Name: Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes (Video) | JoVE Journal
Uploaded: 2025-11-07T13:30:00.000+00:00
Duration: 5 min 7 s
Description: Learn automated RNA-seq quality control using R packages and Bioconductor tools. This computational pipeline enables biologists to assess mapping statistics, identify errors, and ensure reproducible transcriptomics analysis.

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5 minutes

We acknowledge technical assistance by the Bioinformatics Core Facility at the professorship of Systems Biology at JLU Giessen and provision of compute resources and general support by the BiGi service center (BMFB grant 031A533) within the de.NBI network. The work presented here was funded by the German Research Foundation (DFG) grant BE2547/24-1 to A.B., and we are also thankful for the support of the Justus Liebig University Giessen, Germany.

<ol><li>Kivivirta, K. I., Herbert, D., Roessner, C., De Folter, S., Marsch-Martinez, N., Becker, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcriptome+analysis+of+gynoecium+morphogenesis+uncovers+the+chronology+of+gene+regulatory+network+activity%5BTitle%5D)+AND+%22Plant+Physiol%22%5BJournal%5D)">Transcriptome analysis of gynoecium morphogenesis uncovers the chronology of gene regulatory network activity.</a> Plant Physiol. 185 (3), 1076-1090 (2021).</li>
<li>Hohenfeld, C. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparative+analysis+of+infected+cassava+root+transcriptomics+reveals+candidate+genes+for+root+rot+disease+resistance%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">Comparative analysis of infected cassava root transcriptomics reveals candidate genes for root rot disease resistance.</a> Sci Rep. 14 (1), 10587(2024).</li>
<li>Li, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Time-course+transcriptomic+information+unravels+the+mechanisms+of+improved+drought+tolerance+by+drought-priming+in+wheat%5BTitle%5D)+AND+%22J+Integr+Agric%22%5BJournal%5D)">Time-course transcriptomic information unravels the mechanisms of improved drought tolerance by drought-priming in wheat.</a> J Integr Agric. 24 (8), 2902-2919 (2024).</li>
<li>DeLuca, D. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA-SeQC%3A+RNA-seq+metrics+for+quality+control+and+process+optimization%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">RNA-SeQC: RNA-seq metrics for quality control and process optimization.</a> Bioinformatics. 28 (11), 1530-1532 (2012).</li>
<li>Wang, L., Wang, S., Li, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RSeQC%3A+Quality+control+of+RNA-seq+experiments%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">RSeQC: Quality control of RNA-seq experiments.</a> Bioinformatics. 28 (16), 2184-2185 (2012).</li>
<li>Zhou, Q., Su, X., Jing, G., Chen, S., Ning, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA-QC-chain%3A+Comprehensive+and+fast+quality+control+for+RNA-seq+data%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">RNA-QC-chain: Comprehensive and fast quality control for RNA-seq data.</a> BMC Genomics. 19 (1), 144(2018).</li>
<li>Deshpande, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA-seq+data+science%3A+From+raw+data+to+effective+interpretation%5BTitle%5D)+AND+%22Front+Genet%22%5BJournal%5D)">RNA-seq data science: From raw data to effective interpretation.</a> Front Genet. 14, 997383(2023).</li>
<li>Li, D., Zand, M. S., Dye, T. D., Goniewicz, M. L., Rahman, I., Xie, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+evaluation+of+RNA-seq+differential+analysis+methods%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">An evaluation of RNA-seq differential analysis methods.</a> PLoS One. 17 (9), e0264246(2022).</li>
