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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Curr Protoc. 2023 Feb;3(2):e664. doi: 10.1002/cpz1.664

A computational workflow for the analysis of 3’ Tag-Seq data

Akshay D Paropkari 1,2, Priyanka S Bapat 1,2, Suzanne S Sindi 3,*, Clarissa J Nobile 2,4,*
PMCID: PMC9930165  NIHMSID: NIHMS1864724  PMID: 36779816

Abstract

RNA-sequencing (RNA-seq) is a gold standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3’ Tag-Seq (also called TagSeq, 3′ Tag-RNA-Seq, and Quant-Seq 3′ mRNA-Seq) is one RNA-seq variation, where the 3’ end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy to use, and publicly available computational workflow to analyze 3’ Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC, aligned to the reference genome, and read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3’ Tag-Seq data. As such, transcript length-based normalizations are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements.

Keywords: 3’ Tag-Seq, RNA sequencing, differential gene expression, computational pipeline

INTRODUCTION

RNA sequencing (RNA-seq) is a widely used method to detect genome-wide changes in gene expression (Wang, Gerstein, & Snyder, 2009). It was first used in Saccharomyces cerevisiae to identify the gene expression patterns of all genes, exons, and their boundaries across the yeast genome (Nagalakshmi et al., 2008). For humans, as well as many model organisms such as yeast, fruit flies, and mice, RNA-seq has been instrumental in providing high resolution, functionally relevant, genome annotations (Cherry et al., 2012; Gnerre et al., 2011; International Human Genome Sequencing Consortium, 2004; Matthews et al., 2015). Briefly, the RNA-seq protocol begins with the isolation of total RNA, which is typically enriched/selected for polyadenylated (poly(A)) RNA or alternatively depleted for ribosomal RNA (rRNA) (Zhao, Zhang, Gamini, Zhang, & von Schack, 2018). After this step, double stranded complementary DNA (cDNA) is synthesized via reverse transcription from the RNA, resulting in cDNA libraries. The cDNA molecules are fragmented, and sequence adapters are added to the cDNA fragments. Then, the cDNA fragments are subject to high-throughput sequencing to generate short sequence reads, which are used for downstream analyses.

Whole transcript RNA-seq workflows produce data providing information on quantifying gene expression, novel transcripts, alternatively spliced genes, and allele specific expression (Wang et al., 2009). Although this information is valuable, oftentimes, the goal of many biological research projects is to simply identify changes in gene expression patterns between conditions of interest. Given this simplified goal, classical RNA-seq protocols are overly complex and more costly than is necessary towards obtaining genome-wide gene expression changes (Ma et al., 2019; Wang et al., 2009). To simplify classical RNA-seq protocols, recent advances in the field have provided alternative RNA-seq methods that are used today to address specific biological questions (Moll, Ante, Seitz, & Reda, 2014; Morrissy et al., 2011). One of these alternative methods is 3’ Tag-Seq (also called TagSeq, 3′ Tag-RNA-Seq, and Quant-Seq 3′ mRNA-Seq). In this method, cDNA libraries are reverse transcribed only from the 3’ end of the mRNA, resulting in a single copy of cDNA arising from each transcript (Ma et al., 2019; Moll et al., 2014; Torres, Metta, Ottenwälder, & Schlötterer, 2008). Compared to classical RNA-seq methods, 3’ Tag-Seq is simpler, quicker to perform, and less costly while providing sufficient sequencing depth for differential gene expression analysis (Ma et al., 2019). These benefits make 3’ Tag-Seq an ideal choice for researchers whose goal is to identify changes in patterns of gene expression between two or more conditions.

