Antibody performance in ChIP-sequencing assays: From quality scores of public data sets to quantitative certification

We have established a certification system for antibodies to be used in chromatin immunoprecipitation assays coupled to massive parallel sequencing (ChIP-seq). This certification comprises a standardized ChIP procedure and the attribution of a numerical quality control indicator (QCi) to biological replicate experiments. The QCi computation is based on a universally applicable quality assessment that quantitates the global deviation of randomly sampled subsets of ChIP-seq dataset with the original genome-aligned sequence reads. Comparison with a QCi database for >28,000 ChIP-seq assays were used to attribute quality grades (ranging from ‘AAA’ to ‘DDD’) to a given dataset. In the present report we used the numerical QC system to assess the factors influencing the quality of ChIP-seq assays, including the nature of the target, the sequencing depth and the commercial source of the antibody. We have used this approach specifically to certify mono and polyclonal antibodies obtained from Active Motif directed against the histone modification marks H3K4me3, H3K27ac and H3K9ac for ChIP-seq. The antibodies received the grades AAA to BBC ( www.ngs-qc.org). We propose to attribute such quantitative grading of all antibodies attributed with the label “ChIP-seq grade”.


Introduction
Chromatin immunoprecipitation combined with massive parallel sequencing (herein described as ChIP-sequencing or ChIP-seq) is currently intensively used as a method for assessing protein-DNA interactions and/or chromatin modifications on a genome-wide scale. It is well-accepted that optimal ChIP-sequencing assays require highly specific and sensitive antibodies, thus an important battery of validation tests is strongly recommended for their characterisation 1 . In addition to the concerns of the scientific community about the current strategies for validating antibodies, which are commercially promoted for certain various applications, also the assessment of antibody performances in ChIP-sequencing assays remains still a major issue, mainly due to the absence of quantitative approaches for qualifying ChIP-seq assays.
We have previously described a universal in silico approach to generate quality descriptors for any ChIP-sequencing and related datasets 2 . Importantly, this concept has been used to establish the largest database worldwide, which harbors currently the quality scores for more than 28,000 publicly available datasets (www.ngsqc.org). Notably, in contrast to other metrics previously described for the qualification of ChIP-seq assays 3 , this system evaluates the robustness of the enrichment patterns populating a given profile by comparative analyses of the original profile and profiles generated after random sub-sampling from a reduced fraction of the total mapped reads. This methodology is applicable to any type of enrichment-related dataset and the inferred quality indicators can thus be used for comparative purposes. In order to provide simple and intuitive quality designations the quality indicators were discretised using a three letter grading score; accordingly, the profiles range from highest "AAA" to the lowest "DDD" quality.
In this study, we present first an analysis concerning the quality of the available >28,000 data sets in the context of their sequencingdepths used and of the antibody sources. Thereafter, we introduce a certification procedure, which should be used to associate a defined referenced batch of an antibody with a reliable validated ChIPsequencing grade.

NGS-QC database content
All datasets presented in the retrospective analysis were originally retrieved from the GEO database and processed with the NGS-QC Generator algorithm. Quality indicators (QCis) were computed as previously reported 2 . Briefly, QCis were generated by comparing the original read intensity profile with those observed in a fraction of the total mapped reads. For this, total mapped reads (TMRs) were first randomly sub-sampled at three defined subsets (90%, 70% and 50% respectively), then the read counts in 500 nt bins of the genome were computed for each of the random sub-sampled as well as in the original dataset. In the ideal theoretical case the read counts in all genomic bins are expected to decrease proportionally to the random subsampling (e.g., a 50% decrease of the read count intensity (RCI) when 50% of the original TMRs were sub-sampled). Genomic regions presenting the lowest variations from this theoretically expected value are considered "robust" to the random sampling and thus of high quality. For quantitation we calculated the fraction of genomic windows with RCI dispersions within defined levels (2.5, 5 and 10%).
Quality scores for all analysed publicly available datasets are available at www.ngs-qc.org. The antibody references associated with the ChIP-seq datasets analysed in this study are given in Table 1.