<li>FastQC: A quality control tool for high throughput sequence data. , Babraham Bioinformatics. 
 <a target="_blank" href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc/">http://www.bioinformatics.babraham.ac.uk/projects/fastqc/</a> (2025).</li>
<li>Kassambara, A. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aKassambara%2C+A+%22fastqcr%3A+Quality+control+of+sequencing+data%22">fastqcr: Quality control of sequencing data</a>. , https://rpkgs.datanovia.com/fastqcr/index.html (2023).</li>
<li>Chen, S., Zhou, Y., Chen, Y., Gu, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((fastp%3A+An+ultra-fast+all-in-one+FASTQ+preprocessor%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">fastp: An ultra-fast all-in-one FASTQ preprocessor.</a> Bioinformatics. 34 (17), i884-i890 (2018).</li>
<li>Wang, W., Luo, J. D., Carroll, T. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aWang%2C+W+%22Rfastp%22">Rfastp</a>. , https://www.bioconductor.org/packages/release/bioc/html/Rfastp.html (2025).</li>
<li>Wickham, H. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aWickham%2C+H+%22ggplot2%3A+Elegant+graphics+for+data+analysis%22">ggplot2: Elegant graphics for data analysis</a>. , Springer-Verlag. New York. https://ggplot2.tidyverse.org (2016).</li>
<li>Shi, W. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aShi%2C+W+%22Rsubread%22">Rsubread</a>. , https://bioconductor.org/packages/release/bioc/html/Rsubread.html (2025).</li>
<li>Wagner, G. P., Kin, K., Lynch, V. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Measurement+of+mRNA+abundance+using+RNA-seq+data%3A+RPKM+measure+is+inconsistent+among+samples%5BTitle%5D)+AND+%22Theory+Biosci%22%5BJournal%5D)">Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples.</a> Theory Biosci. 131 (4), 281-285 (2012).</li>
<li>Liu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evaluating+the+impact+of+sequencing+depth+on+transcriptome+profiling+in+human+adipose%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Evaluating the impact of sequencing depth on transcriptome profiling in human adipose.</a> PLoS One. 8, e66883(2013).</li>
<li>Schurch, N. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((How+many+biological+replicates+are+needed+in+an+RNA-seq+experiment+and+which+differential+expression+tool+should+you+use%5BTitle%5D)+AND+%22RNA%22%5BJournal%5D)">How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use.</a> RNA. 22 (6), 839-851 (2016).</li>
<li>Liu, Y., Zhou, J., White, K. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA-seq+differential+expression+studies%3A+More+sequence+or+more+replication%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">RNA-seq differential expression studies: More sequence or more replication.</a> Bioinformatics. 30 (3), 301-304 (2014).</li>
<li>Deschamps-Francoeur, G., Simoneau, J., Scott, M. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Handling+multi-mapped+reads+in+RNA-seq%5BTitle%5D)+AND+%22Comput+Struct+Biotechnol+J%22%5BJournal%5D)">Handling multi-mapped reads in RNA-seq.</a> Comput Struct Biotechnol J. 18, 1569-1576 (2020).</li>
<li>Seppey, M., Manni, M., Zdobnov, E. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((BUSCO%3A+Assessing+genome+assembly+and+annotation+completeness%5BTitle%5D)+AND+%22Methods+Mol+Biol%22%5BJournal%5D)">BUSCO: Assessing genome assembly and annotation completeness.</a> Methods Mol Biol. 1962, 227-245 (2019).</li>
<li>Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., Kingsford, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Salmon+provides+fast+and+bias-aware+quantification+of+transcript+expression%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Salmon provides fast and bias-aware quantification of transcript expression.</a> Nat Methods. 14 (4), 417-419 (2017).</li>
</ol>

