Differential gene expression analysis
In projects with differential gene expression analysis, we provide you with several data analysis plots.
BaseClear offers a complete bioinformatics workflow for both prokaryotic and eukaryotic RNA-Seq projects. We use highly cited, continuously supported, and open-source computational tools for read quality control, reference alignment, and (differential) gene expression analysis. Our pipeline has been thoroughly tested using both publicly available RNA-Seq datasets as well as with those that we have generated in-house. In addition, we support you with project-based downstream analyses that may require a tailored approach.
Paired-end reads from the Illumina platform are quality controlled using Trim Galore (Krueger, 2012). Next, QC’d reads are mapped to the reference genome using the STAR RNA-Seq aligner (Dobin et al., 2013). Based on alignments, gene counts are estimated by featureCounts (Liao et al., 2014). In addition to the alignments (BAM files), results per sample are provided as both raw and TPM-normalised counts.
In projects that involve samples from different biological conditions, statistical analyses can be used to identify quantitative changes in gene expression between the different conditions. We perform this analysis using the DESeq2 framework (Love et al., 2014). The main output is a table that contains the average expression, fold-change, and associated statistics such as the P and corrected P values for each gene.
In projects with differential gene expression analysis, we provide you with several data analysis plots.
The PCA plot gives insight into how samples are associated based on their gene expression.
The MA-plot gives an overview of the differential expression versus the average expression levels of genes.
The volcano plot shows the relationship between the statistical significance (P value) and fold-change of gene expression. This allows you to see whether significantly differentially expressed genes also show large differences in expression.
Our main goal is helping you answer your research question as completely as possible. When results from individual gene expression changes become less straightforward to interpret, this may require the design and application of a tailored approach. In some project this is the simple inclusion of an additional functional database (e.g. eggNOG) in the analysis using which the genes are functionally annotated. In others, it may require a more complex approach that statistical analyses are performed not at gene level but at pathway level. Therefore, in addition to the above-mentioned framework of (differential) gene expression analysis, we are happy to help with you with more downstream analyses, where examples include (but are not limited to) clustering and heatmap generation, gene-set and Gene Ontology enrichment analysis, and pathway visualisation.
Figures: Downstream analyses where gene differences are summarised at higher functional levels can be performed based on the requirements of the project’s specific biological question.
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