Analysis of sequencing datasets from classical RNA-seq data was complex when this technology was first introduced (Wang et al., 2009). Even today, analysis of RNA-seq data can be challenging because it is highly dependent on the experimental design used to create the sequencing libraries (Conesa et al., 2016). Consequently, there is no “one size fits all” workflow for the analysis of RNA-seq output reads. Here, we present a simple, easy to use, and publicly available computational workflow to analyze 3’ Tag-Seq data, where the user can use default analysis parameters or can adjust parameters to fit their unique experimental design considerations. The workflow begins by trimming sequence adapters from raw FASTQ files (Cock, Fields, Goto, Heuer, & Rice, 2010; Conesa et al., 2016). The trimmed sequence reads are checked for quality using FastQC, aligned to the reference genome, and read counts are obtained using STAR (Dobin et al., 2013). Differential gene expression analysis is then performed using DESeq2, based on differential analysis of gene count data (Love, Huber, & Anders, 2014). The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This workflow is well-suited for users with minimal computational expertise.

BASIC PROTOCOL 1: Running the 3’ Tag-Seq workflow

In the following section, we describe in detail our 3’ Tag-Seq analysis workflow. Figure 1 provides a summary of the computational workflow. Briefly the pipeline begins with the processing of raw RNA-seq FASTQ files and ends with a table output of differential gene expression (Love et al., 2014).

Figure 1: 3’ Tag-Seq analysis workflow.

Figure 1:

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(i) Adapters added to raw RNA-seq reads are trimmed using BBDuk. (ii) a quality control (QC) report is generated for trimmed reads using FastQC. (iii) Reads passing the QC check are aligned to the reference genome using STAR and a gene count table is created. (iv) The gene count table is used to run differential gene expression analysis using DESeq2, and the DESeq2 output is saved as a table to a file for downstream usage.

Necessary Resources:

Hardware

An internet connected machine running Linux 64-bit Ubuntu version 20.04.1 with at least 32 GB RAM. The number of threads can be provided by users, but 16 threads is recommended.

Software

The workflow uses the Conda command line tool environment to install all required software and tools. Conda software can be accessed at https://docs.conda.io/en/latest/miniconda.html. The workflow is saved in a bash script file called pipeline.sh. The source code and documentation can be found on GitHub at https://github.com/akshayparopkari/RNAseq/wiki. The Conda environment file is provided in Supplemental file 1.

Other Requirements

  • Access to a computational cluster and login information

  • Basic knowledge of Unix

  • Raw FASTQ sequencing data

  • Sample metadata

Downloading the RNA-seq workflow on a local machine

On Linux and MacOS, users can use the built-in Terminal application, and on Windows, users can download and use Git Bash (https://gitforwindows.org/).

  • 1
    Navigate to the desired directory to download this folder on your machine. 
    git clone https://github.com/akshayparopkari/RNAseq.git

    Note: Alternatively, users can click on the green “Code” button on the GitHub page https://github.com/akshayparopkari/RNAseq and click the “Download ZIP” option. Users can then unzip the downloaded folder and save it to a relevant location on their local machines.

  • 2
    Make script files executable. [*Copyeditor: I adjusted the step numbers to run consecutively through the protocol. Please ask the authors if this is OK.] 
    cd RNAseq/
chmod u+x pipeline.sh
chmod u+x format_counts_table.py

Loading the Conda virtual environment

Conda enables virtual environments that contain the required software packages/libraries to be installed and set up. In this instance, the RNA-seq Conda environment contains the BBMap suite, STAR alignment software, and FASTQC tool (Bushnell, 2014; Dobin et al., 2013). Additionally, required Python and R libraries and their dependencies are also installed.

  • 3
    Create Conda environment using Supplemental File 1 
    conda env create -f Supplemental_file_1.yml -n RNAseq
    Note: Users only need to create the environment once. For subsequent analysis, users can activate the environment to run the analysis using the following command: 
    conda activate RNAseq

Creating an input data folder

  • 4
    The main script of 3’ Tag-Seq is the pipeline.sh file. This single bash script contains all the preprocessing steps: QC filtering with BBDuk, generating QC summaries with FastQC, and alignment and gene counting with STAR. The pipeline.sh script takes in a single input, which is a folder/directory containing:
    1. all raw FASTQ sequence files AND
    2. the sample metadata Excel file

The raw FASTQ sequence files may either be compressed (using gzip) or uncompressed. The file names must start with the sample ID, followed by the underscore and the rest of the file name. For example, projectname_date_L001.fastq.gz should be named sampleid_projectname_date_L001.fastq.gz. The first part of the file name before the first underscore dictates the sample the script is processing. The sample metadata file contains all metadata associated with the input samples including sample ID, genotype, condition, treatment, time, etc. For this repository, the sample metadata file must contain at least two columns - SampleID and Condition. Table 1 is an example of a sample metadata file, where the first two columns SampleID and Condition are required, and the third column FASTQ_file and beyond is optional, but highly recommended. A comprehensive metadata file also enables convenient sample submission to a sequence read archive (SRA), once your manuscript is published.