The authors have no conflicts of interest to declare.

The quality of differential gene expression analyses highly depends on two factors: the number of reads sequenced in each sample and replicate16 and the number of replicates per sample17,18. Here, we present the user-friendly Rup pipeline to determine the number of reads suitable for gene expression quantification in each replicate. Different metrics allow researchers to understand why replicates show low assigned read numbers and to identify problems in sample correlation. Although Rup was developed for quality assessment of plant RNA-seq samples, it is equally suitable for other eukaryotic organisms, requiring no further adjustments for non-plant samples (Supplemental File 1).

The first step of Rup determines the total read count before and after read trimming. The number of sequenced reads determines the number of detectable genes, and if the read number is too low, many differentially expressed genes will remain undetected. A large fraction of reads discarded during the quality trimming and filtering process might indicate RNA degradation, which could lead to an underestimation of expression for highly degraded genes. However, whether a sample can be used for downstream applications depends on the target organism and the aim of the study. For example, to capture the expression of most plant genes, in our experience, 30 million to 50 million reads are required, whereas for fungi, only 10 million might be sufficient. Thus, Rup will not define quality thresholds for exclusion of problematic samples but rather provide metrics to identify diverse problems that may be encountered.

The second step (mapping quality) evaluates the accuracy and reliability of read alignment to the reference genome, specifying their suitability for downstream analyses. Not all sequenced reads can be used for gene abundance computation; reads that fail to align to the genome or transcriptome or reads that map to multiple locations on the genome are ignored in most cases19 and do not contribute to the overall read counts. The results of the second step of the pipeline can be used to understand why reads are not used in the abundance computation. A high number of unmapped reads could indicate contamination during RNA extraction (e.g., with plant pathogens or herbivores) or an incomplete reference genome. Incomplete gene models of the reference genome may result in a high number of &#8220;no feature&#8221; reads. A large fraction of multi-mapped reads could be caused by a high number of rRNA genes in the library indicating insufficient rRNA removal during the sequencing library preparation process (in the case that the multi-mapped reads are truly derived from rRNAs, they can be analyzed further by providing an rRNA annotation file to the pipeline, which then automatically computes the number of possible rRNA reads).

The third, equally important quality metric is the correlation between replicates of the same sample. Generally, the correlation between replicates of the same sample should be higher than the correlation between replicates of different samples. A low correlation between replicates could indicate large biological variability between the replicates, high similarity between the samples/conditions/tissues, other batch effects, or even sample swapping or mislabeling. Rup computes pair-wise correlations between all samples and produces a clustered heatmap of the sample correlations. Additionally, a principal component analysis (PCA) can be computed on the samples to identify possible batch effects that need to be acknowledged in further downstream analyses. Although batch effects can be corrected in downstream analyses, it might be better to remove problematic samples if the measured difference is too high.

Input material has a direct impact on sequencing quality: tissue sampling, RNA extraction, and library preparation are critical steps for minimizing RNA-seq quality issues. Consistent conditions should be maintained, if possible, for example, by using growth chambers. Pest control measures should be carried out in a timely manner, as only healthy individuals should be selected for sampling. It is further advisable to collect samples on the same day and/or at the same time to reduce circadian transcriptome variation. Numerous RNA extraction kits are available, and selecting an appropriate kit for the target species can improve mRNA quality. The library preparation protocols should include steps for polyA+ RNA enrichment to minimize the rRNA fraction.

Rup can be used as an initial quality control step in differential gene expression analysis. This quality control is required for several critical reasons, as it ensures the quality of the input data (identifies low-quality replicates/samples, can set quality thresholds for read depth and mapping rates) and identifies technical issues such as sequencing errors, batch effects, or mislabeling. If the RNA input is of sufficient quality, Rup can assist by trimming low-quality reads or identifying mislabeled samples. However, Rup cannot compensate for poor input RNA quality. In cases of low-quality RNA or erroneous sequencing data, it may be necessary to re-collect samples, adjust the RNA extraction protocol, and/or repeat sequencing. The same applies to samples with a large fraction of unmapped reads that may have been caused by contamination. However, from our experience, not all RNA extractions can be simply repeated due to a lack of material availability. In this case, they are unsuited for some downstream applications: samples with low numbers of single-mapped reads should not be analyzed in differential gene expression analyses, but they still hold information for the analysis of transcript presence. In this case, the absence of transcripts in tissues/treatments/conditions cannot be considered for the analysis and quantification of transcript abundance.

Rup includes some limitations. For example, it does not directly test for RNA degradation as direct measurements of RNA integrity are required before library preparation and sequencing. However, a script to identify RNA degradation using the RSeQC modules geneBodyCoverage.py and tin.py is included in the repository of this pipeline. Furthermore, Rup does not test for GC content and transcript length bias. The reference genome size is currently limited to 4 Gb, and according to our experience, the read mapping module overestimates multi-mapped reads in polyploids, as exemplified in the Supplemental File in a comparison of read mapping to a haploid vs. the diploid genome versions of E. californica, a haploid genome is thus the preferred input for this pipeline. The Rup output quality largely depends on the quality of the reference genome and annotation, such that an incomplete or highly fragmented genome sequence could lead to an overestimation of unmapped reads. Moreover, incomplete gene model annotation leads to an overestimation of unassigned reads. Completeness and duplication of the genome sequence and the gene annotation can be inferred with tools like BUSCO20.

