How can you use RNA-seq data to analyze gene expression?

RNA-seq is a powerful technique to measure gene expression levels across different samples, conditions, or time points. It can reveal how genes are regulated, how they interact, and how they contribute to biological functions and phenotypes. However, RNA-seq data also pose many challenges, such as quality control, normalization, alignment, quantification, differential expression analysis, and interpretation. In this article, you will learn how to use RNA-seq data to analyze gene expression in six steps.

1 Step 1: Prepare your samples and sequencing libraries

Before you can use RNA-seq data to analyze gene expression, you need to prepare your samples and sequencing libraries. This involves extracting RNA from your biological samples, such as cells, tissues, or organisms, and converting it into cDNA. Then, you need to fragment, size-select, and ligate adapters to the cDNA to generate sequencing libraries. The quality and quantity of your RNA and libraries will affect the accuracy and reliability of your gene expression analysis, so you should check them using methods such as gel electrophoresis, qPCR, or bioanalyzer.

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Dennis Odoi

Biomedical Scientist || Health Writer || Public Health Enthusiast || Reproductive Health Advocate || Entrepreneur || Certified Google Digital Marketer
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The preparation of samples and sequencing libraries is a critical step in utilizing RNA-seq data for gene expression analysis. This process involves converting RNA molecules into sequencer-ready libraries, ensuring accurate representation of gene expression levels. Proper sample preparation enhances the reliability and reproducibility of RNA-seq results, allowing researchers to draw meaningful insights into gene expression patterns. This meticulous step is foundational for unlocking the full potential of RNA-seq data and gaining a comprehensive understanding of the dynamic landscape of gene expression in biological systems.

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Iftekhar Rafiqullah, PhD

Microbiome Sequencing| Molecular Biologist | Freelance Bioinformatician | Laboratory Scientist | Next Generation Sequencing | Linux Python R Bash
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I agree. Sample preparation is the first critical step. Extraction of RNA from biological samples needs to be done carefully under the hood so that it doesn’t get contaminated. Kits and reagents need to be separated. Once cDNA is ready, keep continuing the protocol for fragmentation, adapter ligation, etc. Make sure QC is done to check concentrations, size , etc. appropriate for sequencing breadth and depth.

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2 Step 2: Perform sequencing and quality control

The next step is to perform sequencing and quality control of your RNA-seq data. You can use different sequencing platforms, such as Illumina, PacBio, or Oxford Nanopore, depending on your budget, throughput, and research questions. You should also choose the appropriate sequencing depth, read length, and paired-end or single-end mode for your experiment. After sequencing, you should perform quality control of your raw data using tools such as FastQC, MultiQC, or Trim Galore. These tools can help you identify and remove low-quality reads, adapter contamination, or other artifacts that can affect your gene expression analysis.

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3 Step 3: Align reads to a reference genome or transcriptome

The third step is to align reads to a reference genome or transcriptome using tools such as STAR, HISAT2, or Salmon. This step will allow you to map your reads to the genomic or transcriptomic regions that they originate from, and quantify the expression levels of genes or transcripts. Depending on the tool you use, you may need to provide a reference genome or transcriptome file, an annotation file, or an index file as inputs. You should also adjust the parameters of the alignment tool to optimize the speed, accuracy, and sensitivity of the alignment process.

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Jingbo Zhang

Gene therapy | Viral vector | Gene delivery
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If the whole genome date is not available for the organism you studied, you can also do the De novo transcriptome assembly from the RNA-seq data you received. The reads can then align to the De novo transcriptome.

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Frances Turner

Bioinformatician at The University of Edinburgh
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Sometimes more than one species is present in a sample (for instance a host and a pathogen). Even if you are only interested in one species, it is best practice to concatenate the the genomes/transcriptomes of all the species in your samples, then align all data to the concatenated genome. Once you have performed the alignments, further QC can be carried out. We find the % of reads mapping to the genome/transcriptome, median PCR duplicate rate per gene, and % of rRNA are all useful metrics to assess library quality and ensure appropriate downstream analysis. Picard tools is useful at this point.

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4 Step 4: Perform differential expression analysis

The fourth step is to perform differential expression analysis using tools such as DESeq2, edgeR, or limma. This step will allow you to compare the expression levels of genes or transcripts across different samples, conditions, or time points, and identify the ones that are significantly upregulated or downregulated. Depending on the tool you use, you may need to provide a count matrix, a design matrix, or a contrast matrix as inputs. You should also consider the factors that may affect your differential expression analysis, such as normalization, dispersion estimation, statistical testing, multiple testing correction, and fold change threshold.

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Frances Turner

Bioinformatician at The University of Edinburgh
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Before testing for differential expression, it is important to remove lowly expressed genes. Some tools (such as DESeq2) will do this for you, other tools (such as edgeR) required you to do this yourself. Failure to perform this step will result in much less significant adjusted p-values (or false discovery rates) after multiple correction testing. It is also important to consider any factors in your experiment (such a sample batch) that may influence gene expression, and ensure you control for these by including them in your design matrix

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5 Step 5: Interpret and visualize the results

The fifth step is to interpret and visualize the results of your differential expression analysis using tools such as R, Bioconductor, or Python. This step will allow you to explore the biological meaning and significance of your differentially expressed genes or transcripts, and communicate your findings to others. You can use various methods to interpret and visualize the results, such as gene ontology analysis, pathway analysis, gene set enrichment analysis, volcano plots, heatmaps, or clustering.

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6 Step 6: Validate and extend the results

The final step is to validate and extend the results of your gene expression analysis using other methods or data sources. This step will allow you to confirm the reliability and reproducibility of your results, and discover new insights or hypotheses. You can use various methods to validate and extend the results, such as RT-qPCR, western blot, RNA interference, CRISPR-Cas9, or single-cell RNA-seq.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Salma B.

Early Stage innovation ꘡ Deep Technology Commercialization ꘡ Innovation Ecosystems ꘡ Community Building
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Analyzing RNA-seq data is a common method for estimating gene and transcript expression. The process typically involves several steps: Trimming, aligning, counting, and normalizing the sequenced reads, followed by differential expression (DE) analysis across different conditions. Statistical analysis is then performed to determine p-values and establish the false discovery rate (FDR) cutoff. Subsequent analyses depend on the specific hypothesis being investigated. Additional approaches may include utilizing Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape, EnrichmentMap, and Metascape for pathway enrichment analysis. These methods help provide mechanistic insights into the gene lists generated from RNA-Seq data.

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How can you use RNA-seq data to analyze gene expression?

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1 Step 1: Prepare your samples and sequencing libraries

2 Step 2: Perform sequencing and quality control

3 Step 3: Align reads to a reference genome or transcriptome

4 Step 4: Perform differential expression analysis

5 Step 5: Interpret and visualize the results

6 Step 6: Validate and extend the results

7 Here’s what else to consider

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