RNA-Seq Report

2 Background

2.1 Experimental Process

Please find below the figures explaining the experimental process of RNA-Seq. RNA-Seq experimental process can be done using one of the following four options.

2.1.1 Poly-A (Standard Input)2.1.2 Poly-A (Low Input)2.1.3 rRNA depletion (Standard Input)2.1.4 rRNA depletion (Low Input)

The Poly-A selection method requires high quality RNA (RIN >7) which is suitable for studying gene expression. Note that non-coding RNA will not be included in this method. To understand the process, please refer to the picture below.

Designed for low-input or single-cell samples, the SMART-Seq mRNA LP kit selectively captures polyadenylated RNA to generate full-length cDNA with high sensitivity. This approach minimizes starting material requirements while ensuring reliable transcript coverage across mRNA species. Refer to the diagram below for a detailed overview of the workflow.

Primarily utilized for low-quality or degraded RNA samples (RIN < 7), rRNA depletion RNA-seq is valuable for retaining both coding and non-coding transcripts in the resulting data. Common commercial kits for this approach include NEBNext and Illumina TruSeq. For a detailed overview of the workflow, please refer to the diagram below.

The Takara Bio kit will be used for this method. RNA input ( ~10 pg ) is very low. Both Poly-A and rRNA depletion can be applied in this kit. To understand the process, please refer to the picture below.

2.2 Bioinformatics Process

The picture below shows the bioinformatics pipeline used in the analysis of RNA-Seq data. FastQC (version v0.12.1) was applied to check the quality of raw reads. Trimmomatic (version v0.39) was applied to cut adaptors and trim low-quality bases with default setting. STAR Aligner version 2.7.10b was used to align the reads. Picard tools (version 3.0.0) was applied to mark duplicates of mapping. The StringTie version 1.3.0 was used to assemble the RNA-Seq alignments into potential transcripts. The featureCounts (version 2.0.6)/HTSeq-Count(version 2.0.3) was used to count mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations. The De-Seq2 (version 1.46.0) was used to do the differential analysis. The Gene Ontology Analysis was done using ClusterProfiler package in R. For more details about ClusterProfiler, Please check Reference 6. Gene Set Enrichment Analysis (GSEA) was run with clusterProfiler on MSigDB gene sets obtained through the msigdbr package (References 7-9).

3 Raw Data and Quality Control

3.1 RNA-Seq Raw Data Files

The Fastq files from the sequencer downloaded for further analysis. NOTE: If the sequencing library is prepared using the QuantSeq 3’ mRNA-Seq V2 Library Prep Kit FWD (lexogen.com), only the R1 Fastq file from each sample is required for gene expression analysis.

3.2 Quality Control

A FastQC (v0.12.1) report is generated to spot errors that originate either in the sequencer or in the library. It shows a quick summary of the modules run and a quick evaluation of whether the results are normal (pass), slightly abnormal (warn) or very unusual (fail). For more details about each module, please refer to the manual provided by Babraham bioinformatics. The plots below summarise all samples (aggregated by MultiQC); hover a line to identify the sample, click legend entries to hide/show samples.

4 Mapping Files

The fastq files were mapped using STAR Aligner version 2.7.10b. To know more about STAR aligner, please refer to Reference 1 below. BAM files are in the delivery folder 02.BamFiles/.

5 RNA Metrics

Picard (version 2.20.4) was used to collect metrics on the distribution of the bases within the transcripts. This tool calculates the total numbers and the fractions of nucleotides within specific genomic regions including untranslated regions (UTRs), introns, intergenic sequences (between discrete genes), and peptide-coding sequences (exons). This tool also determines the numbers of bases that pass quality filters specific to Illumina data (PF_BASES).

Full results: 03.rnaMetrics/, 03.rnaMetrics-multiqc_report.html

6 FPKM

FPKM stands for Fragments Per Kilobase of transcript per Million mapped reads. It is a normalised estimation of gene expression based on the RNA-seq data. FPKM are calculated from the number of reads that mapped to each particular gene sequence taking into account the gene length and the sequencing depth. In this project, StringTie version 1.3.0 was used to calculate FPKM values. StringTie used a network flow algorithm and an optional de novo assembly to assemble complex data sets into transcripts. To know more about StringTie, please refer to Reference 2 below in the References section.

7 Count

HTSeq is a Python library and it provides custom scripts to parse common data formats. The htseq-count tool counts for each gene how many aligned reads overlap its exons. This project used HTSeq-count (version 2.0.3).

8 Differential Analysis

The differential analysis can be divided into two parts: 1. Data exploration and QA on gene expression (sample distribution, p-value distribution, sample-to-sample distance heatmaps and principal component analysis) and 2. Quantification of differential expression and exploratory analysis (MA-plot and Volcano plot). The Differential Analysis was done using DESeq2 (version 1.46.0; 1 vs 1 sample comparison using 1.18.1). See reference 4.

Significance: pvalue < 0.05 and |log2FoldChange| > 1.0.

9 Functional Analysis

The functional analysis helps to understand the functions and utilities of the biological systems. Genes that are differentially expressed are further subject to functional analysis, done using the clusterProfiler package in R (reference 6): Gene Ontology (GO) classification and GO biological process / molecular function / cellular component enrichments. Enrichment is run separately on all significant genes, up-regulated genes and down-regulated genes.

10 References

1. Alexander Dobin, Carrie A. Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, Thomas R. Gingeras, STAR: ultrafast universal RNA-seq aligner, Bioinformatics, Volume 29, Issue 1, January 2013, Pages 15-21.
2. Pertea, M., Pertea, G., Antonescu, C. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33, 290-295 (2015).
3. Lever, J., Krzywinski, M. & Altman, N. Principal component analysis. Nat Methods 14, 641-642 (2017).
4. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).
5. Yang Liao, Gordon K. Smyth, Wei Shi, featureCounts: an efficient general purpose program for assigning sequence reads to genomic features, Bioinformatics, Volume 30, Issue 7, 1 April 2014, Pages 923-930.
6. Yu, G., Wang, L. G., Han, Y., & He, Q. Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology, 16(5), 284-287.
7. Subramanian, A., Tamayo, P., Mootha, V. K., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43), 15545-15550 (2005).
8. Liberzon, A., Birger, C., Thorvaldsdottir, H., et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1(6), 417-425 (2015); msigdbr: MSigDB gene sets for multiple organisms in a tidy data format (Dolgalev, I., R package).
9. Korotkevich, G., Sukhov, V., Sergushichev, A. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012.