and power of interspecies comparisons of transcription factor binding and epigenetics [12, 13]. Prior studies have been typically restricted to three to five species of protein-DNA data , which have been painstakingly collected using manual ChIP experiments; indeed, the standard ChIP-seq protocol performed in our laboratory, detailed in , is laborious. Combined with the increasing number of reference genomes and ChIP-validated antibodies, full ChIP automation could reveal the evolution of combinatorial networks of tissue-specific transcription factors, chromatin state, RNA polymerase binding, and RNA transcription across potentially hundreds of mammalian species.
Here, we report a fully automated high-throughput ChIP-seq (AHT-ChIP-seq) robotics protocol that starts with sonicated chromatin and ends at multiplexed Illumina DNA library preparation. We comprehensively compared the robotic ChIP experiments against CEBPA, a tissue-specific transcription factor, in a well-studied mammalian tissue (mouse liver) to manually obtain protein-DNA mapping experiments. We further demonstrate automated profiling of the genome-wide occupancy of an additional tissue-specific transcription factor (HNF4A), trimethylation of H3K4, RAD21 (a cohesin subunit), and the transcriptional co-activator p300 in liver. Finally, we estimate the minimum and optimal cell numbers a typical ChIP experiment on the plate would require by profiling trimethylation of H3K4 in titrated cell numbers of HepG2 and MCF7 cells, both of which are widely used human cancer models.
Results and discussion
Scaling-down ChIP to a 96-well automatable format
We first carried out a series of experiments to optimize the volumes of both input chromatin and antibodies, and eliminate chemically noxious steps employed by standard protocols . For development and evaluation purposes, we utilized the well-characterized liver transcription factor CEBPA as a benchmark , because our laboratory primarily uses liver as a model system for interspecies comparisons of ChIP experiments.
A traditional manual experiment for isolating DNA bound to the transcription factor CEBPA would use one-third of an adult mouse liver (well in excess of 100 million cells), 10 μg of antibody bound to 100 μl of Protein G magnetic beads, in a 3 ml hybridization volume. Operating a high-throughput method based around mouse liver would increase animal requirements significantly: based on our current laboratory protocol, 32 mouse livers would be needed to perform 96 ChIP experiments.
First, we established that CEBPA ChIP could be performed using one-sixteenth of a mouse liver in a 200 μl lysis buffer volume using quantitative PCR (Figure S1 in Additional file 1). Using this standard, six mouse livers would suffice to perform 96 ChIP experiments. Antibodies represent a significant proportion of the cost when performing a high-throughput experiment. We therefore tested the affect of antibody concentration on the CEBPA ChIP enrichment. We determined that 2.5 μg of CEBPA antibody when combined with one-sixteenth of an adult mouse liver produced adequate ChIP-seq enrichment (Figure S1 in Additional file 1). Although we expect our guideline amounts will be largely adaptable to typical mammalian ChIP experiments, other specialized ChIP experiments may well require additional optimization of antibodies and cell/tissue quantities.
Produce a homogeneous suspension of magnetic beads through mixing
Remove all supernatant without disturbing Protein G beads or Ampure beads whilst plate on a magnet
Transfer volumes precisely
Mix RIPA solution without foaming.
Evaluation of AHT-ChIP-seq and comparison with manual ChIP-seq
To demonstrate the quality and type of data a typical user could obtain from the AHT-ChIP-seq platform, we dissected a set of simultaneously performed ChIP-seq experiments, including both technical and biological replicates, in one representative somatic tissue; together, these experiments capture multiple layers of functional information.