Antibody certification procedure
We propose the following certification procedure for antibodies to be used in ChIP-seq and related enrichment-based technologies. Clearly, this certification will not replace but rather complement the molecular biology assays currently used for validating antibody specificity. Indeed, it provides a certificate of antibody performance specifically in ChIP-seq assays. The following experimental conditions were used for the certification of antibodies described in this study: Following the reviewers' comments, we have improved our initial document by incorporating further elements in the 'Conclusion' section. In fact, following the comments of reviewer #1, we have further explained the spirit of the antibody certification procedure, which is based on the establishment of a standardized procedure and not a standardized set of reagents. Furthermore, we have explained the fact that due to the absence of systematic information concerning the antibody batch for polyclonal antibodies, it becomes difficult to perform dataset population studies in the context of their quality grades. Finally we wanted to highlight that at the time of the resubmission of this version of the manuscript, our database bypassed 30,000 processed datasets.
Overall, we believe that the modifications included in this new version of the manuscript have adequately addressed the points raised during the revision process. as follows: 20µL of sonicated chromatin was diluted with 20µL TE (10mM Tris-HCl pH 8, 1mM EDTA) and 5µL 5M NaCl was added. Diluted chromatin was incubated at 100°C for 30 min, centrifuged at 12,000 rpm at room temperature and the supernatant was loaded onto a 2% agarose gel.

REVISED
Chromatin Immunoprecipitation: 25µL of ChIP-IT Protein G Magnetic Beads (ActiveMotif) were incubated with the antibody under evaluation (the amounts of antibody in use corresponded to that indicated by the supplier's information) in presence of 100µL PIC-containing Lysis Buffer. After two hours at 4°C on a rotating shaker, chromatin from 3 million cells was added and the final volume was adjusted to 500µL with PIC-containing Lysis Buffer and incubation on a rotating shaker was continued overnight at 4°C. The immunoprecipitated chromatin was recovered by magnetic bead separation, followed by multiple washing steps on a custom liquid handling platform (TECAN EVO75). Specifically, the washing is performed as follows: (1)  DNA library preparation and massive parallel sequencing. The DNA library preparation for massive parallel sequencing was performed according to standard procedures (NEXTFlex ChIP-Seq Kit (Biooscientific)) adapted to automation by our custom liquid handling platform (TECAN EVO75). Prior to DNA sequencing library preparation was monitored using a Tapestation (Agilent). Samples were sequenced on an Illumina HiSeq2500 platform following manufacturer's standard procedures.

NGS-QC certification.
The antibody certification is based on the quality control system previously described 2 . The certification is based on two biological replicate ChIP-seq assays performed at high sequencing depths (~50 million mapped reads per dataset). For each replicate the global quality grades were computed. In addition the following issues were part of the certification: Optimal sequencing depth: This is performed by re-computing quality grades at decreasing fractions of the initial total mapped reads (TMRs). Briefly, defined TMR subsets were generated by random sampling (20%, 40%, 60%, 80% and 100% of TMRs) and quality scores were assessed. The sequencing depth at which the quality grades transit from A to B is extrapolated and designated as optimal sequencing depth.

Local QC Irreproducibility Discovery Rate (local QC-IDR):
Concordance among biological ChIP-seq replicate assays were previously assessed by the Irreproducibility Discovery Rate assay 4 . Similarly, we have established an IDR-type assay based on the comparison of the location of genomic regions (500nt length) presenting the lowest read count intensity dispersion (dRCI). Briefly, genomic regions displaying a dRCI <10% in the two biological replicates were paired and ranked on the basis of their lower absolute difference between paired dRCI values (genomic regions present in only one dataset are kept and paired with a "penalty genomic window" for which a dRCI=15% was allocated). Finally, the local QC-IDR was defined as the fraction of the top 5,000 windows using a sliding window of 500nt.
Comparing QC scores with those assessed for publicly available data. Antibody certification scores were compared with quality scores computed from publicly available data in the context of their TMRs. For it, a scatter-plot displaying quality scores (y-axis) relative to the TMRs (x-axis) was generated for all datasets associated to the particular target molecule, as well as for that related to the certified antibodies.

