Visualisations

No result counts, if it is not presented in the best possible way. Therefore we are supplying you with high-resolution, publication ready figures. Additionally we always generate a pdf version of your figures. This has two advantages. First we or you can import the pdf in any vector based graphic program. Widely used examples for such programs would be Adobe Illustrator or the open-source alternative Inkscape. Here we can make easy adjustments for example in the alignment of text elements or add or remove distinct elements without corrupting the actual graphics. Another easy manipulation would be changing the background colour for seamless incorporation into your presentations. Secondly, one can export the whole or parts of the figure to an image, controlling the resolution of the output. This, for example if you want to focus on just a part of the figure or you need even higher resolution, for example for a poster. The result will be always stunning, without the typical artefacts you might know from other sources.
Our preferred image format is png (Portable Network Graphics). The pros are double. First the png image is lossless compressed, making them reasonable sized without any loss in quality. This is in contrary to jpg compression, which always goes along with quality impairment. Secondly png supports transparent backgrounds, which can be handy when making posters or presentations. However, if you prefer another image format, we are happy to supply your selection as well.
Below we show some example images we made for this page. The resolution is adapted to be shown on the web without artefacts. Yours will be even nicer.
We also make videos! We found it a creative way to show an overview over results of an experiment. One example would be a video of all positively called peaks of a ChIP-Seq experiment. Have a look below the image gallery where we show an example.

Gallery

  • PLAY VIDEO
    RNA-Seq merged read tracks
    Read tracks from 4 ER-negative and 4 ER-positive Cell Lines had been merged into a single track, respectively. Alternative Splicing of the two isoforms can be seen
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    Dexseq plot CD47
    Alternative splicing on exon level between estrogen receptor positive and negative cell lines. Significant exons are coloured in orange
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    Post alignment Quality Control
    Table of alignment efficiency using STAR aligner
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    RNA-Seq Exon Skipping
    Breast cancer cell line MPE600 shows skipping of exon 9 (blue box) of the E-Cadherin gene
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    CD47 alternative splicing
    Isoform proportion plot for the gene CD47. Clear alternative isoform usage can be seen between ER-negative (orange) and ER-positive (blue) cell lines
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    Meta result from RNA-Seq experiment
    - Venn diagram -
    A meta-analysis of different statistical tests for differential expression in RNA-Seq data using Metaseq. Venn diagram shows the overlap between significant called genes from limma/voom, DESeq, edgeR and NBPSeq
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    Quality control of alignment and mapping to transcripts
    Visualization of the percentage of read overlap with transcripts derived from ENSEMBL
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    ChIP-Seq: 100 peaks
    "Video" from 100 significant called peaks of one ChIP experiment. Reads from ChIP sample are labelled in orange, reads from mock sample are labelled in blue
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    CD47 alternative splicing: Pie Chart
    Expression result of two different isoforms from the gene CD47 (coloured orange and blue, respectively) represented by a Pie chart visualization
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    ChIP-Seq transcription factor binding site analysis
    Result from a de-novo detection of transcription factor binding site motifs derived from the sequences covered by significant peaks
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    RNA-Seq Dot plot
    Dot plot showing the expression values for one significant regulated gene (MMP14) in breast cancer cell lines
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    Fusion transcript analysis: Circos Plot
    Cutout of a circos plot showing fusion events of the breast cancer cell line BT474
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    FastQC quality control of paired end raw reads (fastq files)
    Summary visualization of FastQC quality output from fastq from (RNA-Seq)
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    DNA-Seq: Genome Plot showing a somatic mutation
    Detection of somatic mutation in a cancer normal sample pair. Area of somatic mutation is boxed in green.
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    Micro-RNA-Seq: hierarchical cluster
    Hierarchical cluster of signficiant regulated micro RNAs
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    Chip-Seq plot: Distance to Transcriptional Start Site
    Histogram of peak distances from TSS
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    RNA-Seq: Bargraph of expression values for CD47
    Alternative splicing of two isoforms of CD47 represented by a bargraph
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    Chip-Seq: Pie chart loacation of peaks in the genome
    Shown is the percentage of the overlap of significant ChIP-Seq peaks with information from the ENSEMBL transcript database
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    ChIP-Seq: One representative ChIP peak
    Intronic chIP peak in relation to the transcript model (upper panel). In the lower panel background read alignment from the mock control can be observed
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    Hierarchical Cluster of genes from one distinct Gene Ontology Category
    Plotted are normalized expression values for genes associated with "regulation of smooth muscle cell proliferation" according to GO ontolgy
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    Chip-Seq: Gene Ontology (GO) over-representation
    Significant ChIP peaks were associated with their targeted genes in the genome and an analysis of enriched GO categories was performed
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    Read tracks RNA-Seq CD47
    Read track plot for CD47 showing alternative splicing. Blue (ER-positive) cell lines are showing two additional peaks representing two exons of the second isoform.
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    Single Cell Sequencing: Quality control of raw reads (fastq files)
    In single cell RNA-Seq analysis we are dealing with hundreds, if not thousands of single samples. Provided here is a screenshot from an overview PDF for one quality pIn single cell RNA-Seq analysis we are dealing with hundreds, if not thousands of single samples. Provided here is a screenshot from an overview PDF for one quality parameter (phred score per position)arameter
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    Single Cell RNA-Seq analysis. Pseudo-time representation of the expression values for the genes CDK1, MEF2C and MYH3
    Please refer to the Single Cell Analysis section in our Portfolio for detailed explanation
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    Single Cell RNA-Seq analysis
    PCA Dot plot from the expression values of the gene CDK1. Please refer to the Single Cell Analysis section in our Portfolio for detailed explanation