We performed 55 ChIP reactions on a single 96-well plate plus matched sonicated DNA controls. We used five individual mouse livers, each of which was used to generate technical triplicate ChIP experiments for CEBPA and HNF4A; duplicate ChIPs of RAD21 and p300; and singlicate ChIPs of H3K4me3. The ChIP-enriched DNA was then used to create multiplexed libraries for Illumina sequencing reactions (see Materials and Methods), followed by paired-end sequencing on a HiSeq2000 with read length of 75 bp. Reads passing quality control were aligned to the mouse genome (NCBI mm9) using BWA version 0.6.1  (Table S1 in Additional file 4). Aligned data from our own previously published manual data sets [17, 18] displayed a high degree of correspondence with AHT-ChIP-seq libraries (Figure 1B) as further detailed below. These three manual experiments were selected as the best examples from over 200 manual CEBPA ChIP-seq experiments available in our laboratory.
First, we directly compared the 15 ChIP-seq replicates of CEBPA obtained using the robot with three CEBPA ChIP-seq experiments our laboratory had previously generated manually (as detailed above). Regions of enrichment for CEBPA-bound DNA were identified in the three manual ChIP-seq experiments and our 15 AHT-ChIP-seq by the peak calling algorithm MACS .
As an initial quality control of our ChIP libraries, we performed a cross-correlation analysis to confirm the quality of the ChIP experiments, as has been implemented by the ENCODE consortia . A high-quality ChIP-seq experiment affords a high density of sequencing tags directly surrounding the protein-DNA contact location, which accumulate on both the forward and reverse strands centered around the binding site. By contrast, ChIP-seq experiments with poor ChIP enrichment lack these features, and instead show variable and dispersed distribution of reads across the genome. Further, in poor quality experiments, a phantom peak can be observed when performing cross-correlation analysis, which corresponds with read length. Use of this cross-correlation metric confirmed that 13 out of our 15 CEBPA AHT-ChIP-seq reactions were extremely high quality, and identified two ChIP experiments as lower quality, both from a single mouse (Mmu3), which presented non-specific peaks (Figure S3 in Additional file 1).
We next asked what sources of experimental variation contribute to inter-replicate differences in the complete set of CEBPA ChIP experiments. By performing a principal component analysis, utilizing both the genomic intervals (peaks) and aligned reads, we observed a high degree of correspondence between 13 of our AHT-ChIP-seq CEBPA experiments (Figure 2B). The two outliers obtained using AHT-ChIP-seq were the two lower-quality ChIP experiments that showed relatively poor cross-correlation metrics (see above). As might be expected, the three manual ChIP-seq experiments were segregated from the AHT experiments; these experiments were performed at different times by different researchers in tissues from different mouse litters.
Next, we compared how well technical CEBPA ChIP replicates within a single mouse compared to each other. As a reference, we used the three biological replicate experiments performed manually. On average, the AHT-ChIP-seq CEBPA derived experiments identified 27,000 binding sites common to each biological replicate (Figure S4 in Additional file 1). To evaluate the reproducibility of peak calls within our five biological replicates, we used the overlap rate function in the R/Bioconductor DiffBind package  (Figure S5 in Additional file 1). In summary, CEBPA-bound intervals were consolidated into union sets for each independent mouse liver; we then asked how many peaks from this union set occurred within increasing numbers of replicates. The degree of overlap among peak-sets for two replicates was about 60% and 40% among three replicates. By this measure, the level of overlap demonstrated that technical reproducibility between matched mouse livers done under exacting conditions was at least equal to experiments carried out using standard manual methods on three separate biological samples.
To further evaluate the effectiveness of our AHT-ChIP-seq method, taking a similar approach to , we calculated a simple score for ChIP enrichment as the percentage of aligned reads located within identified peak intervals. We established a benchmark set of genomic intervals bound with high confidence by CEBPA in all three previously published manual ChIP experiments (n = 46,664; median length = 634 bp) (Figure S4 in Additional file 1). We next calculated the proportion of aligned reads that intersected this benchmark set by randomly sampling five million reads from each of our 15 AHT-ChIP-seq and the three manual ChIP-seq experiments, as well as sonicated input DNA control. Using BedTools and controlling for input DNA , we identified the fraction of aligned reads located within the benchmark set. There was a high degree of correlation (Pearson r2 = 0.79) between the ChIP enrichment score and the number of identified peaks for each replicate (Figure 2C).