Table 1:

Sample metadata file. Users can use this file as a template to generate their metadata file.

SampleID Condition FASTQ_file Other_Sample_Info
Sample1A WT Sample1A_S8_L001_R1_001.fastq.gz …
Sample1B Mutant Sample1B_S8_L001_R1_001.fastq.gz …
Sample2A WT Sample2A_S8_L001_R1_001.fastq.gz …
Sample2B Mutant Sample2B_S8_L001_R1_001.fastq.gz …
Sample3A WT Sample3A_S8_L001_R1_001.fastq.gz …
Sample3B Mutant Sample3B_S8_L001_R1_001.fastq.gz …
… … … …
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NOTE: The input directory must contain raw FASTQ files and a sample metadata Excel file. Users may implement a user-defined project structure to organize their RNA-seq data. Please see Cookiecutter Data Science project (https://cookiecutter.readthedocs.io/en/1.7.2/) for ideas on how to best organize computational data.

Transferring data to/from a cloud computing resource to a local machine via command line

  • 5
    Below is a common usage of the secure copy scp function, which is one of the commands used to transfer files to/from a cloud computing resource. The other command is a secure file transfer protocol sftp. Please refer to the cloud computing resource wiki for detailed instructions on sftp function. 
    scp FROM TO
    where FROM is the source location and TO is the destination location.

Third party GUI apps

Users can also use third party clients to transfer files to/from a local computer. FileZilla (https://filezilla-project.org/) for Linux and Windows or Cyberduck (https://cyberduck.io/download) for MacOS and Windows are alternatives to using scp or sftp to transfer files with drag and drop.

Running the RNA-seq pipeline

Users must activate the RNA-seq Conda environment before attempting to execute the pipeline.

  • 6
    Run the RNA-seq pipeline 
    INPUTFOLDER=“path/to/your/input/folder” # enter your data folder with FASTQ files here
bash pipeline.sh “$INPUTFOLDER” <num_og_threads_to_use> > “$INPUTFOLDER”/preprocess.log

Output files

pipeline.sh creates multiple output files, which can be useful to gain insights into specific samples to address any discrepancy in the data. The three important files to check are:

  1. gene_raw_counts.txt, which is a tab-separated file of raw gene counts for all samples with gene names as the rows and samples as columns

  2. deseq2_lfc.txt, which is a tab-separated file from the DESeq2 analysis

  3. MA_plot.pdf, which is a .pdf file depicting volcano plots of log fold changes against mean gene expression

Additional information about other output files:

  1. All “_trimmed.fastq” ending files are trimmed sequences from BBmap and are saved in the trim_log directory

  2. All “.bam” ending files are alignment files generated by STAR and are saved in the STAR_log directory

  3. All “ReadsPerGene.out.tab” ending files are gene count files for each sample generated by STAR and are saved in the STAR_log directory

All “Log.out”, “Log.final.out”, and “Log.progress.out” ending files are intermediary alignment files generated by STAR and are saved in the STAR_log directory.

Visualizing overlaps in multiple experimental conditions

  • 7

    Use the overlap_upsetR.R script to visualize overlaps in genes for multiple experimental conditions. The overlap is represented as an UpSet plot (Lex, Gehlenborg, Strobelt, Vuillemot, & Pfister, 2014). UpSet plots are an extension of Venn diagrams and are useful when there are more than three categories/sets of conditions/samples to consider. The overlap_upsetR.R takes one input – either “up” or “down” – to calculate overlap between various samples/conditions. Users need to supply an input directory in the code on line 38, and run the following command to get the output UpSet plot:

    # To visualize genes upregulated in multiple conditions/samples 
    overlap_upsetR.R up
    # To visualize genes downregulated in multiple conditions/samples 
    overlap_upsetR.R down

Figure 2 is an example of an UpSet plot output of Candida albicans RNA-seq data showing upregulated genes overlapping various conditions.

