Next-generation sequencing

Accelerate transcriptome insights with powerful RNA sequencing solutions

RNA sequencing explained

RNA sequencing (RNA-seq) is a highly sensitive and accurate next-generation sequencing (NGS) technology that drives novel transcriptomics and gene expression research discoveries. By capturing the complete set of RNA transcripts in a biological sample, RNA-seq offers:

Greater dynamic range for gene expression analysis
Higher specificity to detect low-abundance transcripts
Novel isoform discovery crucial for cancer genomics, immunology, microbiome research and drug development

The RNA sequencing workflow starts with RNA isolation from cells or tissues, followed by library preparation where RNA is converted into cDNA, fragmented and ligated with sequencing adapters. The prepared RNA-seq library is then sequenced to generate millions of reads, which are analyzed using bioinformatics tools to reveal transcriptome insights.

Facing challenges with degraded RNA or low-input samples? Maximize your RNA-seq performance with QIAseq RNA-seq solutions, designed to conquer the complexity of the transcriptome, for example, by improving on-target gene expression reads or enhancing sensitivity for low-quality or FFPE samples – all while saving time and effort.

RNA extraction for RNA-seq

RNA can be extracted from various sources, including whole blood, stool/microbiome, cultured cells and fresh, frozen or FFPE tissues. Cultured cells typically yield high‑quality, homogeneous RNA, but the optimal sample type depends on the biological question; small tumor biopsies, for example, can be heterogeneous and low‑input.

RNA quality largely reflects the quality and handling of the starting material – fresh is ideal, but immediate stabilization and appropriate storage are critical as is minimizing freeze–thaw cycles.

Typical steps include sample stabilization, disruption and homogenization, RNA purification, concentration and QC for quantification and integrity assessment.

mRNA enrichment and rRNA removal

Total RNA generally contains only a very small percentage of coding or functional RNA; ribosomal RNA (rRNA) makes up the majority (up to 90%) of the RNA in a sample. rRNA must be removed from total RNA before sequencing to ensure the highest quality results.

This is often achieved either by specifically depleting rRNA or selectively enriching polyadenylated RNA using oligo-dT enrichment. Depletion of rRNA preserves information on both coding and noncoding RNA, while enrichment of the poly A fraction preserves only coding mRNA.

Is highly abundant rRNA impacting the sensitivity of your gene expression and transcriptomics research, leading to wasted reads and increased RNA-seq costs?

Benefit from efficient one-step removal of >95% of unwanted rRNA using QIAseq FastSelect technology. Whether you’re working with mammalian, plant, yeast or bacterial RNA samples, high-quality or degraded or FFPE RNA samples, QIAseq FastSelect can transform your RNA-seq – and outperform Ribo-Zero, RiboErase and RiboMinus.

Learn more

QIAseq FastSelect, monitor screen with barchart in a lab

Introduction to bulk RNA sequencing

Bulk RNA sequencing (bulk RNA-seq) is a method of transcriptome analysis that involves extracting and sequencing pooled RNA from cell populations, tissues or biopsies.

Unlike single-cell RNA sequencing, bulk RNA-seq measures the average gene expression levels across different conditions or samples, making it particularly useful for analyzing complex tissues or large cell populations where single-cell resolution is not required. It is commonly used in comprehensive transcriptome profiling, differential gene expression analysis, biomarker investigations and functional genomics studies.

A typical bulk RNA sequencing workflow includes library preparation (either stranded or non-stranded), which starts with direct RNA extraction and amplification of tissues and cell populations, cDNA synthesis and adding adapters or junctions. One may have to enrich mRNA or deplete ribosomal RNA (rRNA) for total RNA sequencing. The prepared library then undergoes sequencing on a high-throughput platform, enabling robust analysis of the transcriptomic landscape.

Stranded (directional) library preparation preserves the polarity of the transcripts. That matters whenever transcripts overlap on opposite strands (antisense/lncRNA, UTR overlaps), because stranded data assigns reads to the correct gene and reduces “ambiguous” counts; several evaluations show better quantification in overlapping regions compared with unstranded data.

