BaseClear offers complete bioinformatics solutions for transcriptome analysis (RNA-Seq projects), including expression analysis and isoform detection. All comparisons and tests include a P-value analysis to ensure the statistical significance of your results can be evaluated. Of course, tailored strategies are also offered. Our managers are happy to discuss the ideal workflow that best fits your needs and budget. Our main drive is to make sure that your research questions are answered.

Expression analysis

Our bioinformatics department has developed state-of-the-art RNA-Seq analysis pipelines which follow a reference-based or de novo approach. The ultimate goal is to provide our customers with the best possible answers to their transcriptome analysis research questions. To accomplish this task we generally use the Illumina HiSeq sequencing platform. If no annotated reference genome is available, we are able to generate high-quality de novo transcriptome assemblies with Trinity (Grabherr et al., 2011), and subsequently annotate transcripts using our in-house annotation pipeline. Based on an annotated reference genome and mRNA sequencing reads, gene expression levels are calculated using a combination of Bowtie2/Tophat2 and cufflinks based on the work of Trapnell et al (2013).

Quality control and statistical interpretation

Our standard service also includes a number of quality control steps based on the overall distribution of gene expression in the samples. These steps result in publication-ready figures such as a principle component analysis (PCA) scatter plot and a hierarchical clustering figure to inspect inter- and intra-group variability.

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Scatter and volcano plots

Optionally scatter and volcano plots can be created for specific genes of interest. In addition, our standard service includes a table with fragments per kilo base of transcript per million fragments mapped (FPKM) values. On top of this, differentially expressed genes are reliably determined through a robust statistical analysis which includes the assignment of P-values. The exact approach is defined by the type of experiment used (e.g. comparison between two or multiple groups or the presence/absence of biological replicates).

Delivered output

  • RNA-Seq sorted alignment file (BAM) and alignment index file (BAI).
  • Expression analysis table containing the (normalized) expression values between the samples and corresponding P-values.
  • Quality control figures (principle component analysis plot and hierarchical clustering figure).
  • RNA-Seq analysis report containing a summary of the results and quality measures.
  • Optional: scatterplots and volcano plots for specific genes of interest

Isoform detection

For eukaryotic organisms the standard expression analysis includes isoform detection. A complementary table containing the FPKM values of the different isoforms is delivered. In addition, custom options are available to generate figures for specific genes of interest, among which bar plots and/or create Sashimi plots (Katz et. al, 2014) that display the isoform structure.

Delivered output:

  • Expression analysis table containing the (normalized) expression values between the samples and corresponding P-values.
  • Optional: bar plots and sashimi plots for genes of interest.

Functional analysis

Using Gene Orthology (GO) we investigate functional groups between samples, by looking for a significant over/underestimation of certain GO terms between samples/conditions. Standard a table listing the significant GO-terms will be generated. These significant GO-terms can be visualized creating a heat map of the relationship between genes and the GO terms. Hierarchical clustering, genes are grouped together based on their expression patterns, thus clusters are likely to contain sets of co-regulated or functionally related genes. (Wencke et. al, 2015).

Delivered output:

  • GO term enrichment table.
  • Optional: GO term Heat maps and Hierarchical clustering

De novo transcriptome analysis

If a reference genome is not available we can perform a de novo transcriptome analysis using Trinity software (Grabherr et. al, 2011) for the assembly and an in-house annotation pipeline to assign a functional annotation to the assembled transcripts.

Delivered output:

  • Assembled transcriptome scaffold sequences in FastA format
  • GenBank and GFF annotation formats.
  • Extended annotation report containing a summary of the assembly results and quality measures.
  • Optionally: for annotation table containing full annotation for predicted coding sequence regions.

Meet Walter Pirovano!

Walter Pirovano (PhD) is the Director of the Bioinformatics department and a member of the BaseClear Management Team. He supervises our team of excellent bioinformaticians with expertise in the field of next-gen analysis and (far) beyond.

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