Rup is a stand-alone tool for all initial quality control steps essential in RNA-seq experiments. Unlike RSeQC and RNA-SeQC, preprocessing of the raw sequencing reads is not required. Sequencing quality analysis cannot be done with RNA-SeQC, and sample correlation heatmaps are not calculated by RSeQC and RNA-SeQC (Table 1). Further, the normalization output is in FPKM in RSeQC and output visualization is not implemented as a standard for all modules in RSeQC and RNA-SeQC. Thus, several quality control issues are not tested in the two alternative tools, which include low read number, high fraction of trimmed reads and the identification of replicate outliers. A comparison of Rup, RSeQC and RNA-SeQC is available in Supplemental File 1. Moreover, Rup can be used complementary to RNA-SeQC or RSeQC, for example, the sorted BAM files produced by this pipeline can be used as input for these tools.

Currently, Rup is optimized for a small number of samples, but the most time-consuming steps, like quality trimming and read mapping, can be precomputed, for example, on a computer cluster or a cloud infrastructure. For genomes larger than 4 Gb, this is mandatory, since the Rsubread aligner is limited to genomes smaller than 4 Gb. rRNA annotation is done with barrnap, which identifies the highly conserved rRNA genes. According to our experience, rRNA gene annotation does not require comprehensiveness, because even if some abnormal rRNA genes are missing, a gross overview of rRNA presence in the dataset is sufficient for dataset quality assessment. Future versions of Rup might switch to a different mapping method, like the pseudo-alignment tool salmon21, to decrease runtime. Moreover, more normalization and correction methods, like GC bias or length bias correction, could be added to Rup.

In summary, Rup provides essential information to ensure the reliability and reproducibility of the RNA-seq data analysis. This pipeline comprehensively reports the main RNA-seq quality metrics and produces output files for direct use in downstream analyses. It was designed as a stand-alone tool for researchers with minimal bioinformatics knowledge to assess their primary sequencing data quality with intuitive visualization.

