A suitable control knockdown e. The methodology used for binding-region signal normalization for instance, normalization against total read counts or using values from reference peaks quantified by qPCR under all experimental conditions should also be reported.
All immunoreactive bands identified by immunoblot analysis are analyzed Fig. ENCODE passes such analyses if the protein of interest is identified in such bands; if additional chromosomal proteins are identified in an immunoreactive band, the Consortium accepts the experiment as long as they are present at lower prevalence than the desired protein as measured by peptide counts or other methods or can be demonstrated to arise from nonspecific immunoprecipitation e.
All proteins identified by mass spectrometry and the number of peptide counts for each are reported. Different antibodies against different parts of the same protein or other members of a known protein complex can be used in analyzing the specificity of antibodies.
Note that for different proteins that are members of a complex, there may be some functions that are independent of one another. Thus, the targets lists for two different proteins may not entirely overlap. In this case, specific evidence about limited overlap of binding specificity in the literature is presented to justify the significance of the overlap observed between data sets for the factors in question.
An epitope-tagged version of the target protein may be used, preferably expressed from the endogenous gene promoter. For transcription factors, if a factor has a well-characterized motif derived from in vitro binding studies or another justifiable method, and if either no paralogs are expressed in the cell lines being analyzed or if the antibody is raised to a unique region of the factor, motif enrichment can be used for validation.
Motif analysis can be performed using a defined set of high-quality peaks a 0. Analysis of data sets deposited as of January identified data sets that meet these standards for 49 of 85 factors Fig. We note that due to differences in transcription-factor recruitment mechanisms, failure of a data set to meet the motif enrichment threshold does not necessarily indicate poor quality data.
For antibodies directed against members of a multigene family, the best practice is to prepare or obtain antibodies that recognize protein regions unique to individual family members. For an ENCODE validated antibody, any potential cross-reaction is noted when reporting data collected using that antibody.
For antibodies that have been previously characterized for one cell type, ENCODE has used only one validation method such as immunoblot analysis when the antibody is used to perform ChIP in a new cell type or organism.
If an antibody has been validated in at least three different cell types, we do not require further validation for ChIP-seq experiments with additional cell types for ENCODE submission. Similarly, for whole organisms, if the antibody has been characterized in three growth stages, no further characterization is required.
If antibodies derived from the same lot are used by different groups in ENCODE, they only need to be characterized once. However, antibodies from different lots of the same catalog number are characterized as if they were new antibodies.
Epitope-tagged factors are introduced into cells by transfection of an expression construct. To help ensure that ChIP-seq results obtained using the tagged factor are comparable to those expected for the endogenous factor, ENCODE uses the criteria that tagged factors are expressed at a comparable amount to the endogenous factor.
This is usually achieved by cloning into a low-copy number vector and using the natural promoter to drive expression. If the tagged protein is expressed from a heterologous promoter, data comparing expression levels of the tagged and endogenous proteins i. There are special cases in which ChIP cannot be obtained at endogenous protein levels, and here, elevated expression can provide useful information.
For ENCODE data to be submitted, all commercial histone antibodies are validated by at least two independent methods, as described below, and new lots of antibody are analyzed independently.
These validations are performed by the ENCODE laboratory performing the ChIP-seq or by the antibody supplier, but only if the supplier provides data for the specific lot of antibody. The tests need only be performed once for each antibody lot.
All antibodies used in ENCODE ChIP experiments are checked for reactivity with nonhistone proteins and with unmodified histones by performing immunoblot analysis on total nuclear extract and recombinant histones. To enable visual quantification of reactivity, a concentration series of both extract and recombinant histones are analyzed using recombinant histone levels that are comparable to those of the target histone in nuclear extract.
Since cross-reactivity may vary between species, this test is performed using nuclear extracts from each species to be studied by ChIP. In addition to the primary test, antibody specificity is verified by at least one additional test. The pros and cons of each test are described. The first two are the most commonly used. Peptide binding and peptide competition assays provide a fast method to initially evaluate the specificity and relative binding strength of antibodies to histone tails with different modifications e.
A potential drawback is that antibodies may differ in their binding specificity toward histone tail peptides in vitro versus toward full-length histones in the context of chromatin in IP experiments.
