BaseClear offers complete bioinformatics solutions for transcriptome analysis (RNA-seq projects). Our managers are happy to discuss the ideal workflow that best fits your needs and budget. Standard solutions include expression analysis, alternative splicing detection and novel exon discovery.
Also we offer P-value analysis to ensure the statistical significance of your results. Of course tailored-made strategies are also offered. Our main drive is to make sure that your research questions are answered.
Our bioinformatics department has developed state-of-the-art expression analysis pipelines which follow a reference-based or De Novo approach (and in some cases a combination of both). 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 the CLC Genomics Workbench. The approach taken for sample normalization is based on (Mortazavi et al., 2008).
Graphical overview of an RNA-Seq alignment which is the basis for expression analysis. BaseClear customers receive, in addition to expression tables, also the corresponding BAM and BAI alignment files.
QUALITY CONTROL AND STATISTICAL INTERPRETATION
Our standard service also includes a number of quality control steps based on the overall distribution of expression analysis in the samples. These results in publication-ready figures such as a comparative box-plot, a principle component analysis (PCA) scatter plot and a hierarchical clustering figure to inspect inter- and intra-group variability.
Also we include a significance assessment of differentially expressed genes following a well-defined statistical analysis. In this manner differentially expressed genes are reliably determined through 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).
Two examples of RNA-Seq expression analysis quality control figures: in the left panel a box plot shows overall distribution of the expression values in the samples. Biological repeats have the share the same color; in the right panel a hierarchical clustering plot confirms that the samples meet the expected groups but also gives an overview of the inter- and intra-group variability.
EXON DISCOVERY, PSEUDOGENE DETECTION AND MUCH MORE!
Our team is well capable of performing a number of downstream/in-depth analysis, among which novel exon discovery and pseudogene detection. We are also happy to offer our expertise for variant calling, analysis of non-specific versus specific matches, and customized analysis of differential gene expression. For additional RNA-Seq related services please contact our specialists.
- Optionally: De Novo transcriptome assembly in FASTA format and annotation in GFF format.
- RNA-Seq alignment file (BAM) and sorted alignment index file (BAI).
- Expression analysis table containing the (normalized) expression values between the samples and corresponding P-values.
- Quality control figures (box plot, principle component analysis scatter plot, hierarchical clustering figure).
- RNA-Seq analysis report containing a summary of the results and quality measures.