Transcriptomics (RNA-seq) experiments comprehensively interrogate transcriptional signatures shaping phenotypes. This approach became irreplaceable in plant molecular genetics to identify single or co-expressed genes and biological processes involved, for example, in development, plant pathogen interaction, or abiotic stress resistance1, 2 ,3. Recent advancements in RNA-seq technology have increased specificity and enabled the detection of different isoforms and variants at single-base resolution, allowing the identification of sequence variations from larger indels to single-nucleotide polymorphisms (SNPs). The data obtained by RNA-seq are characterized by a broad dynamic range, facilitating the detection of both highly abundant and lowly expressed transcripts, and require appropriate experimental conditions for consistency. In addition, RNA-seq datasets are large and thus computationally intensive to analyze and store, entailing efficient data management and extensive computing resources. RNA-seq requires high-quality input at every step of the workflow, as weaknesses in any stage can propagate and compromise the results. Poor RNA integrity or technical issues during library preparation will lead to bias and a reduction of accuracy. Parameters for obtaining high-quality raw reads can vary between next-generation sequencing (NGS) facilities. 
According to our experience, we recommend using only RNA with an RNA integrity number (RIN) above 7, which is indicative of largely intact mRNA structure, as input for RNA-seq library preparation. For successful sequencing, approximately 2 µg of total RNA at a concentration of 50–200 ng/µL is required for standard library preparation protocols. RNA purity should be confirmed by an OD260/280 ratio between 1.8 and 2.1 and an OD260/230 ratio greater than 1.5 using a spectrophotometer. Insufficient sequencing depth, low mapping rates, or misalignment to the reference genome may further distort gene expression and splicing quantification. Moreover, inadequate experimental design, such as a lack of discrimination between samples of different tissues or treatments and high variability between replicates, can introduce noise and diminish reproducibility. While many labs analyze transcriptomes routinely, stringent quality controls of the first essential analysis steps are often not reported or may be entirely absent from publications. This may lead to over-interpretation of results derived from transcriptome analysis and, consequently, to irreproducible results. 
Here, we provide a workflow for quality control of the initial steps required for high-quality transcriptome analyses of mRNA profiles to measure transcriptional alterations. Our aim is to enable wet-lab biologists with limited knowledge in bioinformatics to evaluate their primary transcriptomics data. Rup (Figure 1) is accessible to researchers who are familiar with basic knowledge of R. Executing the workflow presented here will provide researchers with a detailed understanding of their primary data, including their potential limitations for subsequent analysis. To our knowledge, a practical primary transcriptome data evaluation pipeline in combination with guidelines for distinguishing high- from low-quality data is lacking so far. 
<img alt="RNAseq process diagram with quality control methods, input file sources, and data visualization." class="xfigimg" src="/files/ftp_upload/69253/69253fig1.jpg" data-altupdatedat="1783396841000" data-alt="Figure 1"> 
Figure 1: Workflow of the RNA-seq in silico quality control pipeline. The input files for quality control are derived from RNA-seq data generated by sequencing plant material, as well as publicly available datasets. The provided R pipeline assesses sequence quality through three main approaches: sequencing quality, mapping quality, and replicate quality. Various quality metrics are computed, and statistical results are visualized using common R packages such as ggplot2 and pheatmap (e.g., bar charts and heatmaps). <a href="https://www.jove.com/files/ftp_upload/69253/69253fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Previously reported evaluation pipelines required pre-processed data (e.g., read alignment as .bam files), do not cover all metrics, or are no longer maintained4,5,6. The advantage of the workflow presented here lies in its comprehensiveness by consolidating the most common quality control issues into a single pipeline. Further, we provide examples for high-quality data suitable for all downstream analysis applications, but also examples for low-quality data, and discuss their specific limitations for further analysis. Several previously published reports describe the purpose of RNA-seq analysis tools and provide comparative evaluations of their performance7,8. However, RNA-seq quality control and methodology reporting standards are not standardized and aggravate reproducibility and biologically meaningful interpretations of transcriptomics experiments. 
Rup integrates standard high-quality tools, quality control analysis, and visualization of the results. Rup requires raw sequencing reads and an annotated genome as input and runs on Mac OS, Linux, and “Windows Subsystem for Linux” (WSL) on Windows systems. It integrates tools for the quality of the sequencing and quantifies read mapping to a genome, includes rRNA content measurement in the samples, and offers sample correlation to evaluate replicate correlation. The test data were obtained using laser microdissection of the central meristem tissue of two different stages of the plant species Eschscholzia californica, an ultra-low input protocol for library preparation, and sequenced on Novaseq 6000.

<table><tbody><tr><td>fastqcr</td><td>R</td><td>0.1.3</td><td></td></tr><tr><td>FUJITSU ESPRIMO D958 Desktop</td><td>FUJITSU</td><td></td><td>the pipeline was developed and tested on Ubuntu Linux 24.02 (32 Gb RAM)</td></tr><tr><td>getopt</td><td>R</td><td>1.20.4</td><td></td></tr><tr><td>ggplot2</td><td>R</td><td>3.5.2</td><td></td></tr><tr><td>MacBook Pro&amp;nbsp;</td><td>Apple</td><td></td><td>the pipeline was tested on maxOS 15.4.1 (16 Gb RAM)</td></tr><tr><td>pheatmap</td><td>R</td><td>1.0.13</td><td></td></tr><tr><td>R</td><td></td><td>4.4.3</td><td></td></tr><tr><td>reshape2</td><td>R</td><td>1.4.4</td><td></td></tr><tr><td>rfastp</td><td>bioconductor</td><td>1.16.0</td><td></td></tr><tr><td>rsamtools</td><td>bioconductor</td><td>2.22.0</td><td></td></tr><tr><td>rsubread</td><td>bioconductor</td><td>2.20.0</td><td></td></tr></tbody></table>