Figure 2: Example UpSet plot output.

Figure 2:

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UpSet plot output of example Candida albicans RNA-seq data for the wildtype (WT) strain and three knockout (KO) mutant strains – NRG1_KO, RFG1_KO and ZCF8_KO. The bar plot on the top represents the overlap of upregulated genes in each condition. The horizontal bar on the left represents the number of significantly upregulated genes in each condition. The black circles and lines at the bottom show the categories for which overlap is calculated as indicated in the bar chart on the top. For example, the fourth bar on the top represents 199 significantly upregulated genes observed in the WT and NRG1-KO conditions. The blue bar highlights the overlap of significantly upregulated genes observed in the three KO conditions.

SUPPORT PROTOCOL 1: Generating genome indices

During the alignment step, STAR utilizes genome index files for mapping sequenced reads to a reference genome. This protocol describes how to generate genome indices for the Candida albicans Assembly 21 genome as an example. These steps can be used to generate genome indices for your reference genome of choice.

Necessary Resources:

Hardware

The hardware requirements are the same as for Basic Protocol 1.

Software

The workflow uses the Conda command line tool environment to install all required software and tools. Conda software can be accessed at https://docs.conda.io/en/latest/miniconda.html. The workflow is saved in a bash script file called pipeline.sh. The source code and documentation can be found on GitHub at https://github.com/akshayparopkari/RNAseq/wiki. The Conda environment file is provided in Supplemental file 1.

Other Requirements

  1. Access to a computational cluster and login information

  2. Basic knowledge of Unix

  3. Raw FASTQ sequencing data

  1. In your home folder, download Candida albicans chromosomal sequences from the Candida Genome Database - http://www.candidagenome.org/ (Skrzypek et al., 2017). 
    wget http://www.candidagenome.org/download/sequence/C_albicans_SC5314/Assembly21/current/C_albicans_SC5314_A21_current_chromosomes.fasta.gz
gunzip C_albicans_SC5314_A21_current_chromosomes.fasta.gz
  2. Download the Candida albicans genome annotation GTF file from the Candida Genome Database. 
    wget http://www.candidagenome.org/download/gff/C_albicans_SC5314/Assembly21/C_albicans_SC5314_A21_current_features.gtf
gunzip C_albicans_SC5314_A21_current_features.gtf
  3. Activate the 3’ Tag-Seq Conda environment. 
    module load anaconda3
source activate RNA-seq
  4. Generate STAR genomes. 
    mkdir ca_genome/
cd ca_genome/
STAR --runMode genomeGenerate --genomeDir ./ --genomeFastaFiles ~/ C_albicans_SC5314_A21_current_chromosomes.fasta

  5. STAR will generate output index files in the ca_genome folder.

GUIDELINES FOR UNDERSTANDING RESULTS

The outputs of this 3-Tag-Seq pipeline are MA plots, tables of differentially expressed genes, and UpSet plots. The output files are saved in the input folder supplied with the script. The DGE output table consists of six numerical columns – baseMean, log2FoldChange, lfcSE, stat, pvalue, and padj. The details of these columns are explained in Analyzing RNA-seq data with DESeq2 - https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html. To obtain a list of genes that are differentially regulated between the two conditions, users can focus on the log2FoldChange and padj columns. The log2FoldChange indicates the magnitude of change in expression values between the two experimental conditions and the padj value provides statistical significance towards the calculated magnitude change. The availability of these output files including the MA-plot indicates a successful run of this pipeline.

COMMENTARY

Background Information:

We present a simple and user-friendly workflow to analyze 3’ Tag-Seq experimental data. This workflow allows the user to determine the differentially expressed genes in the experimental conditions of interest. This workflow also includes a useful UpSet plot script to allow users to further analyze their differential gene expression data.