Contents of the NGS-QC database
The NGS-QC Generator database hosts currently quality scores for >28,000 datasets (www.ngs-qc.org; December 2015), covering a variety of ChIP-sequencing and related assays (e.g. Dnase-seq, FAIRE-seq, MBD-seq, GRO-seq, etc) performed from 9 species (Homo sapiens: 54%; Mus musculus: 34%; D. melanogaster: 6%; etc) ( Figure 1A). As it is based on datasets available from GEO, we retrieved antibody sources for 12,036 datasets from the complementary information; 96% of these corresponded to human or mouse-related assays ( Figure 1B). Furthermore, these datasets concern 680 different target molecules, with several histone modifications being highly represented ( Figure 1C). Finally, thanks to the information provided by the authors, we managed to trace the commercial sources of the antibodies ( Figure 1D). Overall, this analysis reflects the interest of the scientific community in studying the role of epigenetic factors in human and mouse systems. The data reveal also that three companies dominate the market, as they provided the antibodies for 75% of the evaluated datasets.
ChIP-seq quality relative to sequencing-depth and antibody source As ChIP-seq datasets for the histone modifications H3K4me3, H3K27ac, H3K27me3 and H3K4me1 are the most represented in the collection ( Figure 1C), we focused our analyses on these datasets. . The NGS-QC Generator database hosts currently quality scores for 28,108 datasets from a variety of organisms. For about 50% of the datasets the commercial source of the used antibodies could be retrieved from the information present in GEO. (B). Classification per organism of the qualified datasets for which the antibody source is known. Note that nearly 90% correspond to Homo sapiens or Mus musculus datasets. (C). Top ranked target molecules for which the antibody source is known. (D). Ten most-used commercial antibodies retrieved from the NGS-QC database.
We first evaluated quality grades in the context of sequencing depths. Importantly, the expected increase in quality relative upon increased sequencing-depth is not uniform but is rather targetdependent, as is supported by the different slopes of the datasets ( Figure 2A). Indeed, while H3K4me3 datasets reach high quality even with relatively low sequencing depth (~25M TMR), the profiles for H3K27me3, which display broad signals, require >60M TMRs for reaching an "AAA" qualification ( Figure 2A). Figure 2B illustrates the differences in quality that correspond to the different quality grades ( Figure 2B); note that these data have been obtained with human embryonic stem (ES) cells using the same antibody 5-7 .
Another aspect to highlight is the fact that there is a high variability of quality scores for profiles from <50M TMRs. In contrast, all datasets with higher sequencing depths tend to have quality grades between "B" and "A" but the number of such datasets is low compared to those with less sequencing coverage. These observations clearly support the notion that in addition to the sequencing depth, other experimental factors -including the commercial source of the antibodydirectly influence the global quality of ChIP-sequencing assays.
To further explore the role of the antibody source, we have classified the different datasets for antibody vendors and TMR intervals, such that the quality grades per datasets are displayed in context of these two parameters. As illustrated in Figure 3, this classification recapitulates the influence of the sequence depth irrespective of the antibody source and reveals the target-specific quality differences. For example at <50 million TMRs most of the evaluated H3K27me3 Scatter-plot illustrating global quality scores relative to sequencing depth for ChIP-seq assays concerning the indicated histone modification marks. All illustrated mouse or human datasets were retrieved from the public domain and qualified with the NGS-QC Generator. Quality scores were computed for a read count intensity dispersion (dRCI) of 2.5%. Broken lines depict to the transition borders between discretized quality