Nevertheless, observing at least a fold enriched binding signal for the modification of interest relative to other modifications provides confidence in the antibody specificity. For these assays, histone tail peptides with particular modifications can be purchased commercially. For antibodies generated against related and historically problematic modifications, the ability of the antibody to effectively distinguish between similar histone marks e.
This test may often not be successful because IP for one modification can simultaneously isolate coassociated histones with other modifications. Thus, only a positive result i. Strains or cell lines harboring knockouts or catalytically inactive mutants of enzymes responsible for particular histone modifications offer the opportunity to test antibody specificity.
Such mutants exist for S. In cases where more than one enzyme modifies the same residue e. However, positive controls showing that the antibody works on wild-type samples processed in parallel, and positive controls showing that the mutant extract is amenable to the assay employed are included for data to be submitted. Mutant histones e. When analyzing a strain containing a mutated histone that cannot be modified, we expect at least a fold reduction in immunoblot or IP signal relative to wild-type histone preparations.
Mutant histone tests cannot distinguish whether antibodies discriminate between mono, di, and trimethylation. Enrichment at annotated features e. If a well-characterized modification e.
H3K4me3 is analyzed, the observed localization to annotations are expected to be similar to that of known overlap standards derived from the literature or existing ChIP-seq data sets for point source peaks, overlap with known annotations can be assessed using the IDR guidelines in Box 3.
Even if antibodies pass the specificity tests described above, observing similar ChIP results with two independent antibodies provides added confidence. We therefore aspire to obtain ChIP-seq data from two independent antibodies whenever possible, providing statistical comparisons of the results and presenting the intersection of the peak sets obtained with the two antibodies.
The reasons for a significant discordance can be either biological or technical, and merit further dissection. Motif enrichment is the easiest assay to perform, but requires pre-existing information about the sequences to which a protein binds and assumes that the motif is uniquely recognized in a given cell source by the factor of interest.
ChIP with a second antibody or against an epitope-tagged construct and siRNA experiments coupled with ChIP provide independent evidence that the target sites are bound by the factor of interest.
We found that mass spectrometry is particularly useful for cases where multiple or unexpected bands are observed on an immunoblot and the presence of spliced isoforms, post-translational modification, or degradation is suspected. Additionally, it can precisely identify potential alternate sources of ChIP signal, often with novel biological implications, which can be tested by additional ChIP experiments. Due to the significant effort and expense required to perform these assays, our standard for the consortia requires only one secondary assay.
A summary of motif detection for all data sets is in preparation P Kheradpour and M Kellis, in prep. Validating histone modification antibodies involves multiple issues Egelhofer et al. H3K27me , 3 specificity with respect to mono-, di-, and trimethylation at the same residue e. For all consortia histone measurements, we set the standard that immunoblot analysis and one of the following secondary criteria are applied: Peptide-binding tests dot blots , mass spectrometry, immunoreactivity analysis in cell lines containing knockdowns of a relevant histone modification enzyme or mutants histones, or genome annotation enrichment.
The details of these standards are in Box 1. Given the challenges in obtaining antibodies for suitable ChIP, an attractive alternative is to tag the factor with an exogenous epitope and immunoprecipitate with a well-characterized monoclonal reagent specific for the tag.
Epitope-tagging addresses the problems of antibody variation and cross-reaction with different members of multigene families by using a highly specific reagent that can be used for many different factors.
However, this introduces concerns about expression levels and whether tagging alters the activity of the factor. The level of expression is typically addressed by using large clones usually fosmids and BACs carrying as much regulatory information as possible to make the level of expression nearly physiological Poser et al.
Higher expression is known to result in occupancy of sites not necessarily occupied at physiological levels DeKoter and Singh ; Fernandez et al.
In some cases, information regarding expression is not available and expression from an exogenous promoter has been used P Farnham, unpubl. Biological replicate experiments from independent cell cultures, embryo pools, or tissue samples are used to assess reproducibility.
The irreproducible discovery rate IDR analysis methodology Li et al. For experiments with poor values for quality metrics described in Section III, additional replicate s have been generated. For a typical point-source DNA-binding factor, the number of ChIP-seq positive sites identified typically increases with the number of sequenced reads Myers et al.