RNA-seq library preparation methods

Choosing the right RNA sequencing library preparation method depends on several factors, including your experimental objective, budget and availability of a reference transcriptome for the organism of interest.

The QIAseq library preparation kits mentioned here are compatible with most medium- and high-throughput sequencers on the market.

Total RNA sequencing

Total RNA-seq (whole transcriptome RNA-seq) captures all the RNA species in a sample, often larger than 75 bases, including mRNA and identifies genes and pathways associated with biological response or lack of response to novel drug therapies and non-coding RNA. It can accurately measure gene and transcript abundance and is ideal for discovering novel transcripts and alternative splicing events across a wide dynamic range. A few key applications include the discovery of disease pathways, biomarker identification and classification of disease subtypes, as well as therapy-response prediction and monitoring.

Total RNA-seq follows four basic steps: RNA extraction, ribosomal RNA (rRNA) depletion and library preparation, sequencing and data analysis.

Because total RNA libraries include many overlapping sense/antisense and intronic reads, using a strand-specific library-prep chemistry (e.g., dUTP second-strand marking or directional adapter ligation) preserves each read’s transcriptional orientation, allowing unambiguous assignment to the correct gene or isoform and eliminating ambiguity from overlapping transcripts.

QIAseq FastSelect RNA Library Kits utilize a simple <5-hour workflow for complete transcriptomics starting from 1 ng of total RNA.

Discover QIAseq FastSelect

Young male Scientist using micro pipette with DNA

mRNA sequencing

Messenger RNA sequencing (mRNA-seq) involves high-resolution analysis of the coding transcriptome by enriching for polyadenylated RNAs from the total RNA. This results in an RNA-seq library that has a high abundance of protein-coding genes and low amounts of other types of RNA. If an RNA is not polyadenylated, it will be absent. The sample quality and amount necessary for poly-A enrichment can be challenging for low-input or fragmented samples, like those from FFPE.

Researchers can integrate mRNA enrichment kits such as the QIAseq Stranded mRNA Enrichment Kit and focus RNA-seq library construction on protein coding mRNAs using the QIAseq FastSelect RNA Library Kits.

This method quantifies gene expression and detects known or novel isoforms, gene fusions and allele-specific expression across diverse species. By revealing transcript-level biomarkers, it clarifies disease mechanisms and guides drug development, patient stratification and safety assessments.

Find out more

3’ RNA sequencing

3' RNA sequencing (3′ RNA-seq) focuses sequencing on the poly-A tail of each transcript, but traditional workflows struggle with rRNA carry-over, fragmented or low-input RNA, and complex, multi-step protocols.

QIAseq FastSelect RNA Library Kits turn 3′ RNA-seq into a one-tube, one-day workflow that delivers strand-specific, rRNA-free libraries from even low-input or FFPE samples. It cuts sequencing costs (≈ 1–5 M reads per sample) while still enabling high-throughput multiplexing (up to 768 samples) and accurate gene-level expression quantification.

Key applications include:

Large-scale differential expression studies such as drug screens, population cohorts, biomarker discovery
Cost-sensitive projects where the main question is which genes are up- or down-regulated
Clinical/translational research where hundreds of samples are profiled at once

Learn more

miRNA sequencing

miRNA sequencing (miRNA-seq) is a targeted RNA-seq approach used to profile small regulatory RNAs, such as microRNAs (miRNAs). These molecules play central roles in gene regulation and are increasingly studied in disease contexts. Accurate results depend on optimized library preparation and analysis workflows. When working with biofluid samples, low RNA input and high inhibitor levels can pose challenges. A gel-free miRNA-seq solution compatible with just 1 ng of total RNA helps overcome these issues and supports robust detection of differential miRNA expression. Gain novel disease insights faster and with ease using the RNA-seq Analysis Portal – a user-friendly, web-based tool included with QIAseq miRNA Library Kits.

Want to discuss your RNA-seq project?

Our specialists are on hand to answer all your questions and help you find the best solutions for your project.

