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RNAseq Quality Control Process

Acquiring the raw RNAseq data

Upload RNAseq data

Demultiplexing of raw data

Checking the quality of raw data with FastQC

Trimming of low quality reads and adapter sequences

Checking the quality of processed data with FastQC

Comparing raw vs processed data quality metrics

Mapping reads to the reference genome using STAR

Generating an alignment summary statistics

Checking the distribution of mapped reads coverage

Evaluation of sequencing depth and saturation

Inferring transcript abundance using RSEM

Checking for transcriptome alignment warnings or errors

Conducting Principal Component Analysis (PCA) on normalized counts

Determining the differential gene expression using DESeq2

Approval: Bioinformatician Review of the resultant gene list

Validation of differential gene expression using quantitative PCR

Interpretation of qPCR results

Approval: Supervisor Review of final QC report

Acquiring the raw RNAseq data

In this task, you will acquire the raw RNAseq data needed for the analysis. The raw data is essential as it contains the initial information necessary for downstream analysis. Make sure to obtain the data from a reliable source that provides accurate and high-quality sequencing results. Have you identified the appropriate source to acquire the raw RNAseq data from?

Raw data source

1

Public database
2

Collaborator
3

In-house sequencing facility
4

Other

Upload RNAseq data

Now that you have acquired the raw RNAseq data, it's time to upload it for further processing. Uploading the data will allow you to access it in the subsequent steps of the workflow. Have you prepared the raw RNAseq data files for upload?

Upload type

1

Bulk upload
2

Individual file upload

Demultiplexing of raw data

In this task, you will demultiplex the raw data. Demultiplexing is the process of separating the sequencing reads belonging to different samples based on their unique barcodes or indexes. This step is crucial for downstream analysis as it allows you to assign each read to its corresponding sample. Have you ensured that each sample in the raw data has a unique barcode or index for demultiplexing?

Demultiplexing requirements

1

Barcode or index for each sample is present
2

Demultiplexing software is available
3

Reference mapping file for sample identification

Checking the quality of raw data with FastQC

To ensure the reliability of the raw RNAseq data, it is essential to assess its quality. FastQC is a widely used tool for quality control analysis of sequencing data. Running FastQC on the raw data will provide valuable information about the sequencing quality, including measures such as per-base sequence quality and GC content. Have you checked the quality of the raw RNAseq data using FastQC?

Raw data FastQC report