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ChIP-Seq analysis and visualization using Galaxy and IGB

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Add a Review. Detailed metadata annotations, analysis results and quality control QC metrics are presented for each sample.

Cistrome DB is a comprehensive annotated resource of publicly available ChIP-seq and chromatin accessibility data in human and mouse.

Metadata were automatically parsed from GEO entries, followed by manual curation of factor names and biological sources to ensure annotation consistency.

The number of ChIP-seq and chromatin accessibility experiments has increased dramatically since , with alone contributing new samples Figure 2A.

In total, our database contains 23 ChIP-seq and chromatin accessibility samples, for mouse and 13 for human. Among them, there are 10 ChIP-seq samples for transcription factors and chromatin regulators, 10 ChIP-seq samples for histone modifications and variants, chromatin accessibility samples and the remaining are classified as other Figure 2B.

Database content. A Growth statistics of ChIP-seq and chromatin accessibility data. C Statistics of transcription factor and histone modification type.

E Batch sample visualization through WashU browser showing the co-binding pattern between master transcription factors in embryonic stem cells.

Analysis included three steps: read mapping, peak calling and peak annotation. Peak calling was done using MACS2 Finally, we performed annotation analysis, including average conservation profiles across peak regions, motif analysis 21 and putative gene target identification We provide seven different QC criteria across three layers Figure 2D.

In the reads layer, the median quality score is used to evaluate the raw sequencing quality; uniquely mapped reads is used to reflect mapping quality; PCR bottleneck coefficient PBC 23 is used to identify potential over-amplification by PCR.

In the ChIP layer, the FRiP score 23 fraction of non-mitochondrial reads in peak regions and the number of high quality peaks with or fold enrichment over background were calculated to show data quality at the ChIP level.

QC measures in the annotation layer include the proportions of peaks in promoters, introns and intergenic regions, along with the proportion of peaks overlapping with a union of DNase-seq peaks across diverse cell types.

Using our collection of 23 samples, we established the thresholds of quality control characteristics based on the overall distributions.

The Cistrome DB result page displays whether or not the quality control characteristics of each sample meet these quality control thresholds.

See the Supplementary Material for details on the definition and calculation of QC statistics. Distributions of quality control statistics and thresholds are shown in Supplementary Figure S1 and on the Cistrome DB website.

Peak calling algorithms are specialized in identifying narrow or broad enrichment although there is no precise threshold that distinguishes one category from the other 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.

The accuracy of ChIP-seq experiments is highly reliant on antibody specificity and quality and it is common for antibodies to recognize several proteins apart from the stated target.

Cistrome DB also provides visualization functions that allow users to view peaks and signal intensity in either the UCSC 25 or WashU 26 genome browsers.

Visualization of both single or batch samples is supported. Using Cistrome DB, users can select the relevant ChIP-seq samples and visualize the co-binding pattern between master transcription factors on the WashU genome browser using the sample batch view function Figure 2E.

Cistrome DB provides automatically parsed and subsequent manual curation of metadata annotations for each sample, including species, factor name, biological source, publication and process status, which are stored in a local MySQL relational database.

Each ChIP-seq or chromatin accessibility sample has a unique sample identifier. A web interface has been designed to provide user-friendly access and visualization.

The result page displays detailed annotations, analysis results and quality control metrics for each sample. To track the source of the original sample, links to citations and the data repository are provided.

In addition, Cistrome DB provides a list of putative target genes of the factor for each sample. Users can view the complete ranked list of putative target genes or search for a gene of interest by the gene symbol.

Cistrome DB contains two options for searching. One is through a selection list and the other is based on an advanced search menu.

Users can select a sample of interest from a list of factors and biological sources, or search by factor name, cell type, GSM accession number or other keywords.

Each search produces a table of matched samples. Users can then view detailed data annotations and analyze results by clicking on the table entry.

Users can start their data explorations either by keyword search or by selecting a species and looking at lists of factors or biology sources.

Searching produces a table of matched samples. Cistrome DB makes it easy to query one factor in multiple cell types or multiple factor types within a single cell type.

Cistrome DB provides four layers of content for each sample. First, it provides a manually curated metadata annotation, including the species, factor name, biological source, citation and data accession number.

Second, it presents analysis results, including a peak file, a read density file, motif scan results, putative target genes and summaries of the distribution of peaks across different genomic locus categories.

Third, it provides comprehensive QC metrics at the read, peak and annotation levels. Finally, it provides functions to analyze and visualize these samples; users can directly send data to the Cistrome analysis pipeline Cistrome AP or load data to the UCSC and WashU genome browsers for visualization.

Both single-sample and batch visualization are supported. We present Cistrome DB, the most comprehensive knowledgebase and data portal for ChIP-seq and chromatin accessibility data in human and mouse.

Cistrome DB is a valuable resource for transcriptional and epigenetic regulation studies. Transcriptional regulation is a complex process, which is controlled by hundreds of TFs, cofactors and chromatin regulators With the Cistrome DB data, users can systematically investigate patterns of transcription factor or chromatin regulator binding and histone modifications related to their research questions.

Chromatin profiles in diverse cell types and under various experimental conditions can be used in tissue specific or cell developmental studies Experimentalists can use our historical quality control data to evaluate the quality of their own ChIP-seq or chromatin accessibility experiments.

In the future, the utility of Cistrome DB will be improved in several ways. Cell information, QC metrics, TF type and histone modifications can be further classified.

In addition we will also integrate Cistrome DB with other data. Cistrome DB is more than a data repository, it also allows users to visualize and explore the data.

In comparison with other resources, Cistrome DB is by far the most comprehensive database for curated and analyzed ChIP-seq and chromatin accessibility data.

The authors would like to acknowledge Sujun Chen, Qi Liao and Sheng'en Hu for their helpful discussions about the project. Park P. ChIP-seq: advantages and challenges of a maturing technology.

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