Get in touch

Low-input RNA-seq

Low-input RNA library prep can be immensely problematic, but it doesn’t have to mean low confidence. Each workflow step has the potential to result in further loss of RNA. Low input RNA-seq protocols often use the template-switch method of library construction ensuring higher sensitivity than traditional ligation-based methods. The QIAseq Low Input RNA Library Kits maximize sensitivity and preserve strandedness from as little as 1 ng of total RNA. With the option of integrated rRNA/globin depletion during reverse transcription, you can minimize sample loss while generating clean, NGS-ready libraries in under 5 hours.

Looking to scale beyond low-input projects? Explore how the QIAseq Low Input RNA Library Kit lets you customize your workflow based on sample number, read budgets and sequencing platform.

See how

Targeted RNA sequencing

Targeted RNA-seq focuses on sequencing specific subsets of RNA transcripts rather than the entire transcriptome; and can be achieved via either enrichment or amplicon-based approaches. It allows for the detection of low-abundance transcripts, particularly useful in biomarker validation and studies where specific genes or pathways are of interest.

If you’re monitoring gene expression changes or looking to identify fusion genes and variants using targeted RNA-seq, challenges during library construction and NGS analysis, such as PCR bias and sequencing artifacts, can jeopardize the sensitivity and reproducibility of your data.

The QIAseq panels for targeted RNA sequencing address these challenges with proprietary QIAseq enrichment technology and Unique Molecular Index (UMI) technology. Together, they improve precision, accuracy and coverage uniformity, helping you overcome traditional RNA-seq limitations and reduce bias.

Find out how

Take the shortcut – library normalization without quantification

Dreading time-consuming library quantification? Achieve effective NGS library normalization effortlessly in just 30 min and with qPCR-level accuracy.

Find out more

Introduction to single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) studies RNA transcript heterogeneity at the single-cell level, uncovering diverse cell types, functions and interactions within complex tissues and organisms. This makes it particularly useful for studying dynamic processes such as differentiation, proliferation and tumorigenesis.

Standard scRNA-seq workflow

Cell isolation – Individual cells are isolated from bulk tissue or cell suspensions
mRNA capture and cDNA synthesis – Polyadenylated mRNA is extracted and converted into cDNA
Library preparation – Unique molecular identifiers (UMIs) and cell barcodes are added to differentiate transcripts by cell
Sequencing – Libraries are sequenced on NGS platforms
Bioinformatics analysis – Reads are analyzed to generate gene expression profiles for each cell

Working with single cells or limited RNA doesn’t have to mean limiting your view of the transcriptome.

Explore how the QIAseq Single Cell RNA library kit provides NGS-ready, high-complexity libraries from isolated cells in just 5.5 hours. Optimized chemistry and a PCR-free protocol eliminate issues with bias, ensuring sensitive and accurate transcriptome coverage.

A superior amplification technology compared to PCR, QIAseq Single Cell REPLI-g enables genome and transcriptome studies from single cells or other low-input DNA/RNA samples, providing reliable coverage and uniform representation.

Visit the single cell hub

RNA-seq analysis

Struggling to make sense of your RNA-seq data? We’ve got just the solution! QIAseq RNA Library and FastSelect Kits now include free access to the RNA-seq Analysis Portal – an intuitive, web-based solution made for biologists to simplify data analysis. Fully integrated with GeneGlobe, the RNA-seq Analysis Portal allows you to seamlessly go from generating RNA-seq data to gaining gene expression insights.

Automate and accelerate your library prep

How can automating your NGS library prep benefit your lab?

Find out

Success stories

Whole transcriptome & 3' mRNA sequencing service — just $130/sample*

Take advantage of premium-quality gene expression data at a highly competitive price. Ideal for pathway analysis, differential-expression studies and large-scale expression profiling.

Inquire

Unlock high-quality gene expression at a competitive price. Ideal for pathway analysis, differential-expression studies and large-scale expression profiling.

* Contact your local sales representative for more details. Time-limited offer. Terms and conditions apply.

Your guide to RNA-seq success with degraded, low-quality or difficult-to-sequence RNA samples

Not getting sufficient on-target reads? Working with limited or fragmented RNA? See what advantages QIAseq RNA-seq technologies offer to work optimally with the most challenging of samples:

Discover Genomic Services

Combine unparalleled expertise and support with our high-performance RNA-seq technologies

Learn how

RNA sequencing FAQs

What are the latest advancements in RNA-seq technologies?

