RNA-Seq utilizes high-throughput sequencing technology to detect and measure the transcriptional readout (transcriptome) in a biological system. Analysis of RNA-Seq data allows expression quantification of different ribonucleic acids (RNAs) including mRNAs, miRNAs and lncRNAs. This data can be used to determine the differential gene expression patterns between different conditions, such as healthy vs disease. In addition, RNA-Seq can also be used to study RNAs structure or post-transcriptional splicing and predict its regulation mechanism in various conditions like complex diseases.
Can we identify which genes or transcripts are differentially expressed in liver cancer?
By using RNA-Seq, we can compare the expression levels of liver tumour tissues with that of adjacent normal tissues and detect the differentially expressed genes (DEGs). DEGs can be represented in a heat map (Figure 1.1) or in a volcano plot (Figure 1.2).
Figure 1.1: Heat map of the top 100 DEGs in liver tumour tissues compared to adjacent normal tissues. Differences in expression levels reflected by colour intensity, with red representing an upregulation and blue representing a downregulation.
Figure 2.1: Dot plot showing the top over-represented biological processes. The size of each dot signifies the number of genes involved in that particular biological process found to be differentially expressed between liver tumour tissues and adjacent normal tissues. Colour intensity of dots reflect levels of significance.
Figure 2.2: Dot plot showing the over-represented KEGG pathways that DEGs are involved in.
Figure 3.1: Different motifs significantly present in the upstream promoter of DEGs that are upregulated in liver tumour tissues as compared to adjacent normal tissues.
Figure 3.2: Different motifs significantly present in the upstream promoter of DEGs that are downregulated in liver tumour tissues as compared to adjacent normal tissues.
- FREE initial consulation includes discussion on project objectives and result expectation, analysis method proposal, timeline and cost estimation.
- Standard data analysis includes primary sequencing reads quality control to downstream reads quatification and functional profiling for up to 12 samples. Please refer to analysis catalog for detail analysis workflow and result description.
- Publication ready figures/plots includes customize processing of analysis result plots such as spliting by samples group/conditions, color selection, highligting specific components within the plot etc. Only applies to analysis result of samples in package analysis.
- Publication submission consulation includes compilation of publication ready analysis result, scientific writing review, scientific plots review or customization, consultation for reviewer queries for up to 5 hours in total.
- Data integration includes intergration of existing pre-processed data for up to two applications between RNA-Seq, ChIP-Seq, or ATAC-Seq.
- TCGA differential expression and correlation analysis profiles various cancer types and identifies the most associated genes and pathways related to gene of interest. Please refer to our TCGA Analysis page for more information.
- Customize bioinformatic analysis: USD 370 / hour
- Standard data analysis: USD 110 / sample
- Bioinformatics consulation: USD 110 / hour
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