rup (rna-seq usability assessment pipeline) - quality control for bulk rna-seq experiments in eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Modern approaches in molecular plant science often require bulk RNA-seq experiments, for example, to track global changes in transcriptomes upon treatments or to identify key components of regulatory pathways. Consequently, diverse areas of plant sciences rely on high-quality and reproducible bulk RNA-seq data for scientific advancement. However, from our experience, knowledge about and application of quality control measures in RNA-seq datasets are often lacking. Here, we introduce Rup (RNA-seq usability assessment pipeline) for the quality control of bulk RNA-seq data, for subsequent gene expression analyses, that is stand-alone and readily applicable for wet-lab biologists with basic knowledge of R. Rup helps to discriminate between sequencing data of high-quality, suitable for downstream gene expression experiments, and those unsuitable for general further analysis. Rup includes tests for several commonly encountered problems such as insufficient read numbers or mapping, identification of contaminations, quantification of rRNA fractions in the total RNA-seq data, replicate similarity testing, using real data for demonstration and offering intuitive visualization. Rup provides a suite of tools to identify experimental shortcomings before standardized transcriptome analysis, thereby improving data quality for individual researchers and the field. This enhances confidence in bulk RNA-seq data analysis and provides a foundation for future guidelines defining minimum quality control criteria, thus improving the reliability and transparency of published RNA-seq data. Rup and test data are available at https://github.com/oliverrupp/rup.

The pipeline is completely implemented as an R script and has been tested on Linux and Mac OS operating systems. Windows users can use Windows Subsystem for Linux (WSL). The code and test data are available as a GitHub repository: https://github.com/oliverrupp/rup. The sequencing data are available under the ENA EBI project PRJEB96400.
Ten samples were created artificially from two real samples to exemplify diverse problems that may be encountered during quality control of bulk RNA-seq using Rup for analysis. Sample s2_r1 was designed to exhibit a low total read number, sample s2_r2 contains a large fraction of low-quality reads to be discarded by the trimming process. Sample s2_r3 includes a large fraction of rRNA reads and sample s2_r4 includes contaminant reads that failed to map to the reference genome. The names of samples s1_r5 and s2_r5 were swapped to illustrate low replicate correlation.
Protocol section 2 enables the identification of samples with low read numbers prior to or following trimming (Figure 2). The bar plot shows the lower read number in sample s1_r1 both before and after trimming. Here, the initial read number was low. The low read number of sample s1_r2 after trimming suggests a large amount of adapter sequences, sequencing errors, primer sequences, poly-A/T stretches sequenced that were removed during the trimming process. Degradation of input RNA may also reduce the number of high-quality reads.
Protocol section 3 identifies problems in read assignments. Figure 3 depicts the elevated number of multi-mapped reads in sample s2_r3 (green), as well as the high number of rRNA reads. Contamination of sample s2_r4 is apparent by the large fraction of reads not mapped to the reference genome (pink). These reads do not correlate to rRNA but to sequences from a non-target organism. Figure 4 shows general problems associated with low numbers of assigned reads in transcriptomes. In samples with a low rate of reads mapped uniquely to the reference genome (s2_r1 to r4) only about a third of the genes have more than 100 reads assigned whereas in the other samples, about half of the genes fall into these classes. Reads of genes with very low expression may not be found in the samples s2_r1 to r4, and consequently, comparative expression analysis with these samples will be highly unreliable and should be avoided.
Protocol section 4 can be used to identify replicate outliers. Replicates of the same sample/condition/tissue are expected to show a higher correlation between each other than to replicates from other samples/conditions/tissues. Figure 5 shows a correlation heatmap of the two samples (S1 and S2) with five replicates each. The dendrograms on top and to the side of the plot show two clusters with five replicates in each cluster. The left cluster contains four replicates of sample 1 and one replicate of sample 2 (s2_r5), the right cluster contains four replicates of sample 2 and one replicate of sample 1 (s1_r5). In this case, when the sample names s1_r5 and s2_r5 are swapped back again, each cluster contains all replicates of one sample, which may indicate a replicate labeling mistake. Other reasons for replicates not clustering together could be a lack of differentiation between the samples/conditions/tissues, or clustering of replicates for sequencing quality reasons only. The latter may occur when all replicates with exceptionally low or those with exceptionally high read counts after trimming and mapping form a cluster.
<img alt="Correlation heatmap diagram; visualizing data relationships via color gradients for pattern analysis." class="xfigimg" src="/files/ftp_upload/69253/69253fig5.jpg" data-altupdatedat="1783396841000" data-alt="Figure 5"> 
Figure 5: Sample correlation heatmap. The sample correlation heatmap is based on log2 transformed TPM values and shows two distinct clusters of five samples each. Biological/technical replicates are expected to show a higher correlation between each other than those from other tissues/treatments. The left cluster contains four replicates of sample 1 and one replicate of sample 2 (s2_r5), the right cluster contains four replicates of sample 2 and one replicate of sample 1 (s1_r5). The single replicates should be checked for possible sample swapping or batch effects to explain their clustering in the heatmap. <a href="https://www.jove.com/files/ftp_upload/69253/69253fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Comparison of RNA-seq quality assessment pipelines. For a detailed analysis, see Supplemental File 1. <a href="https://www.jove.com/files/ftp_upload/69253/69253_Table%201.xlsx" target="_blank">Please click here to download this Table.</a>
Supplemental File 1: Parameter and reference selection. The analyses show the influence of parameter and reference selection on the overall QC results. <a href="https://www.jove.com/files/ftp_upload/69253/20250912_JoveManuscript_SupplementalFile.docx" target="_blank">Please click here to download this File.</a>