Recent advancements in RNA-seq include high-throughput 3'-end sequencing for cost-effective gene quantification, the integration of Unique Molecular Indices (UMIs) to reduce PCR bias, and single-cell RNA-seq for high-resolution transcriptome profiling. Other innovations include spatial transcriptomics and direct RNA sequencing using long-read platforms, offering deeper insights into gene regulation and isoform diversity.

What are the best practices for designing an RNA-seq experiment to ensure robust and reproducible results?

To ensure high-quality RNA-seq data, begin by clearly defining your biological question. Include at least three biological replicates per condition and incorporate proper controls. Plan for batch effect mitigation, randomize sample processing and choose a sequencing strategy that aligns with your sample type and goals (e.g., polyA-enriched, total RNA or small RNA). Including control RNAs like the ERCC standards, can help determine the sensitivity of each library for better comparison across batches and runs.

What are the best practices for extracting RNA or total nucleic acid for RNA sequencing?

Use RNA extraction kits that minimize contamination and preserve RNA integrity. Avoid RNase contamination by working in RNase-free conditions. For degraded or FFPE samples, use kits optimized for fragmented RNA. Validate extraction success using Qubit or Nanodrop for quantification and Bioanalyzer or TapeStation for integrity assessment.

What are the best practices for ensuring RNA sample quality before sequencing?

Assess RNA quality using the RNA Integrity Number (RIN) or DV200 for FFPE samples. Confirm RNA purity by checking 260/280 and 260/230 ratios. Avoid freeze-thaw cycles and remove genomic DNA with DNase treatment to prevent downstream interference.

How do I select the appropriate RNA-seq library preparation kit based on my research objectives and sample type?

Choose your library prep kit based on:

Sample type (fresh, FFPE, low-input, biofluids, single-cell)
RNA type (mRNA, total RNA, miRNA, lncRNA, fusion transcripts)
Research goals (discovery, targeted analysis, biomarker validation)

For example, use QIAseq miRNA Library Kits for small RNA profiling or QIAseq Targeted RNA Panels for detecting fusion genes in clinical samples.

How do I choose the appropriate sequencing depth for my RNA-seq experiment?

Sequencing depth depends on your application and the input amount of your sample.

mRNA-seq: 20–30 million reads per sample
Transcriptome-wide (rRNA removal): 40-60 million reads per sample
Small RNA-seq: 5–15 million reads
Single-cell RNA-seq: 50k–100k reads per cell
Targeted RNA-seq: ~1 million reads

Deeper sequencing improves detection of low-abundance transcripts but must be balanced with cost and experimental design.

What are the common challenges in RNA-seq data analysis and how can they be addressed?

Common challenges include batch effects, PCR duplication, alignment errors, and low-complexity reads. These can be addressed by:

Using UMI-aware tools to eliminate PCR duplicates
Employing normalization strategies (e.g., DESeq2, edgeR)
Applying updated genome annotations
Visualizing data with PCA and clustering to detect outliers

What are the considerations for analyzing RNA-seq data from degraded samples, such as FFPE tissues?

Use rRNA-depleted or total RNA library prep protocols optimized for degraded RNA. Design amplicons <170 bp and use UMIs to correct for errors and bias. Assess sample quality using DV200 rather than RIN, and expect some limitations in transcript coverage.

How can I integrate RNA-seq data with other omics datasets for a comprehensive analysis?

Integrating RNA-seq with other omics (proteomics, metabolomics, epigenomics) requires consistent sample metadata, data normalization, and pathway-based interpretation. Use platforms like Ingenuity Pathway Analysis (IPA) to align gene expression data with functional biological pathways and networks.

What are the current best practices for differential gene expression analysis in RNA-seq studies?

Best practices include:

Using statistical tools like DESeq2, edgeR, or limma-voom
Applying proper normalization and filtering
Using False Discovery Rate (FDR) correction for multiple testing
Visualizing results through heatmaps, volcano plots, and PCA

Robust differential expression analysis depends on sound experimental design, proper replicates, and accurate data preprocessing.