Development and validation of the PipeSeq program for RNA-seq data analysis in the Chlamydomonas reinhardtii as a model

RNA sequencing (RNA-seq) is a highly sensitive method for transcriptome analysis that allows simultaneous assessment of expression of thousands of genes and identification of expression patterns under various conditions. The existing variety of RNA-seq data formats, normalization methods, and approaches to statistical processing of results complicates comparison of data from different studies and reduces reproducibility of the analysis. This study presents an automated pipeline PipeSeq that combines standard steps of RNA-seq data processing: loading (SRA Toolkit), read alignment to the reference genome (HISAT2), transcript assembly (StringTie), transcript counting (FeatureCounts) and statistical analysis of differential gene expression under various experimental conditions (DESeq2). PipeSeq has a simple visual interface, supports multithreading, and generates ready-to-analyze gene expression heat maps, tables and graphs. The functionality of the pipeline is demonstrated on three sets of raw RNA-seq data from the green alga Chlamydomonas reinhardtii cells available in the NCBI SRA database. The data from these experiments were used to analyze the differential expression of C. reinhardtii genes encoding the GATA family transcription factors under different light cultivation conditions. The data obtained by in silico methods were verified by real-time reverse transcription polymerase chain reaction (RT-qPCR) for 12 GATA genes, which allowed us to hypothesize their functions and evaluate the correlation between the bulk (RNA-seq) and targeted (RT-qPCR) approaches. Our results showed that RNA-seq and RT-qPCR methods reveal similar directions of gene expression changes, but demonstrate differences in the effect size and sensitivity, which emphasizes the need for a combined use of the two approaches. Thus, the PipeSeq program is a tool for conducting a full cycle of bioinformatic analysis of RNA-seq data, additionally providing the opportunity to process RT-qPCR data and perform a comparative statistical analysis of the results obtained.

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