Services

ChIP-Seq

We provide standard processing and quality assessment for single- and paired-end chip-seq data. More specific analysis are also available on demand. See below for details.

RNA-Seq

We provide standard processing and quality assessment for single- and paired-end rna-seq data. More specific analysis are also available on demand. See below for details.

ATAC-Seq

We provide standard processing and quality assessment for paired-end atac-seq data. More specific analysis are also available on demand. See below for details.

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These services are developped in collaboration with GBCS in Heidelberg. Visit their website.

Details

ChIP-Seq

The processing of single- and paired-end data is currently provided through a galaxy workflow as requested by biologists.

You receive the following from us:

  • QC (quality control)
    • A multiqc report containing different metrics: single-end and paired-end.
    • pca and samples correlation plots
    • Fingerprint and coverage plots

  • Signal
    • Bam files
    • bigwig files
    • MACS2 peaks

  • Analysis
    • gene_ontologies
    • Motifs detection

Further analysis

Custom plots and analysis can be provided within the framework of a collaboration. This includes but is not limited to:

  • Heatmaps and clustering: K-mean, hierarchical, custom feature based, supervised clustering.
  • Advanced motif analysis with ensembl methods.
  • Gene ontologies with ChIPEnrich, clusterProfiler.
  • Graphical analysis: Boxplots, violin plots, barplots, piecharts, etc.
  • Differential binding analysis.
  • Hidden markov modelling: Typically used to describe chromatin states.
  • Linear regression analysis.
  • Sub-group definition by venn diagram analysis.
  • Refined peak calling with hiddenDomains, SICER, SPP, etc.
  • Tissue specificity analysis.

RNA-Seq

The processing of single- and paired-end data is currently provided through a galaxy workflow as requested by biologists.

You receive the following from us:

  • QC (quality control)
    • A multiqc report containing different metrics: single-end and paired-end.
    • Counts table: Raw and TPM Normalized counts from Salmon (transcriptome based) and Featurecounts (reference genome based)
    • Correlation and PCA: Using no, CPM, RPKM and TPM normalization.
  • Signal
    • Bam files
    • bigwig files: Using no, CPM, RPKM and TPM normalization.

Further analysis

Custom plots and analysis can be provided within the framework of a collaboration. This includes but is not limited to:

  • Differential expression analysis with DEseq2 and/or EdgeR
  • Heatmaps and clustering of differentially expressed genes.
  • Gene ontologies of differentially expressed genes.
  • Graphical analysis: Boxplots, violin plots, barplots, piecharts, etc.

ATAC-Seq

In construction