RNA sequencing

RNA-seq Data Analysis

TACGenomics have extensive experience in RNA sequencing field and provide the most comprehensive data analysis from Quality Control to Data Visualization.

RNAseq Analysis Demo Report

For your project, firstly we will give a detailed QC report of data from raw fastq file to aligned BAM file. Our QC analysis will tell you whether your data is suitable for some specific analysis such as gene fusion or splicing and whether your sequencing depth is enough for expression analysis. Below figures are some examples from our sample project:

a.The plot of gene body coverage. A good RNA_seq data should have a high uniformity of the reads coverage in the gene body region. Significant 3’bias of the coverage always indicated a degradation of the RNA samples. An even coverage of gene body is required for Gene fusion, splicing and mutation analysis, although it is OK for gene expression analysis.
b.The plot of reads saturation. Through the quality check of the reads saturation, we can know if the current sequencing depth was enough or not in terms of ability of detect expressed genes. If less than 50% genes was detected or the curve did not reach saturation, more reads for this sample may required.
c.The plot of PCA. PCA is an unsupervised analysis of large data sets such as expression level of the whole transcriptome. PCA help you to understand the relative relationship between each sample in 2 or 3-dimensional coordinate space. PCA can identify the outlier of the samples or the potential swap of the samples between groups.