This result is expected, as studies of numerous factors by ENCODE and by other groups have repeatedly found a continuum of ChIP signal strength, rather than a sharply bounded and discrete set of positive sites Rozowsky et al. Weaker sites can be detected with greater confidence in larger data sets because of the increased statistical power afforded by more reads. Examination of peak signals reveals that the signal enrichments consistently plateau at greater sequencing depths.
Interestingly, many additional peaks, with enrichment values of three- to sevenfold, can still be found by sequencing to much greater depths.
Peak counts depend on sequencing depth. A Number of peaks called with Peak-seq 0. B Called peak numbers for 11 ChIP-seq data sets as a function of the number of uniquely mapped reads used for peak calling. Data sets are indicated by cell line and transcription factor e. C Fold-enrichment for newly called peaks as a function of sequencing depth. For each incremental addition of 2. ENCODE generates and sequences a control ChIP library for each cell type, tissue, or embryo collection and sequences the library to the appropriate depth i.
If cost constraints allow, a control library should be prepared from every chromatin preparation and sonication batch, although some circumstances can justify fewer control libraries. Importantly, a new control is always performed if the culture conditions, treatments, chromatin shearing protocol, or instrumentation is significantly modified.
Experiments are performed at least twice to ensure reproducibility. The relationship of ChIP signal strength to biological regulatory activity is a current area of active investigation. The biological activity of known enhancers, defined in the literature independently of ChIP data, is distributed quite broadly relative to ChIP-seq signal strength Ozdemir et al. Some highly active transcriptional enhancers reproducibly display modest ChIP signals Fig. This means that one cannot a priori set a specific target threshold for ChIP peak number or ChIP signal strength that will assure inclusion of all functional sites see Discussion.
Therefore, a practical goal is to maximize site discovery by optimizing immunoprecipitation and sequencing deeply, within reasonable expense constraints. For point-source factors in mammalian cells, a minimum of 10 million uniquely mapped reads are used by ENCODE for each biological replicate providing a minimum of 20 million uniquely mapped reads per factor ; for worms and flies a minimum of 2 million uniquely mapped reads per replicate is used.
For broad areas of enrichment, the appropriate number of uniquely mapped reads is currently under investigation, but at least 20 million uniquely mapped reads per replicate for mammalian cells and 5 million uniquely mapped reads per replicate for worms and flies is currently being produced for most experiments.
Criteria for assessing the quality of a ChIP-seq experiment. A Library complexity. Individual reads mapping to the plus red or minus strand blue are represented. B Distribution of functional regulatory elements with respect to the strength of the ChIP-seq signal. ChIP-seq was performed against myogenin, a major regulator of muscle differentiation, in differentiated mouse myocytes.
While many extensively characterized muscle regulatory elements exhibit strong myogenin binding, a large number of known functional sites are at the low end of the binding strength continuum.
C Number of called peaks vs. ChIP enrichment. Except in special cases, successful experiments identify thousands to tens of thousands of peaks for most TFs and, depending on the peak finder used, numbers in the hundreds or low thousands indicate a failure.
Peaks were called using MACS with default thresholds. D Generation of a cross-correlation plot. Reads are shifted in the direction of the strand they map to by an increasing number of base pairs and the Pearson correlation between the per-position read count vectors for each strand is calculated. Read coverage as wigglegram is represented, not to the same scale in the top and bottom panels. F Correlation between the fraction of reads within called regions and the relative cross-correlation coefficient for human ChIP-seq experiments.
G The absolute and relative height of the two peaks are useful determinants of the success of a ChIP-seq experiment. Site discovery and reproducibility are also affected by the complexity of a ChIP-seq sequencing library Fig. We define library complexity operationally as the fraction of DNA fragments that are nonredundant. With increased depth of sequencing of a library, a point is eventually reached where the complexity will be exhausted and the same PCR-amplified DNA fragments will be sequenced repeatedly.
Low library complexity can occur when very low amounts of DNA are isolated during the IP or due to problems with library construction. A useful complexity metric is the fraction of nonredundant mapped reads in a data set nonredundant fraction or NRF , which we define as the ratio between the number of positions in the genome that uniquely mappable reads map to and the total number of uniquely mappable reads; it is similar to a recently published redundancy metric Heinz et al.