grades ("A", "B", "C" or "D") defined as the quartiles of the quality distribution computed from the entire NGS-QC collection (>28,000 datasets). The continuous lines correspond to the locally weighted scatter-plot smoothened (LOWESS) regression curves (displayed confidence interval p-value: 0.995). (B) Local genomic view (HoxD cluster) displaying read count intensity patterns for the histone modification mark H3K27me3 derived from three different datasets. In all cases the same cell type (human ES cells) and antibody source (Millipore: # 07-449) has been used, while different DNA sequencing coverage was applied. Note that the associated quality grades correlate with the sequencing depth. Figure 3. Bar graphs illustrating the frequency of datasets related to a defined antibody vendor per quality grade and total mapped reads (TMRs) interval. Datasets for each target were categorized on the basis of their sequencing coverage (X-axis: from 1 to 100 total mapped million reads; intervals of 10 million and a last category from 100-500 million), as well as on their quality grade (Y-axis: from "A" to "D", defined on the basis of a read count intensity dispersion (dRCI) threshold criterion of 2.5%). The illustrated bar graph correspond to the fraction of datasets related to a given vendor per TMRs interval. A to D. Frequency bar graphs corresponding to H3K27me3, H3K4me1, H3K27ac and H3K4me3, respectively. datasets ( Figure 3A) have quality grades lower than "A". However, while datasets generated with an antibody from a particular vendor would rapidly gain in quality grades with increased TMRs, those from other sources improve only weakly and require much more TMRs to reach the highest quality grades; examples are the H3K27me3 antibodies of Millipore and Abcam with the Millipore one exhibiting the higher robustness. Similarly, H3K4me1 datasets require >20M TMRs to reach high quality grades but a few antibodies yielded datasets at 20-30M TMRs that got grade "A", while others needed much higher coverage to reach "A" grades; examples are again the Abcam and Millipore antibodies, which show the inverse robustness of their H3K27me3 counterparts ( Figure 3B). Notably, datasets related to the histone modification marks H3K27ac and H3K4me3 present high quality grades even for lower TMRs. In fact a minimum of 10-20 million reads was sufficient to attribute quality grade "A" to some H3K27ac datasets ( Figure 3C) and even lower DNA sequencing coverage was required to reach such quality scores for H3K4me3 datasets ( Figure 3D).
Taken together this analysis reveals that while there is a general direct correlation between the sequencing depth and the quality scores of the corresponding profiles, there are three important considerations: (i) a minimal sequencing depth is required to ensure an acceptable quality of the assay; (ii) the nature of the histone mark has a profound impact on the sequencing depth needed to reach high quality grades, as the increase in quality with increasing coverage is not genuine and differs between marks; and (iii) the source of the antibody has a significant impact on the quality of the profile that can be obtained at a certain sequencing depth.
Overall, this meta analysis provides not only numerical values to assess the effect of the sequencing depth on ChIP-seq quality, but it offers in addition the possibility to rate the performance of new antibodies and antibody batches. Note, however, that a multitude of additional factors affects the quality of ChIP-seq datasets independently of the antibody source; these factors include cell types/tissues in use, the chromatin fixation conditions, experimenter variability, and many more. Indeed, the scatter of QCis observed at defined TMR intervals (Figure 2A) results most likely from such effects. As a consequence antibody certification for ChIP-seq assays should be based on standardized experimental conditions together with quality assessment derived from biological duplicates at high sequencing depth, such that optimal sequencing depths can be recommended to the users.