The overall quality of robotic reactions was very high, with half the automated replicates comparable to the manual method by these measures, although the ChIP enrichment score for the manual method was modestly higher than the AHT-ChIP-seq method, likely due to the larger amount of tissue and antisera used in manual ChIP reactions. Because there was little correlation between sequencing depth and the number of CEBPA-bound regions identified (Figure 2D), sequencing depth did not appear to limit our ability to capture ChIP-enriched regions. The two exceptions were the lower-quality experiments, (Mmu3_r2 and Mmu3_r3) mentioned above: both were under-represented in their sequencing pools (approximately six million aligned reads) and had the least number of peaks identified by MACS peak calling.
We calculated the fraction of peaks that occurred in one, two, or three-or-more of the 15 AHT replicates (Figure 2E). In every ChIP-seq experiment, the vast majority of CEBPA-bound regions were found in at least two other ChIP experiments; conversely, 5% to 10% were not found in any other ChIP experiment and were thus private to a specific experiment. Collected across all 15 ChIP experiments, privately bound regions numbered in the tens of thousands (Figure 2A).
Applying motif analysis to confirm CEBPA-bound regions
All CEBPA peaks, regardless of whether found in group I or group II, had characteristics that reflect direct CEBPA protein binding to a consensus DNA motif. First, the CEBPA motif was present in almost all bound regions. De novo motif searches using the MEME-ChIP analysis suite and NestedMica [31, 32] identified a CEBPA consensus binding motif in virtually every CEBPA-bound region (Figure 3B). The CEBPA motif was present in 85% of identified peaks with a summit window size of 200 bp, 65% for window size of 100 bp, and 49% for window size of 50 bp. By contrast, similar analyses of random genomic DNA found the CEBPA motif in 35%, 20%, and 12% for summit window sizes of 200 bp, 100 bp, and 50 bp respectively (Figure 3C). Second, in regions identified as bound by CEBPA, the motif was consistently found at the center of the ChIP-enriched region (Figure 3D).
AHT-ChIP-seq can be used to interrogate protein-DNA binding in cell lines
We conclude that for typical human cancer cell lines, 10 million cells is an optimum cell number for use in an AHT-ChIP-seq experiment, and 1 million should be regarded as a minimum number for reproducible ChIP signal (Figure 4A and B).
AHT-ChIP-seq delivers high quality data on a large scale
To date, most genome-wide characterizations of protein-DNA contacts (such as transcription factor binding and histone marks) have been performed manually in small batches. Recently, liquid handling robotics have been used to partially automate functional genomics protocols based on ChIP experiments [8, 9].
Here, we have taken a related approach to create a fully-automated protocol to perform ChIP experiments in a standardized 96-well format after isolation of nuclei and sonication. As the robotics basis for our high-throughput ChIP platform, we selected the Agilent Bravo NGS workstation due to its flexibility and intuitive software. We validated our protocols by comparing multiple (>15) technical and biological ChIP replicates in detail, as well as with published data sets for a representative tissue-specific transcription factor, CEBPA. Our robotics-produced data were of high-quality and in strong concordance with previous manual experiments. Based on list price reagents and excluding cost of final sequencing, it would cost approximately £750 to perform almost 100 ChIP experiments. As the price of next-generation sequencing is dropping and the capabilities for multiplexing several libraries in one lane is increasing, this protocol will be accessible to increasing numbers of laboratories.
Our automation has dramatically increased the typical efficiency of performing ChIP experiments. Manual protocols demand over four hours of hands-on time to perform eight ChIP experiments; by contrast, our robotics protocols require less than two hours of hands-on time to perform 96 ChIP experiments.