This protocol allows initial quality control for RNA-seq experiments for wet-lab biologists with limited bioinformatics experience.

Modern approaches in molecular plant science often require bulk RNA-seq experiments, for example, to track global changes in transcriptomes upon ...

We developed innovative bioinformatics tools to simplify, automate, and integrate data analysis from high throughput experiments. We use high throughput sequencing, advanced bioinformatics software, and powerful computing infrastructure to enable systematic biological analysis. To begin, install all required R packages using the bioconductor package manager.Create a source folder to organize the input files for the analysis. Add the reference genome sequence in FASTA format as ReferenceGenome. fa to this folder.Add the gene model annotation file named ReferenceAnnotation. GTF to the same folder. Optionally, include the RRNA gene annotation as a GTF file named ReferenceRRNA.gtf.Place all sequencing reads as compressed FASTQ files into the folder named Reads. Ensure that each file follows the naming format. Then set the analysis parameters according to the sequencing method used.To map quality of the sequence, use the Rsubread package to build an index of the reference genome from the genome FASTA file. For each sample, use the align function to iterate and align sequencing reads to the reference genome. Store the resulting alignment files in the output folder in bam format.Now use the feature counts function to count reads mapped to each gene. The annotation files should be in the GTF format. Ensure only reads with a single match to the genome are counted.Count the reads that map to RRNA genes by using the feature counts function with the RRNA gene GTF file. Allow multi-mapped reads to be included in this count. Retrieve the read assignment statistics generated by the feature counts function for each sample.These statistics include the number of reads categorized as assigned, unmapped, multi-mapped, and others. Collect the statistics for RRNA gene assignments separately. Then generate bar plots, visualizing the read mapping statistics from the previous steps.Group genes based on the number of reads assigned to them. Plot the classification results as a bar plot. Sample S2R1 showed a low number of reads both before and after trimming.The trimmed read count of sample S2R2 was visibly reduced compared to its raw read count, indicating removal of low quality reads during trimming. Mapping identified problems in the read assignments. Sample S2R3 exhibited a high number of multi mapped reads and an elevated amount of ribosomal RNA reads.A large fraction of reads in sample S2R4 did not map to the reference genome, suggesting contamination with sequences from a non-target organism. Samples S2R1 through S2R4 showed fewer genes with more than 100 assigned reads. In the correlation heat map, sample S2R5 clustered with the replicates of sample S1 and sample S1R5 clustered with the replicates of sample S2, indicating a likely replicate labeling error.We address missing quality control for RNA-Seq, ensuring reliable data assessment before downstream gene expression analysis. Our tool integrates multiple quality text, offering accessible, automated, and reproducible RNA-Seq assessment for biologists. Our tool enables exploring how RNA-Seq quality influences biological interpretation, paving the way for transparent and reproducible transcriptomics.

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Research

JoVE Journal

Biology

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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AQRNA-seq for Quantifying Small RNAs

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Preparation of Artificial Bilayers for Electrophysiology Experiments

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Learn automated RNA-seq quality control using R packages and Bioconductor tools. This computational pipeline enables biologists to assess mapping statistics, identify errors, and ensure reproducible transcriptomics analysis.

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

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