Improved certification for "ChIP-seq grade" antibodies
To respond to the needs of the scientific community for highly reliable, target specific, low background polyclonal (and monoclonal) antibody sources for epigenome or any other ChIP-based studies commercial antibody suppliers have incorporated "ChIP-seq grade" antibodies in their portfolio and based this grade on visual inspection of generally a single selected genomic region from a ChIP-seq profile. However, there are several problems associated with this procedure: First, the screenshot of a rather short genomic region says next to nothing about the quality of the entire genome-wide profile. Second, the assay conditions and sequencing statistics are generally not provided, and third, information on replicate and batch variation is generally missing.
To improve this situation we have set up a certification procedure which is based on (i) the use of a highly reproducible pipeline for performing ChIP-seq samples preparation and (ii) the use of the NGS-QC Generator tool for assessing quality grades from biological replicates that can this time reflect the performance of the antibody ( Figure 4A). The procedure involves an extensive documentation including experimental details, full sequencing statistics and quality analysis of the profiles obtained from biological replicates together with recommendations for how to use this antibody to obtain optimal quality grades.
This certification procedure, which is based on the use of an automated liquid handling platform for ChIP assays and subsequent preparation of the corresponding sequencing library, has been set up with a panel of six antibodies kindly provided by Brian Egan of Active Motif. Specifically, antibodies targeting the histone modification marks H3K4me3, H3K27ac and H3K9ac were enrolled in the certification process; for each of them monoclonal and polyclonal preparations were assessed. ChIP assays were performed with Hela cells cultured and formaldehyde-fixed (see Materials and methods). The efficacy of the ChIP was verified by quantitative RT-PCR ( Figure 4B). The conditions for the automated preparation of the sequencing library were also standardized.
Massive parallel sequencing was performed at high depth such that coverage was not a limiting factor for quality assessment and that the minimal sequencing depth to obtain "A" quality scores could be predicted ( Figure 4C). An important component is the evaluation of the irreproducibility discovery rate of biological replicates as an integral part of the certification process ( Figure 4C). Finally, we also performed a comparison with the quality grades assessed relative to the publicly available datasets ( Figure 4D) and we provide screenshots of several local genomic regions to illustrate the read count enrichment for the biological replicates and to provide a local quality score (500nt window, heatmap; Figure 4E).
Overall, this certification procedure provides quantitative means for assessing antibody performance in ChIP-sequencing assays, thus their "ChIP-seq grade" would not represent a questionable marketing argument but rather a solid certification label.

