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.
The figure presents the proportion of data publicly available on GEO that have been processed, and integrated into QC Genomics.
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.
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.
Experiments are annotated with references from PubMed, allowing to search by author, PubMed ID, keywords from title/abstract, or journal. When browsing experiments, original citations are displayed, and links to PubMed are provided.
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 four years, 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.
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”.
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”.