A quality control system for profiles obtained by ChIP sequencing

Mendoza-Parra, Marco Antonio; Gool, Wouter Van; Saleem, Mohamed Ashick Mohamed; Ceschin, Danilo Guillermo; Gronemeyer, Hinrich

doi:10.1093/nar/gkt829

Cited by 41 publications

(60 citation statements)

References 34 publications

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“…Peak calling algorithms are specialized in identifying narrow or broad enrichment although there is no precise threshold that distinguishes one category from the other (24). Cistrome DB QC metrics are mostly developed for TFs and histone marks with sharp enrichment; for factors or marks with broad enrichment patterns the current QC measures might not be as reliable.…”

Section: Data Browser Contentsmentioning

confidence: 99%

Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse

et al. 2016

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Chromatin immunoprecipitation, DNase I hypersensitivity and transposase-accessibility assays combined with high-throughput sequencing enable the genome-wide study of chromatin dynamics, transcription factor binding and gene regulation. Although rapidly accumulating publicly available ChIP-seq, DNase-seq and ATAC-seq data are a valuable resource for the systematic investigation of gene regulation processes, a lack of standardized curation, quality control and analysis procedures have hindered extensive reuse of these data. To overcome this challenge, we built the Cistrome database, a collection of ChIP-seq and chromatin accessibility data (DNase-seq and ATAC-seq) published before January 1, 2016, including 13 366 human and 9953 mouse samples. All the data have been carefully curated and processed with a streamlined analysis pipeline and evaluated with comprehensive quality control metrics. We have also created a user-friendly web server for data query, exploration and visualization. The resulting Cistrome DB (Cistrome Data Browser), available online at http://cistrome.org/db, is expected to become a valuable resource for transcriptional and epigenetic regulation studies.

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Section: Data Browser Contentsmentioning

confidence: 99%

Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse

et al. 2016

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“…Sequence-aligned files were qualified for enrichment using the NGS-QC Generator (Mendoza-Parra et al 2013b). Briefly, this methodology computes enrichment quality descriptors discretized in a scale ranging from "AAA" (Best) to "DDD" (worst).…”

Section: Chromatin Immunoprecipitation Assaysmentioning

confidence: 99%

Reconstructed cell fate–regulatory programs in stem cells reveal hierarchies and key factors of neurogenesis

Mendoza-Parra¹,

Malysheva²,

Saleem³

et al. 2016

Genome Res.

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Cell lineages, which shape the body architecture and specify cell functions, derive from the integration of a plethora of cell intrinsic and extrinsic signals. These signals trigger a multiplicity of decisions at several levels to modulate the activity of dynamic gene regulatory networks (GRNs), which ensure both general and cell-specific functions within a given lineage, thereby establishing cell fates. Significant knowledge about these events and the involved key drivers comes from homogeneous cell differentiation models. Even a single chemical trigger, such as the morphogen all-trans retinoic acid (RA), can induce the complex network of gene-regulatory decisions that matures a stem/precursor cell to a particular step within a given lineage. Here we have dissected the GRNs involved in the RA-induced neuronal or endodermal cell fate specification by integrating dynamic RXRA binding, chromatin accessibility, epigenetic promoter epigenetic status, and the transcriptional activity inferred from RNA polymerase II mapping and transcription profiling. Our data reveal how RA induces a network of transcription factors (TFs), which direct the temporal organization of cognate GRNs, thereby driving neuronal/endodermal cell fate specification. Modeling signal transduction propagation using the reconstructed GRNs indicated critical TFs for neuronal cell fate specification, which were confirmed by CRISPR/Cas9-mediated genome editing. Overall, this study demonstrates that a systems view of cell fate specification combined with computational signal transduction models provides the necessary insight in cellular plasticity for cell fate engineering. The present integrated approach can be used to monitor the in vitro capacity of (engineered) cells/tissues to establish cell lineages for regenerative medicine.