Conclusion
As ChIP-sequencing assays are becoming widely used for studies in epigenetics/chromatin modification and the definition of chromatin interactions with transcription factors and other regulatory factors/machineries, it is of highest importance that scientist have access to antibodies of high quality and reliability such that the antibody performance can be excluded as a source of low quality and variability between datasets. Such quality differences are obvious from QC indicator database that we have established (www. ngs-qc.org). At the time of the revision of this manuscript, this database comprises more than 30,000 qualified datasets and this number is expected to increase largely within the years with the "democratisation" of this technology. It is important to point out that the present retrospective analysis revealed that an important fraction of the datasets in the public domain is below acceptable quality standards to perform for example multi-profile comparisons. promoter regions are used for the evaluation of the enrichment levels (Fold occupancy relative to DPP10). (C). Extrapolation of the optimal sequencing depth by sub-sampling of the initial TMRs. The inferred quality scores are displayed relative to the mapped reads. The vertical green line defines the reads at which the transition of quality grade from "A" to "B" is observed. (D). Irreproducibility discovery rate (IDR) of biological replicates. The local QC IDR is defined as the fraction of genomic regions (500nt window size) which exhibit reproducibly read count intensity dispersion levels (dRCI) below 10%. For computation, all genomic regions were subdivided in groups of 5,000 windows (sliding window with a span of 500nt) followed by ranking according to their RCI dispersion. The X-axis displays the ranked genomic regions for which the local QC IDRs (Y-axis) were computed. The broken horizontal line delimits a local QC IDR threshold of 0.1, which is used to define the number of genomic windows with sub-threshold IDR levels. (E). Scatter-plot displaying the quality scores computed during the antibody certification (triangles correspond to the two biological replicates used in the certification process) relative to those of publicly available H3K27ac ChIP-seq datasets. (F). Genomic region (chromosome 12), illustrating the local enrichment of H3K27ac mark generated by the certified antibody. Below each read count intensity profile, a heatmap illustrates the robustness of genomic bins (500nt) to random sampling as measured by their dRCI levels (yellow to black: 0-10%). The certification grade attributed to this antibody is quality grade "AAA" as indicated.
Here we clearly demonstrate, on the basis of a numerical quality evaluation of a large number of datasets, several features which impact on ChIP-seq quality. First, there is a direct correlation with the sequencing depth. Second, we reveal the direct influence of the nature of the molecular factor as another parameter to consider for the minimal sequencing depth that has to be used to get high quality datasets. As a rule of thumb transcription factors and histone modifications that produce locally confined signals ("sharp peaks") in the corresponding profiles can be sequenced at lower coverage that for example histone marks which generate "broad peaks". Third, given that analysis is based on a rather comprehensive collection of datasets, we were for the first time in a position to evaluate the effect of the commercial source of an antibody on the quality of the obtained datasets. Importantly, while we did not observe dramatic differences among the various antibodies at high sequence coverage, there are indeed significant differences when sequencing was performed at lower coverage. Fourth, there is a large difference between the quality of experiments even when the same antibody and the same cell type is used; most plausibly this is due to difference in the performance/materials/methods that have been used in the various laboratories. Finally, taken in consideration that an important number of ChIP-sequencing assays are performed with polyclonal antibodies, potential differences between antibody batches could be in principle evaluated via dataset quality assessment. This being said, it is difficult to perform dataset population studies between antibody batches, as this information is not systematically available in public repositories.
For this reason we have developed an antibody certification procedure dedicated to ChIP-sequencing applications, in which (i) sample preparation is performed under standardized conditions; and (ii) quantitative analytical metrics are applied for assessing the antibody performance. With such a certification at hand the experimenter knows the performance of a given antibody and can follow the guidelines for its use to obtain maximal quality at minimal cost.
This methodology has been set up with multiple antibodies provided by Active Motif; the corresponding quality certification reports are freely available from our website (www.ngs-qc.org).
Considering that this approach provides quantitative data for attributing the "ChIP-seq grade" to a particular antibody batch, the use of such metrics will significantly improve consumer confidence in this label. It is worth mentioning that the above proposal for an antibody certification procedure does not imply to imperatively use the specified reagents for library preparation or other steps of the certification procedure but rather emphasizes the need for standardized procedures, which reduce the impact of experimenter-derived variability by the implementation of automated liquid handling steps. In that manner, ChIP-sequencing dedicated antibodies would be optimally evaluated on their performance such that at the end we could call them "ChIP-seq grade" on the basis of quantitative readouts.

