Top 6 commercial RNA-seq Data Analysis Software

Numerous commercial RNA-seq data analysis software provide streamlined and robust solutions for biologists, biotechnology, or pharmaceutical companies to rapidly and accurately analyze omics data.

The amount of transcriptomic data researchers can generate with RNA-seq is now greater than ever. However, one major bottleneck for the life science sector is the data analysis stage.

In this article, we highlight the top commercial RNA-seq analysis software available to overcome this bottleneck, focusing on data visualization and interpretation stages.

What is a commercial RNA-seq analysis software?

Commercial RNA-seq analysis software provide biotech or pharma companies with interactive, user-friendly, and streamlined end-to-end RNA-seq analysis solutions.

These are suitable for large-scale, diverse study designs as part of drug discovery pipelines and generate results from complex data with unprecedented ease and speed.

Most platforms require no coding experience.

Adavantages of commercial platforms

Good RNA-seq analysis solutions have intuitive user interfaces, avoid complex coding, and harness powerful ‘under the hood’ algorithms to produce publication-ready plots with few clicks.

Furthermore, good data analysis pipelines are both standardized and reproducible.

This is key because updates to bioinformatic tools or libraries, combined with the complexity of analysis pipelines, can all lead to reproducibility issues (Kulkarni et al., 2018).

While not precluding free software, commercial software manages these issues with robust version control and ensures that entire pipelines are intricately documented and standardized. As a result, users can be confident that results are consistent and reproducible over time.

Most commercial platforms allow real-time sharing of data analysis in the cloud. This enables fast and secure data access for teams with multiple collaborators in biotech or pharma.

Additionally, commercial RNA-seq software are often available entirely online or are easy to install locally and have extensive analysis support. They also have a longer product lifecycle with continual improvements thanks to ongoing development. This contrasts to some free software where development may stop upon its release.

Disadvantages of commercial RNA-seq data analysis platforms

The disadvantages of commercial solutions depend primarily on the user and the amount of data produced.

For example, research teams that run a few experiments per year might need more data to justify the cost of a commercial solution.

However, free versions of different commercial software can provide a range of functions for sporadic users.

More complex platforms such as Partek or QIAGEN Ingenuity Pathway Analysis (IPA) may require a steep learning curve for users with limited bioinformatic experience.

Despite this, scientists can hit the ground running with intuitive, user-friendly options such as the Omics Playground.

Major commercial platforms

1. Omics Playground

Omics Playground RNA-seq data analysis platform

Who's it for?

Omics Playground from BigOmics Analytics is ideal for both biologists and bioinformaticians.

Biologists without bioinformatics experience can visually explore and analyze their RNA-Seq and proteomics data, while bioinformaticians can use it to standardize analytics workflows and offload repetitive tasks.

The platform makes it easy to store, compare, and share data from previous experiments, making it well-suited for collaborative teams.

Types of data

This platform is optimized for bulk and single-cell RNA-seq (scRNA-seq) and proteomics data.

Overview of possible RNA-seq analyses

Bulk RNA-seq analyses include sample clustering by gene expression similarities, differential gene expression analyses, gene set enrichment, gene ontology analyses, comparative intersections and correlations of differentially expressed genes, functional pathway analyses, gene expression signatures, biomarker detection, and drug connectivity (Akhmedov et al., 2020; BigOmics Analytics, 2023).

For scRNA-seq, users can perform single-cell profiling with cell type prediction and marker gene detection, among others.

Pros and cons

The Omics Playground is accessible to everyone thanks to a streamlined user interface and interactive RNA-seq data visualizations. Researchers can share datasets with collaborators in real-time in the cloud and compare their datasets to publicly available RNA-seq databases.

However, the Omics Playground only specializes in RNA-seq and proteomics data analysis. This could limit some users.

2. Rosalind

Who's it for?

Rosalind from OnRamp Bioinformatics is for scientists with no bioinformatics experience.

Types of data

Rosalind is suitable for bulk, single-cell and small RNA-seq, NanoString nCounter data, ChIP-seq, and ATAC-seq data.

Overview of possible RNA-seq analyses

For bulk RNA-seq, researchers can perform differential expression analyses, functional enrichment analyses of pathways, gene ontologies, diseases and drug interactions, and detection of gene expression patterns across datasets (Rosalind, 2023).

For scRNA-seq, researchers can perform cell cluster annotation with cell type prediction and comparisons between cell clusters or bulk RNA-seq data.

Pros and cons

Rosalind enables users to combine multi-omics experiments, share real-time analyses with collaborators in the cloud and compare their data to publicly available datasets.

However, it does not allow users to analyze proteomics data.