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“…To assess more thoroughly the performance of ChIC RF-score, we benchmarked it against other individual quantitative scores to assess ChIP-seq QC, including independent metrics not comprised in our compendium. Namely, in the benchmarking we considered: 1) the RSC and NSC values, derived from cross-correlation strand-shift analysis as described in [3], which are also part of the EM scores, computed using the spp package [11]; 2) the QC tag, which is a discretized version of the RSC score as described in [4]; 3) the recently proposed RSC, NSC based on the Jaccard Index of the strand-shift analysis, and the background uniformity (bu) metrics computed by the SSP package [7]; 4) the "fraction of reads in the top 1% bins", which is also part of the GM scores as described above and derived from the "fingerprint plot" originally proposed by the CHANCE tool [8]; 5) the Jensen-Shannon Distance (JSD) computed on the fingerprint plot as implemented in deepTools [22]; 6) in addition to the global density QC indicator (denQCi) and the QC-STAMP scores at 5% dispersion as computed by the NGS-QC Generator tool [21]. The latter two are another example of metrics examining the global read distribution across genomic bins.…”

Section: Benchmarking Against Other Chip-seq Qc Scoresmentioning

confidence: 99%

“…Moreover, a common view shared by literature in the field [4,7,21] is that a single score providing a reliable summary of the quality of a ChIP-seq sample would be convenient for end users. Despite a few solutions have been proposed [4,7,21,22], there is not yet a consensus in literature on an unbiased single score to reliably discriminate between good and poor-quality samples. Thus, a broad ensemble of parameters must be considered [3].…”

Section: Introductionmentioning

confidence: 99%

A ChIC solution for ChIP-seq quality assessment

Livi

Tagliaferri

Pal

et al. 2020

Preprint

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Despite the widespread adoption of the ChIP-seq technique, there is still no consensus on quality assessment procedures. Quantitative metrics previously proposed in literature are not always effective in discriminating the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. Here we introduce ChIC, a new framework for ChIP-seq data quality assessment that overcomes the limitations of previous solutions.ChIC is the first method for ChIP-seq quality control directly considering the enrichment profile shape, thus achieving good performances on ChIP targets yielding sharp and broad peaks alike. We integrate a comprehensive set of quality control metrics into one single score reliably summarizing the sample quality. The ChIC score is based on a machine learning classifier trained on a compendium with thousands of ChIP-seq profiles, which can also be used as a reference for easier evaluation of new datasets. ChIC is implemented as a user-friendly R/Bioconductor package. RESULTS Standard QC-metrics are biased by the shape of ChIP-seq enrichment profileWe analysed a large set of ChIP-seq experiments with paired input control, for a total of 3936 samples from large public databases (Table 1), including the ENCODE project [23] and Roadmap Epigenomics Consortium (Roadmap) [24], to build a reference compendium of QC-metrics, including previously proposed and novel metrics (Fig. 1a).Previously proposed QC quantitative metrics for ChIP-seq do not explicitly take into account the shape of the enrichment profile, yet this is affecting the QC score values. For example, some of the QC-metrics proposed by the ENCODE consortium are more effective on ChIP targets yielding narrow peak profiles, such as TFs, because they are designed for ChIP-seq enrichment profiles with sequencing reads localized in small regions. This is the case for all the metrics based on strand-shift analyses [3,4,7,10,11]. Strand-shift analyses, such as the "cross-correlation" analysis (Supplementary Figure S1a; methods), aim to detect the clustering of reads in a ChIP-seq sample without relying on peak calling. The strand-shift profile is calculated using the position of reads on positive vs negative strand and shifting them towards each other. At each progressive shift the correlation [3,4] between the position of reads on the two strands is calculated. In other variants of this analysis, other measures like the Jaccard Index [7] or the Hamming distance [15] have been proposed instead to quantify the clustering of reads on positive and negative strands. Then, multiple QC-metrics for the strength of the ChIP enrichment can be derived from the resulting crosscorrelation profile, for example the normalized or relative strand coefficient (NSC and RSC, respectively) or the quality control tag (QC tag) [3,4,11]. We refer to this set of metrics, along with others described by Landt et al. [3], as ENCODE Metrics (EM) (see methods).Notably, the relative strand coefficient (RSC) is proportional to the level of clusterin...

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A quality control system for profiles obtained by ChIP sequencing

Cited by 41 publications

References 34 publications

Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse

Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse

Reconstructed cell fate–regulatory programs in stem cells reveal hierarchies and key factors of neurogenesis

A ChIC solution for ChIP-seq quality assessment

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