The standard protocol we detail permits the simultaneous performance of hundreds of ChIP experiments in parallel with high technical quality. We have demonstrated how rapidly multiple biological replicates of transcription factor binding, chromatin state, and cohesin and co-activator occupancy can be characterized in a primary mammalian tissue. From cell number titration curves for AHT-ChIP-seq, we recommend one million cells as a minimum number for a ChIP experiment, and 10 million as an optimum number.
Future applications and possible technical developments
Reducing cell numbers
Access to adequate patient tissues has been a serious limitation in using functional genomics in the clinic. In part by optimizing the robot’s liquid handling actions throughout the protocol to minimize sample losses, we have modestly reduced the cell numbers needed for a ChIP experiment when performed not only against histone marks, but also CEBPA, a typical tissue-specific transcription factor. Although reducing cell numbers required for ChIP experiments was not our primary focus, the protocol we make available here offers an ideal starting point to optimize clinical genomics experiments to characterize the small amount of tissue found in typical biopsies.
Further increasing throughput
The protocols we have developed and report here are functional on any Bravo robot. Agilent offers a 384-well-adapted Bravo robot, which would immediately increase productivity using AHT-ChIP-seq by a factor of four.
Reducing hands-on time
The simple addition of a refrigerated reagent carousel would make our protocol entirely hands-free.
Materials and Methods
Tissue and cell preparation
ChIP-seq and ChIP-quantitative PCR experiments were performed on liver material isolated from six adult (three-month-old) C57/BL6 male mice obtained from Cancer Research UK Institute and the cell lines HepG2 and MCF7. All tissues were treated post mortem and cells fixed in culture dishes with 1% formaldehyde. The investigation was approved by the CRUK Cambridge Institute ethics committee and followed the CRUK Cambridge Institute guidelines for the use of animals in experimental studies under the UK Home Office license.
ChIP experiments were adapted from those described previously with modifications to allow for scaling to a 200 μl volume for immunoprecipitation, as opposed to laboratory standard 3 ml protocol. Cell lysis and sonication was carried out as previously described , with minor modifications as follows: cell lysates were left undiluted and the Triton X-100 volume adjusted appropriately. Protocols carried out on the Agilent Bravo NGS were programmed as a five-step process and the Agilent Vworks Automated ChIP protocol files are available in Additional file 3. All incubation and wash steps were carried out in a Nunc 1.2 ml deep well plate 260251 (Fisher Scientific UK Ltd, Loughborough, UK).
Step 1: Attachment of antibody to Protein G beads. A 25 μl aliquot of Invitrogen Protein G Dynabeads (Life Technologies Ltd, Paisley, UK) was washed with 0.5% BSA/PBS solution followed by addition of 2.5 μg (12.5 μl at 0.2 μg/μl) of antibody. The plate was sealed and transferred to 4°C and mixed for a minimum of four hours on an orbital shaker Grant-bio, PMS1000 (Grant Instruments Ltd, Cambridge, UK). In this experiment, antibodies used were CEBPA sc-9314 (Santa Cruz Biotechnology, Inc. Heidelberg, Germany), HNF4A ARP31946 (Aviva Systems Biology, Corp. San Diego, CA 92121). p300 sc-585 (Santa Cruz Biotechnology Inc.), RAD21 ab992 (Abcam, Cambridge, UK) and H3K4me3 05-1339 (Millipore Ltd, Watford, UK).
Step 2: Wash and addition of lysate. We again washed antibody-bound Protein G beads in 0.5% BSA/PBS solution and added 180 μl of the sonicated lysate to the prepared beads. The plate was returned to a 4°C cold room for overnight mixing and hybridization.
Step 3: Wash of DNA bound beads. ChIP-DNA-bound beads were washed for 10 repetitions in 180 μl cold RIPA solution, transferred to a rigid PCR plate in 50 μl elution buffer and placed in a 65°C thermal cycler, for a minimum of five hours to reverse protein-DNA cross-links.