Data access
Quality reports for all ChIP-sequencing datasets discussed in this study are available via the NGS-QC database (www.ngs-qc.org). Raw data associated to the discussed antibody certification procedure is available at the NCBI Gene expression Omnibus database under accession number GSE76618.  ChIP-seq is becoming a standard tool for transcription factor or histone mark mapping, but many assumptions are made about the reproducibility of this method, particularly between factors. There is a general assumption that one set of parameters are appropriate for all ChIP-seq experiments, regardless of the factors being purified. No genuine gold standard exists for ChIP-seq and the current accepted guidelines defined by Encode, may or may not be of sufficient robustness (i.e. Encode recommend two ChIP-seq replicates, which eliminates any possibility for statistical differential analysis and simply wouldn't be acceptable for almost any other genomic analysis). One big issue in ChIP-seq, is the quality of the antibody and the number of required reads to get robust data. This manuscript provides a very useful and important tool for dealing with this critical issue. The QC indicator described and tested in this manuscript, provides a user-friendly way of gaining insight into the best antibody for a factor and the minimum read depth needed for an experiment to capture most binding sites and to therefore be considered successful.

Author contributions
The system described here provides information about the best reagents and the appropriate sequencing depth, using a clear and informative grading system. The findings from this paper show that different sources of antibodies have distinct efficiencies and that different sequencing depth is required for individual factors, an importantly and largely unknown finding. Whilst the method is based on a priori individual factors, an importantly and largely unknown finding. Whilst the method is based on a priori knowledge and doesn't account for non-specific antibodies or isn't appropriate for novel factors that haven't been previously explored, the QCi approach is an important and much needed resource, for both novices and experts. This tool will undoubtedly improve the quality of the data that is being produced and analysed by the community and this should be the first place to start for anyone thinking of doing ChIP-seq experiments.
We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
No competing interests were disclosed.

Marco-Antonio Mendoza-Parra
We would like to thank Jason Caroll and Adam Nelson for investing time in the revision of our manuscript. Specially, we are very pleased to learn that we share a common interest in identifying the parameters that impact on the performance of ChIP-seq and related assays. Indeed, it is our aim to describe in this manuscript the usefulness of the NGS QC system, which we have provided freely to the scientific community to infer optimal sequencing depth levels, in addition to providing a resource that specifies the relative performance of previously used antibodies in ChIP-seq assays on the basis of numerical quality indicators. Finally, we describe an antibody certification procedure, which can be used to assess the ChIP-seq "grade" of any existing or novel antibody batch.
No competing interests were disclosed. This paper suggest a quantitative approach for certification of ChIP-seq grade antibodies. This is an interesting aim and the authors point out several factors influencing the quality of ChIP-Seq data sets.
In the paper they have retrieved antibody sources for 12036 datasets from GEO, and these data are illustrated in different figures. Using data from GEO they have also produced a figure illustration how different sequencing depth can be needed for different histone modification analyzed. The authors have also used 3 antibodies from Active Motif to illustrate how they can give scoring to a given antibody. However, if the authors aim to make certification of specific antibodies they should provide a more detailed and standardized protocol. Moreover, they should also comment why they choose to x-link 30 min in paraformaldehyde, rather than more common 10 min in 1% formaldehyde. The choice of kit for making library should be discussed, and also why they have used a not so common kit in their procedure. Also variation in performance between different lot of polyclonal antibodies should be discussed. It would Also variation in performance between different lot of polyclonal antibodies should be discussed. It would also be interesting with a discussion of the practical relevance of the different grades from scoring for a specific antibody.
I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
No competing interests were disclosed.

Marco-Antonio Mendoza-Parra
We are very grateful to Antonio Hurtado for reviewing our manuscript, and for his useful comments/suggestions to improve our manuscript. Following the reviewer's suggestions, we have included additional elements in the revised version of the manuscript. Specifically we would like to emphasize that the described certification procedure does not imply to imperatively use a given library preparation kit nor any of the other reagents. Our aim was solely to advocate for the use of a standardized protocol, which includes the use of an automated procedure to minimize the impact of experimenter-derived variability. Along the same lines, paraformaldehyde cross-linking conditions were those previously used for various types of assays, including the ChIP-seq of transcription factors. This being said, we do agree with the reviewer that shorter crosslinking times are currently used by several laboratories; nevertheless we would like to emphasize that 30 minutes paraformaldehyde fixation did not prohibit multiple assays to perform with the top level "triple A" quality.
We have also included modifications in the Conclusion part of the manuscript concerning the evaluation of antibody batches. Indeed, in the same manner that different antibodies sources can affect with the performance of the assay, we believe that different batches of polyclonal antibodies may present variable quality performances. However, it is difficult to perform population studies using public data in the context of antibody batches, as there is no systematic information about the antibody batch in public databases. In this context, we want to point out that the scientific community would benefit significantly if repositories like GEO would ask authors at the time of submission to include a mandatory description, of the experiments corresponding to the datasets provided, including among others the target, cell line or tissue, the antibody source and batch, and all other relevant experimental descriptions.
No competing interests were disclosed. Competing Interests: