First, use the RNA-seq process to be detected to analyze the DRS575016784630132736 data, and then the gene expression data file is input the detection process to obtain the accuracy of the process.

Background

There are many types of RNA-seq processes. Reproducibility of the output results of different processes is relatively low due to thousands of analysis software. Therefore, in order to measure the accuracy of different transcriptome data analysis processes, author carried out the sequencing quality control project (SEQC). Reference RNA samples were built in multiple laboratories and sequenced by Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms. Quantitative PCR were used in sample (qtPCR) data for comparison. We verify the accuracy of the RNA sequencing workflow by comparing the gene expression correlation between RNA-seq expression matrix and qtPCR results.

Methods

In this experiment, a mixed human tissue sample was used as the A sample. And human brain tissue was used as the B sample. Additional RNA control sequences were added for detection. Subsequently, samples A and B were mixed in a ratio of 3:1 and 1:3 to obtain samples C and D for mutation site analysis (see the figure below for details).

This process only extracts samples A and B from the Shenzhen laboratory as the test data set, and the corresponding qtPCR results as the standard data set.

Result

    We tested the RNA-seq analysis process on the platform, and got an accuracy rate of 0.85, and provided statistical results and correlation analysis diagrams.

Su, Zhenqiang, et al. "A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium." Nature biotechnology 32.9 (2014): 903-914.

v2.0: The process is automated, the code is uploaded to the CRS platform, the software is uploaded to the SRS platform, and the reference genome is uploaded to the DRS platform, making it possible to combine with other processes.

v1.0: Process construction, complete the basic process of data quality control, comparison, and differential gene calculation.