Step 4: Removal of beads, RNase and proteinase K treatment. We added 50 μl of Tris-EDTA buffer to the beads to dilute SDS in elution buffer. Next, 2 μl RNase AM2269 (Life Technologies) was added to eluted ChIP-DNA and incubated on Bravo deck at 37°C for 30 minutes, followed by 2 μl of proteinase K treatment AM2548 (Life Technologies) at 55°C for one to two hours.
Step 5: Purification of DNA. Phenol and ethanol precipitation was replaced with an Ampure Bead A63881 (Beckman Coulter Ltd, High Wycombe, UK) cleanup step. We added 180 μl of beads (1.8 times volume) to the DNA, followed by two 70% ethanol washes. After the DNA was eluted in 50 μl water or similar elution buffer, it was ready for the Illumina library preparation step.
Library and sequencing preparation
Illumina sequencing libraries were prepared from ChIP-enriched DNA in 96-well microtiter plates using automated liquid handling robotic platforms. Pre-PCR library preparation steps were carried out using a Beckman Fxp dual arm instrument with a Cytomat Microplate Hotel (Beckman Coulter Ltd) (for method see Figure S8 in Additional file 1 and Additional file 6). Briefly, 50 μl of DNA was purified by binding to twice the volume of AMPure XP beads (Beckman Coulter Ltd) and eluted in 30 μl of 10 mM Tris-HCl, pH 8.5. End-repair, A-tailing and paired-end adapter ligation were performed using NEBnext reagents E6000S (New England Biolabs, Hitchin, UK), with purification using a 1:1 ratio of AMPure XP to sample between each reaction. Illumina paired-end adapters were used at a final concentration of 20 pM (a 1:20 dilution of our standard library adapter concentration) to reduce adapter dimer formation. After ligation, excess adapters and adapter dimers were removed using two Ampure XP cleanups, first with a 0.7:1 ratio of standard Ampure XP to sample, followed by a 1:1 ratio, with elution in 30 μl of 10 mM Tris-HCl, pH 8.5. We then used 10 μl of this adapter ligated material as a template for PCR amplification with Kapa HiFi 2x Mastermix KK2602 (Kapa Biosystems, Inc. Woburn, MA 01801, US) with 200 nM final concentration of standard PE1.0 and modified multiplexing PE2.0 primers (see Table S2 in Additional file 7). After PCR setup on the Beckman Fxp, PCR reactions were cycled on an MJ Tetrad thermal cycler with the following conditions: 94°C for 2 minutes; 18 cycles of 94°C for 20 seconds, 65°C for 30 seconds, 72°C for 30 seconds; and 72°C for 3 minutes.
After PCR, excess primers and any primer dimers were removed by performing a 0.7:1 Ampure XP cleanup on a Caliper Zephyr liquid handler (Perkin Elmer, Waltham, MA 02451, USA) with elution in 30 μl of 10 mM Tris-HCl, pH 8.5. Libraries were pooled in equal volume and the concentration of that pool determined by real-time PCR using the SYBR Fast Illumina Library Quantification Kit (Kapa Biosystems, Inc.) before sequencing on an Illumina MiSeq, 50 cycles single end plus index read, to determine the relative representation of each barcoded library. Based on this data the library pool was reblended so as to give equal representation of each library, and requantified by real-time PCR as above, before sequencing on an Illumina HiSeq 2000 for 75 cycles paired end, plus index read.
Evaluation of ChIP enrichment by quantitative real-time PCR was performed using an ABI7900-HT system as per manufacturer’s instructions (Life Technologies). Reactions were performed using Power SYBR Mastermix (Life Technologies). Samples were normalized to a standard curve of sonicated input DNA over negative control regions. Primers used can be found in Table S3 in Additional file 8.
Read mapping and sequencing data analysis
Reads were aligned to reference genome mouse build (NCBI mm9) or human NCBI36.3 genome build using BWA version 0.6.1 using default parameters. Ambiguous reads that mapped to more than one region in the genome and those with a mapping score of zero were removed. Regions of ChIP enrichment (peaks) were identified using MACS  versions 126.96.36.199 and 1.4.2 against a matched input DNA control of similar read depth.
Principal component analysis, peak overlap rates, determining peak occupancy and peak clustering were all carried out using functions of the R/Bioconductor package DiffBind version 1.4.2 .
Heat maps were created using the SeqMINER package .
De novo motif analysis was carried out using the MEME-ChIP analysis suite and NestedMica. To assess significance of enrichment for CEBPA motifs, NestedMica [31, 32] was used to detect motifs in whole peaks. Each replicate was compared to a null distribution generated from 1,000 sets of random intervals with the same width distribution. In each case the observed enrichment was highly significant (Wilcoxon test P <0.001). The overall significance of enrichment across all replicates was calculated using a Wilcoxon test comparing the observed motif content to that expected based on the modes of the null distributions. CentriMo  was used to calculate the significance of CEBPA motif within a region 200 bp to either side of the peak summit. Peak summits were obtained from the output provided by MACS. Random intervals were obtained using RSAT analysis suite .
Proportional Venn diagrams were created using BioVenn .
Data sets are available from ArrayExpress under the accession number: E-MTAB-1579, previously published data sets taken from E-MTAB-941 and E-MTAB-1414.
automated high-throughput chromatin immunoprecipitation
bovine serum albumin
polymerase chai reaction.
The authors thank Andrew Knights and Klara Stefflova for reagents; Rory Stark and Gordon Brown for R/Bioconductor helpful advice; Diego Villar for helpful comments on the manuscript; and Peter Ellis for advice on automated systems. This work was supported by Wellcome Trust grant number 098051, ERC Starting Grant 202218 and EMBO YIP.
- Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K: High-resolution profiling of histone methylations in the human genome. Cell. 2007, 129: 823-837. 10.1016/j.cell.2007.05.009.PubMedView ArticleGoogle Scholar
- Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh TY, Peng W, Zhang MQ, Zhao K: Combinatorial patterns of histone acetylations and methylations in the human genome. Nat Genet. 2008, 40: 897-903. 10.1038/ng.154.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhu J, Adli M, Zou JY, Verstappen G, Coyne M, Zhang X, Durham T, Miri M, Deshpande V, De Jager PL, Bennett DA, Houmard JA, Muoio DM, Onder TT, Camahort R, Cowan CA, Meissner A, Epstein CB, Shoresh N, Bernstein BE: Genome-wide chromatin state transitions associated with developmental and environmental cues. Cell. 2013, 152: 642-654. 10.1016/j.cell.2012.12.033.PubMedPubMed CentralView ArticleGoogle Scholar
- Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M, ENCODE Project Consortium: An integrated encyclopedia of DNA elements in the human genome. Nature. 2012, 489: 57-74. 10.1038/nature11247.View ArticleGoogle Scholar
- Yan J, Enge M, Whitington T, Dave K, Liu J, Sur I, Schmierer B, Jolma A, Kivioja T, Taipale M, Taipale J: Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell. 2013, 154: 801-813. 10.1016/j.cell.2013.07.034.PubMedView ArticleGoogle Scholar