We expect that, as sequencing technology improves and read numbers in the hundreds of millions per lane become feasible, even complex libraries from point-source factor libraries may be sequenced at depths greater than necessary.
To maximize information that can be obtained for each DNA-sequencing run and to prevent oversequencing, barcoding and pooling strategies can be used Lefebvre et al. An appropriate control data set is critical for analysis of any ChIP-seq experiment because DNA breakage during sonication is not uniform. In particular, some regions of open chromatin are preferentially represented in the sonicated sample Auerbach et al. There are also platform-specific sequencing efficiency biases that contribute to nonuniformity Dohm et al.
To serve as a valid control, we use identical protocols to build ChIP and control sequencing libraries i. Although rare in our experience, control libraries with particularly strong sonication biases have been observed and they can adversely affect peak calling Supplemental Fig.
After mapping reads to the genome, peak calling software is used to identify regions of ChIP enrichment. The resulting output of these algorithms generally ranks called regions by absolute signal read number or by computed significance of enrichment e. Because ChIP signal strength is a continuum with many more weak sites than strong ones Fig.
Thresholds that are too relaxed lead to a high proportion of false positives for each replicate, but as discussed below, subsequent analysis can strip false positives from a final joint peak determination. Different peak-calling algorithms rely on different statistical models to calculate P -values and false discovery rates FDR , meaning that significance values from different software packages are not directly comparable.
When using standard peak-calling thresholds, successful experiments generally identify thousands to tens of thousands of peaks for most TFs in mammalian genomes, although some exceptions are known Frietze et al. In all cases, it is important to use an appropriate control experiment in peak calling.
Calling discrete regions of enrichment for Broad-source factors or Mixed-source factors is more challenging and is at an earlier stage of development. Methods to identify such regions are emerging e. Standards for the identification of broad enrichment regions are currently in development. The quality of individual ChIP-seq experiments varies considerably and can be especially difficult to evaluate when new antibodies are being tested or when little is known about the factor and its binding motif.
When applied and interpreted as a group, these metrics and approaches provide a valuable overall assessment of experimental success and data quality. A first impression about ChIP-seq quality can be obtained by local inspection of mapped sequence reads using a genome browser.
Although not quantitative, this approach is very useful, especially when a known binding location can be examined; read distribution shape and signal strength relative to a control sample can provide a sense of ChIP quality.
A true signal is expected to show a clear asymmetrical distribution of reads mapping to the forward and reverse strands around the midpoint peak of accumulated reads. This signal should be large compared with the signal of the same region from the control library. Of course it is not feasible to inspect the whole genome in this manner, and evaluating a limited number of the strongest sites may overestimate the quality of the entire data set Supplemental Fig.
The genome-wide metrics discussed below provide more objective and global assessments. For point-source data sets, we calculate the fraction of all mapped reads that fall into peak regions identified by a peak-calling algorithm Ji et al.
Typically, a minority of reads in ChIP-seq experiments occur in significantly enriched genomic regions i. The fraction of reads falling within peak regions is therefore a useful and simple first-cut metric for the success of the immunoprecipitation, and is called FRiP fraction of reads in peaks. However, passing this threshold does not automatically mean that an experiment is successful and a FRiP below the threshold does not automatically mean failure.
Thus, FRiP is very useful for comparing results obtained with the same antibody across cell lines or with different antibodies against the same factor. FRiP is sensitive to the specifics of peak calling, including the way the algorithm delineates regions of enrichment and the parameters and thresholds used.
Thus, all FRiP values that are compared should be derived from peaks uniformly called by a single algorithm and parameter set. Quality control of ChIP-seq data sets in practice. However, the cross-correlation plot profiles A indicated that both IPs were suboptimal, with one being unacceptable. E Representative browser snapshot of the four EGR1 ChIP-seq experiments, showing the much stronger peaks obtained with the second set of replicates.
Regions are ranked by their confidence scores as called by SPP. A very useful ChIP-seq quality metric that is independent of peak calling is strand cross-correlation. It is based on the fact that a high-quality ChIP-seq experiment produces significant clustering of enriched DNA sequence tags at locations bound by the protein of interest, and that the sequence tag density accumulates on forward and reverse strands centered around the binding site. A control experiment, such as sequenced input DNA, lacks this pattern of shifted stranded tag densities Supplemental Fig.
