QC Genomics

QC Genomics is a public resource providing a central access to the largest collection of genomic data. It allows scientists to browse, visualize, compare, and analyze thousands of publicly available genomic data sets.

Not sure what to search? Try MCF-7 ESR1, mESC H3K27me3, or Bernstein ATAC-seq.

QC Genomics resources

 NGS-QC Generator

Galaxy tool for the quality control of NGS data.

 NGS-QC database

Database of quality control descriptors for ChIP-seq and related assays.


Long-range genome interactions (e.g. Hi-C) quality assessment.


Reconstruction of temporal transcriptional gene regulatory networks.


Exploration and visualization of thousands of NGS experiments.

 QC Comparator

A fast, integrative and exhaustive NGS datasets comparison tool.

 QC ChromStater

Discovering de novo re-occuring combinatorial and spatial patterns of marks.

 Data integration

QC Genomics features quality indicators for thousands of ChIP-seq, RNA-seq, chromatin accessibility, and Hi-C experiments collected from the ENCODE Consortium, the NIH Roadmap Epigenomics Mapping Consortium, and the Gene Expression Omnibus.

 Extensive knowledge base

Experiments are processed through a standardized pipeline, and their related metadata are curated using controlled vocabularies. As a result, QC Genomics represents the most complete collection of uniform enrichment-based data.

 Many (epi)genetics methods

QC Genomics covers a plethora of *Seq assays: from commonly used (e.g. ChIP-seq, FAIRE-seq) to recently developed (e.g. ChIPmentation), and we are continuously integrating new methods.

Click on a bar to load all experiments associated to the method.

  • pmid:26769127
    Genomic data integration
  • GSM1267197
    Hi-C data visualization
  • ERX432401,ERX432405
    Chromatin loops

 Genome browser

Our in-house genome browser allows you to visualize profiles of interest, integrate your own data, and explore genome interaction maps (e.g. Hi-C) from the LOGIQA collection.


We regularly have updated QC Genomics over the past three years, following a strict release cycle, and we are processing new data sets as they become available.


Experiment quality is assessed by NGS-QC Generator, an universal in-silico method that infers local and global quality indicators by detecting genomic regions with a robust signal.

The figure presents the sequencing depth and the quality of experiments associated to random search queries. Click on a point to load the experiments.  Reroll.

QC Comparator

Comparing multiple ChIP-seq profiles by looking at a few loci with a genome browser cannot replace a comprehensive analysis of profile differences, nor does it provide a quantitative assessment of the degree of similarity. Addressing this issue, qcComparator provides a global similarity matrix for all requested datasets (up to 500), such that divergent or related datasets in the queried samples can rapidly be identified. Users can chose to cluster datasets on the basis of various similarity index distance metrics, different read count intensity dispersion thresholds and/or multiple random sampling combinations. The final matrix provides information concerning the target molecule, the cell/tissue source and the quality (qcStamp) associated to each of the datasets.

To explore these functionalities, users have to first query their favorite datasets here and then submit the query to the option “Analyze with/QC Comparator”.

QC Comparator
Example illustrating the similarity matrix computed for 221 datasets, including target molecules like H3K4me3, H3K36me3, H3K27me3 and RNAPol2. Note that the Euclidean clustering analysis clearly aggregates datasets according to their target molecule ID.

QC ChromStater

Understanding the functional role of the various factors interacting with the genome requires a combinatorial analysis of their co-localization. qcChromStater computes co-occupancy events among several (up to hundred) requested datasets within and surrounding the coding regions. This analysis identifies enriched co-occurring events, which can be associated by the user to a functional annotation. This functional annotation can be used in a second step to identify the associated coding regions and this by stratifying the information in a cell/tissue context.

To explore these functionalities, users have to first query for their favorite datasets here and then submit the query to the option “Analyze with/ QC ChromStater”.

QC ChromStater
Example illustrating the identification of 23 combinatorial states compiled from multiple histone modification datasets and CTCF. These combinatorial states were subsequently associated to a functional annotation, which can be linked to the respective genes.