- Adams D, Altucci L, Antonarakis SE, Ballesteros J, Beck S, Bird A, Bock C, Boehm B, Campo E, Caricasole A, Dahl F, Dermitzakis ET, Enver T, Esteller M, Estivill X, Ferguson-Smith A, Fitzgibbon J, Flicek P, Giehl C, Graf T, Grosveld F, Guigo R, Gut I, Helin K, Jarvius J, Küppers R, Lehrach H, Lengauer T, Lernmark Å, Leslie D, et al: BLUEPRINT to decode the epigenetic signature written in blood. Nat Biotechnol. 2012, 30: 224-226. 10.1038/nbt.2153.PubMedView ArticleGoogle Scholar
- Zinzen RP, Girardot C, Gagneur J, Braun M, Furlong EE: Combinatorial binding predicts spatio-temporal cis-regulatory activity. Nature. 2009, 462: 65-70. 10.1038/nature08531.PubMedView ArticleGoogle Scholar
- Garber M, Yosef N, Goren A, Raychowdhury R, Thielke A, Guttman M, Robinson J, Minie B, Chevrier N, Itzhaki Z, Blecher-Gonen R, Bornstein C, Amann-Zalcenstein D, Weiner A, Friedrich D, Meldrim J, Ram O, Cheng C, Gnirke A, Fisher S, Friedman N, Wong B, Bernstein BE, Nusbaum C, Hacohen N, Regev A, Amit I: A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol Cell. 2012, 47: 810-822. 10.1016/j.molcel.2012.07.030.PubMedView ArticleGoogle Scholar
- Blecher-Gonen R, Barnett-Itzhaki Z, Jaitin D, Amann-Zalcenstein D, Lara-Astiaso D, Amit I: High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein-DNA interactions and epigenomic states. Nat Protoc. 2013, 8: 539-554. 10.1038/nprot.2013.023.PubMedView ArticleGoogle Scholar
- Kasowski M, Grubert F, Heffelfinger C, Hariharan M, Asabere A, Waszak SM, Habegger L, Rozowsky J, Shi M, Urban AE, Hong MY, Karczewski KJ, Huber W, Weissman SM, Gerstein MB, Korbel JO, Snyder M: Variation in transcription factor binding among humans. Science. 2010, 328: 232-235. 10.1126/science.1183621.PubMedPubMed CentralView ArticleGoogle Scholar
- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin SF, Palmieri C, Caldas C, Carroll JS: Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012, 481: 389-393.PubMedPubMed CentralGoogle Scholar
- Xiao S, Xie D, Cao X, Yu P, Xing X, Chen CC, Musselman M, Xie M, West FD, Lewin HA, Wang T, Zhong S: Comparative epigenomic annotation of regulatory DNA. Cell. 2012, 149: 1381-1392. 10.1016/j.cell.2012.04.029.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmidt D, Schwalie PC, Wilson MD, Ballester B, Goncalves A, Kutter C, Brown GD, Marshall A, Flicek P, Odom DT: Waves of retrotransposon expansion remodel genome organization and CTCF binding in multiple mammalian lineages. Cell. 2012, 148: 335-348. 10.1016/j.cell.2011.11.058.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmidt D, Wilson MD, Ballester B, Schwalie PC, Brown GD, Marshall A, Kutter C, Watt S, Martinez-Jimenez CP, Mackay S, Talianidis I, Flicek P, Odom DT: Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science. 2010, 328: 1036-1040. 10.1126/science.1186176.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmidt D, Wilson MD, Spyrou C, Brown GD, Hadfield J, Odom DT: ChIP-seq: using high-throughput sequencing to discover protein-DNA interactions. Methods. 2009, 48: 240-248. 10.1016/j.ymeth.2009.03.001.PubMedPubMed CentralView ArticleGoogle Scholar
- Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.PubMedPubMed CentralView ArticleGoogle Scholar
- Kutter C, Watt S, Stefflova K, Wilson MD, Goncalves A, Ponting CP, Odom DT, Marques AC: Rapid turnover of long noncoding RNAs and the evolution of gene expression. PLoS Genet. 2012, 8: e1002841-10.1371/journal.pgen.1002841.PubMedPubMed CentralView ArticleGoogle Scholar