It is computed as the Pearson linear correlation between the Crick strand and the Watson strand, after shifting Watson by k base pairs Fig.
The normalized ratio between the fragment-length cross-correlation peak and the background cross-correlation normalized strand coefficient, NSC and the ratio between the fragment-length peak and the read-length peak relative strand correlation, RSC Fig. High-quality ChIP-seq data sets tend to have a larger fragment-length peak compared with the read-length peak, whereas failed ones and inputs have little or no such peak Figs. In general, we observe a continuum between the two extremes, and broad-source data sets are expected to have flatter cross-correlation profiles than point-sources, even when they are of very high quality.
As with the other quality metrics, even high-quality data sets generated for factors with few genuine binding sites tend to produce relatively low NSCs. Based on the cross-correlation profiles, FRiP score, and number of called regions, these replicates were flagged as marginal in quality.
The experiments were repeated, with all quality control metrics improving considerably. These have been constructed based on the historical experiences of ENCODE ChIP-seq data production groups with the purpose of balancing data quality with practical attainability and are routinely revised.
For experiments with NSC values below 1. The following guidelines have been established for mammalian cells optimal parameter may differ for other organisms. Biological replicates are performed for each ChIP-seq data set and subjected to peak calling. Data sets which fail to meet these criteria may still be deposited by ENCODE experimenters, provided that at least three experimental replicates have been attempted and a note accompanies these data sets explaining which parameters fail to meet the standards and providing any technical information that may explain this failure.
This guideline is for point source features; metrics are still being determined for broad peak analyses. A simpler heuristic for establishing reproducibility was previously used as a standard for depositing ENCODE data and was in effect when much of the currently available data was submitted. As another measure of experiment quality, we take advantage of the reproducibility information provided by the duplicates using the IDR irreproducible discovery rate statistic that has been developed for ChIP-seq Li et al.
Given a set of peak calls for a pair of replicate data sets, the peaks can be ranked based on a criterion of significance, such as the P -value, the q-value, the ChIP-to-input enrichment, or the read coverage for each peak Fig.
If two replicates measure the same underlying biology, the most significant peaks, which are likely to be genuine signals, are expected to have high consistency between replicates, whereas peaks with low significance, which are more likely to be noise, are expected to have low consistency.
If the consistency between a pair of rank lists that contains both significant and insignificant findings is plotted, a transition in consistency is expected Fig. This consistency transition provides an internal indicator of the change from signal to noise and suggests how many peaks have been reliably detected. A , D Scatter plots of signal scores of peaks that overlap in each pair of replicates. B , E Scatter plots of ranks of peaks that overlap in each pair of replicates.
Note that low ranks correspond to high signal and vice versa. The IDR statistic quantifies the above expectations of consistent and inconsistent groups by modeling all pairs of peaks present in both replicates as belonging to one of two groups: a reproducible group, and an irreproducible group Li et al.
In general, the signals in the reproducible group are more consistent i. The IDR provides a score for each peak, which reflects the posterior probability that the peak belongs to the irreproducible group.
A major advantage of IDR is that it can be used to establish a stable threshold for called peaks that is more consistent across laboratories, antibodies, and analysis protocols e. Increased consistency comes from the fact that IDR uses information from replicates, whereas the FDR is computed on each replicate independently. The application of IDR to real-life data is shown in Figure 6. It is important that the peak-calling threshold used prior to IDR analysis not be so stringent that the noise component is entirely unrepresented in the data, because the algorithm requires sampling of both signal and noise distributions to separate the peaks into two groups; thus relaxing the default stringency settings when running a given peak caller is advised if IDR analysis will follow.
To ensure similar weighting of individual replicates, the number of significant binding regions identified using IDR on each individual replicate obtained by partitioning reads into two equal groups to allow the IDR analysis is recommended to be within a factor of 2 for data sets to be submitted to UCSC by ENCODE Box 3.
To facilitate data sharing among laboratories, both within and outside the Consortium, and to ensure that results can be reproduced, ENCODE has established guidelines for data sharing in public repositories. Box 4 provides a detailed description of the data and experimental and analytical details to be shared so that others can reproduce both experiments and analyses.