Given that the experimental methodology behind 3’ Tag-Seq is focused on the 3’-end of the transcript, information related to splicing events is not captured, and thus is a limitation of this methodology.

We note that this workflow is distinct from other pipelines that analyze traditional RNA-seq experimental data in terms of gene counting. In traditional RNA-seq analyses, gene counts are normalized by the length of the transcript or fragment to obtain comparable count values across genes and across sampling conditions. For traditional RNA-seq experiments, transcripts are fragmented, and reverse transcribed into cDNA; therefore, longer transcripts generate more cDNA, thus necessitating length-based normalization procedures during analysis. For 3’ Tag-Seq experiments, only the 3’-end of the transcript is reverse transcribed, eliminating this need for length-based normalizations during analysis. This advantage of 3’ Tag-Seq facilitates an overall simpler analysis workflow compared to traditional RNA-seq in obtaining accurate read count estimates from biological samples.

Critical Parameters:

The script only takes in a single input – a folder with raw FastQ files. The FastQ files can be either compressed or uncompressed.

Troubleshooting:

See Table 2 for troubleshooting tips.

Table 2:

Sources and Solutions to Potential Issues

Issue Possible Cause Solution
No gene count table generated by STAR STAR was unable to locate the reference genome Follow steps listed in STAR documentation under Section 2 - https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf
No output from DESEq2 analysis Metadata file was formatted incorrectly Follow steps listed in GitHub Wiki - https://github.com/akshayparopkari/RNAseq/wiki/4.-Creating-input-data-folder
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Supplementary Material

supinfo

ACKNOWLEDGEMENTS:

We thank all past and present members of the Nobile and Sindi laboratories for feedback on the manuscript. We are especially grateful to Deepika Gunasekaran for constructive comments and support on the computational workflow as well as to the anonymous reviewers for providing feedback that significantly improved the utility of the workflow. This work was supported by the National Institutes of Health (NIH) National Institute of General Medical Sciences (NIGMS) award number R35GM124594 and by the Kamangar family in the form of an endowed chair to C.J.N. The content is the sole responsibility of the authors and does not represent the views of the funders. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

Footnotes

CONFLICT OF INTEREST STATEMENT:

Clarissa J. Nobile is a cofounder of BioSynesis, Inc., a company developing diagnostics and therapeutics for biofilm infections. BioSynesis, Inc. was not involved in the content of this manuscript.

DATA AVAILABILITY STATEMENT:

The source files that support the protocol are freely available from the GitHub website (https://github.com/akshayparopkari/RNAseq). The example RNA-seq data used to generate Figure 2 can be obtained from the corresponding authors upon request. The test dataset for the TF028 (rme1 mutant) strain versus the wildtype strain can be accessed at NCBI GEO (https://www.ncbi.nlm.nih.gov/gds) under accession number GSE200778.

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INTERNET RESOURCES:

  1. Candida albicans assembly 21 chromosomal sequence FASTA file - http://www.candidagenome.org/download/sequence/C_albicans_SC5314/Assembly21/current/C_albicans_SC5314_A21_current_chromosomes.fasta.gz
  2. Candida albicans assembly 21 chromosomal features GTF file - http://www.candidagenome.org/download/gff/C_albicans_SC5314/Assembly21/C_albicans_SC5314_A21_current_features.gtf
  3. FileZilla software - https://filezilla-project.org/
  4. CyberDuck software - https://cyberduck.io/download

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supinfo
NIHMS1864724-supplement-supinfo.yml (5.2KB, yml)

Data Availability Statement

The source files that support the protocol are freely available from the GitHub website (https://github.com/akshayparopkari/RNAseq). The example RNA-seq data used to generate Figure 2 can be obtained from the corresponding authors upon request. The test dataset for the TF028 (rme1 mutant) strain versus the wildtype strain can be accessed at NCBI GEO (https://www.ncbi.nlm.nih.gov/gds) under accession number GSE200778.

RESOURCES