- Faure AJ, Schmidt D, Watt S, Schwalie PC, Wilson MD, Xu H, Ramsay RG, Odom DT, Flicek P: Cohesin regulates tissue-specific expression by stabilizing highly occupied cis-regulatory modules. Genome Res. 2012, 22: 2163-2175. 10.1101/gr.136507.111.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS: Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008, 9: R137-10.1186/gb-2008-9-9-r137.PubMedPubMed CentralView ArticleGoogle Scholar
- Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iyer VR, Jung YL, Karmakar S, Kellis M, Kharchenko PV, Li Q, Liu T, Liu XS, Ma L, Milosavljevic A, Myers RM, et al: ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012, 22: 1813-1831. 10.1101/gr.136184.111.PubMedPubMed CentralView ArticleGoogle Scholar
- Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842. 10.1093/bioinformatics/btq033.PubMedPubMed CentralView ArticleGoogle Scholar
- Cereghini S: Liver-enriched transcription factors and hepatocyte differentiation. FASEB J. 1996, 10: 267-282.PubMedGoogle Scholar
- Anderson DE, Losada A, Erickson HP, Hirano T: Condensin and cohesin display different arm conformations with characteristic hinge angles. J Cell Biol. 2002, 156: 419-424. 10.1083/jcb.200111002.PubMedPubMed CentralView ArticleGoogle Scholar
- Haering CH, Lowe J, Hochwagen A, Nasmyth K: Molecular architecture of SMC proteins and the yeast cohesin complex. Mol Cell. 2002, 9: 773-788. 10.1016/S1097-2765(02)00515-4.PubMedView ArticleGoogle Scholar
- Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO, Van Calcar S, Qu C, Ching KA, Wang W, Weng Z, Green RD, Crawford GE, Ren B: Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet. 2007, 39: 311-318. 10.1038/ng1966.PubMedView ArticleGoogle Scholar
- Visel A, Akiyama JA, Shoukry M, Afzal V, Rubin EM, Pennacchio LA: Functional autonomy of distant-acting human enhancers. Genomics. 2009, 93: 509-513. 10.1016/j.ygeno.2009.02.002.PubMedPubMed CentralView ArticleGoogle Scholar
- Santos-Rosa H, Schneider R, Bannister AJ, Sherriff J, Bernstein BE, Emre NC, Schreiber SL, Mellor J, Kouzarides T: Active genes are tri-methylated at K4 of histone H3. Nature. 2002, 419: 407-411. 10.1038/nature01080.PubMedView ArticleGoogle Scholar
- Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ, Nekrutenko A: Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005, 15: 1451-1455. 10.1101/gr.4086505.PubMedPubMed CentralView ArticleGoogle Scholar
- DiffBind: differential binding analysis of ChIP-seq peak data. [http://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf]
- Ye T, Krebs AR, Choukrallah MA, Keime C, Plewniak F, Davidson I, Tora L: seqMINER: an integrated ChIP-seq data interpretation platform. Nucleic Acids Res. 2011, 39: e35-10.1093/nar/gkq1287.PubMedPubMed CentralView ArticleGoogle Scholar
- Down TA, Hubbard TJ: NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence. Nucleic Acids Res. 2005, 33: 1445-1453. 10.1093/nar/gki282.PubMedPubMed CentralView ArticleGoogle Scholar
- Dogruel M, Down TA, Hubbard TJ: NestedMICA as an ab initio protein motif discovery tool. BMC Bioinformatics. 2008, 9: 19-10.1186/1471-2105-9-19.PubMedPubMed CentralView ArticleGoogle Scholar
- Bailey TL, Machanick P: Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res. 2012, 40: e128-10.1093/nar/gks433.PubMedPubMed CentralView ArticleGoogle Scholar
- Thomas-Chollier M, Defrance M, Medina-Rivera A, Sand O, Herrmann C, Thieffry D, van Helden J: RSAT 2011: regulatory sequence analysis tools. Nucleic Acids Res. 2011, 39: W86-W91. 10.1093/nar/gkr377.PubMedPubMed CentralView ArticleGoogle Scholar
- Hulsen T, de Vlieg J, Alkema W: BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics. 2008, 9: 488-10.1186/1471-2164-9-488.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.