Shared information includes the experimental procedures for performing the ChIP, antibody information and validation data, as well as relevant DNA sequencing, peak calling, and analysis details.
For ENCODE experiments that do not meet the guidelines described above, data and results may be reported, with a note indicating that the criteria have not been met and explaining why the data are nevertheless released.
Data should be submitted to public repositories. Investigator, organism, or cell line, experimental protocol or reference to a known protocol. Catalog and lot number for any antibody used. If not a commercial antibody, indicate the precise source of the antibody. Information used to characterize the antibody, including summary of results images of immunoblots, immunofluorescence, list of proteins identified by mass spec, etc.
Peak calling algorithm 29 and parameters used, including threshold and reference genome used to map peaks. A summary of the number of reads and number of targets for each replicate and for the merged data set. Criteria that were used to validate the quality of the resultant ChIP-seq data i.
For point source peaks e. Metadata, including peak caller approach and genome reference used, plus methods for determining signal values, P -values, and Q-values, as applicable. Point-source peaks can be called in addition to broad regions i. We have begun to address the central but vexing issue of immune reagent specificity and performance by establishing a menu of primary and secondary methods for antibody characterization, including performance-reporting practices.
We related these global quality measures to more traditional inspection of ChIP-seq browser tracks Fig. We believe that multiple issues contribute to the variability; the quality of antibody affinity and specificity is surely important, but epitope availability within fixed chromatin, sensitivity of the antibody to post-translational modifications of the antigen, how long and how often the protein is bound to DNA, and other physical characteristics of the protein—DNA interaction likely also contribute.
Further work with epitope-tagged factors, for which the antibody is not a variable, should begin to sort among the possibilities. When measurements differ in quality, the higher-quality replicate often identifies thousands more sites than the lower. Do sites present only in the superior ChIP experiment reflect true occupancy? Motif analysis suggests that many do. The known binding motif is prominent and concentrated centrally under the ChIP peaks, as expected if the motif mediates occupancy; importantly, the central location of the motif is observed, even in the low-ranking peaks.
The trend continues below the peak-calling cut-offs, suggesting additional true occupancy sites. Depending on the goals of an analysis, users may want to be more or less conservative in defining the threshold for inclusion.
We have observed that some biologically important sites can have modest ChIP-seq signals Fig. Given this, the best practical guidance for setting thresholds of sensitivity, specificity, and reproducibility will depend on how the data are to be used. Below, we outline four different common ChIP uses, ranging from more relaxed to stringent in their requirements toward data quality and site-calling sensitivity.
Causal motifs are typically centrally positioned and this can be used as a confirming diagnostic Fig. Notably, motif derivation can also be successful from marginal quality data that fall below recommended quality metric thresholds especially if only the top-ranked peaks are used. However, the risk of artifacts increases, and results from such analyses should be cautiously interpreted and stringently validated.
Biologists often use ChIP-seq data to identify candidate regulatory regions at loci of interest. However, if the aspiration is to identify a comprehensive collection of all candidate regulatory regions bound by a factor, very high-quality and deeply sequenced data sets are required. Typical cis -acting regulatory modules CRM are occupied by multiple factors Ghisletti et al. A frequent goal of ChIP-seq studies is to deduce a combination of factors that mediate a common regulatory action at multiple sites in the genome.
This is a very quality-sensitive use of ChIP data since the presence of one or more weak data sets that fail to identify significant fractions of the true occupancy sites can seriously confound the analysis; therefore we recommend only the highest quality data sets be used for such analyses.
A new frontier of whole-genome analysis is the integration of data from many hundreds or thousands experiments with the goal of uncovering complex relationships. These endeavors typically use sophisticated machine learning methods Ernst and Kellis ; Ernst et al.
The strongest ChIP-seq data-sets that readily meet all quality specifications should be especially useful for regulatory network inference and for diverse integrative analyses, including the effects of genetic variation on human traits and disease. The metrics, methods, and thresholds might also be useful to the wider community, although our intention in outlining our approaches was not to imply that ENCODE criteria must be applied rigidly to all studies.
As discussed above, some ChIP data and antibodies can and do fall outside these guidelines for varied reasons, yet are highly valuable. A highlight of the PowerControl app is the performance instruments, which show all relevant performance data of the engine in real-time.
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