Furthermore, the scRNA-seq analysis options have only been optimized for data from 10X Genomics. Other software allow the analysis of all common types of scRNA-seq data.

3. PanHunter

Image by PanHunter via

Who’s it for?

PanHunter from Evotec is for both biologists and bioinformaticians.

Types of data

PanHunter is suitable for bulk and scRNA-seq data, spatial transcriptomics data, proteomics data, genomics data, and metabolomics data.

Overview of possible RNA-seq analyses

Bulk RNA-seq analyses extend to differential expression analyses, gene expression signature detection, enrichment and clustering, cross-comparison of datasets, gene ontology, interaction networks, functional pathways and molecular signatures, and trend analyses (PanHunter, 2023).

For scRNA-seq, PanHunter can perform cell type clustering, annotation, and comparisons.

Pros and cons

PanHunter enables intersection with large molecular patient databases with options to use either the graphical interface or scripts. Like the other platforms we have discussed, it is cloud-based to allow users to share real-time analysis results.

One downside is that the number of customizable analysis options may overwhelm non-bioinformaticians. However, the platform guides researchers through most analyses

4. IPA and CLC

Who's it for?

Qiagen CLC Genomics Workbench is made for biologists. However, IPA is a more complex software suitable for biologists and bioinformaticians.

Types of data

The CLC workbench has numerous modules for different omics data types. It is mainly suitable for genomics, bulk RNA-seq, scRNA-seq, long-read sequencing, ChIP-seq, and ATAC-seq datasets. In addition, IPA is ideal for RNA-seq, small RNA-seq, microarrays, metabolomics, and proteomics.

Overview of possible RNA-seq analyses

CLC workbench offers differential expression analyses, sample clustering based on gene expression, comparison of results between experiments, and gene ontology analyses (QIAGEN, 2023a).

With IPA, core analysis users can uncover enriched signaling and metabolic pathways, biological functions, and diseases. It can also predict the effects of gene expression on upstream regulators and build networks based on genetic interactions (QIAGEN, 2023b).

Pros and cons

While less visually attractive than other platforms, the CLC workbench is easy to use and offers additional features such as primer design and a genome browser.

However, analyses must be stored locally with the standard CLC package. Furthermore, IPA must also be purchased if in-depth pathway analyses are required. These often come as standard in other tools mentioned.

5. Partek

Who's it for?

Partek may be more involved for a biologist with no coding experience and requires a steep learning curve. However, it is also suitable for bioinformaticians thanks to coding options.

Types of data

Partek is suitable for bulk and scRNA-seq, genomics such as variant detection, copy number variation analysis, and metagenomics. In addition, epigenetic approaches such as DNA methylation and ChIP-seq data can also be analyzed (Partek, 2023).

Overview of possible RNA-seq analyses

Users of Partek can perform differential expression analyses, sample clustering based on gene expression, gene ontology and pathway analyses.

Pros and cons

Partek software includes a genome browser and allows users to integrate gene expression data with other omics datasets. It also features scripting options for advanced users.

However, it is not possible to analyze proteomics data with Partek. Also, this software is one of the more complex platforms and the data analysis interface has a steep learning curve.

6. Sequentia (AIR)

AIR commercial RNA-Seq data analysis software
Image by Sequentia Biotech via

Who's it for?

Artificial intelligence RNA-seq (AIR) from Sequentia Biotech is designed for biologists without coding experience.

Types of data

AIR is optimized solely for RNA-seq data.

Overview of possible RNA-seq analyses

Users receive differential gene expression analysis and gene ontology enrichment results in graphs and tables (Sequentia Biotech, 2023).

Pros and cons

AIR supports a vast range of genomes and is very user-friendly.

However, the platform is only suitable for RNA-seq data with limited analysis options and does not allow integration with other types of omics datasets.


The bottleneck of RNA-seq data analysis for drug discovery pipelines or research projects is now dramatically minimized thanks to streamlined, user-friendly software that makes bioinformatics available to everyone.

A subscription to a commercial RNA-seq analysis software ensures data security, standardization, and reproducibility of data analysis pipelines. These software make transcriptomic analyses quicker and easier than ever before. These benefits will undoubtedly enhance biotechnology and pharmaceutical sector drug discovery workflows to drive advances in human health and understanding of disease.

For those who are already data savvy and for the more advanced users, more complex platforms such as Partek, PanHunter, or CLC and IPA from Qiagen may be the most suitable.

In contrast, other platforms such as Rosalind and the Omics Playground are suited to both scientists with limited coding experience and bioinformaticians thanks to their user-friendly interfaces and coding options. Between these latter two, Omics Playground has a wider range of RNA-seq data analysis functionalities to accelerate novel discoveries.

Why not get playing with your RNA-seq data analysis in the Omics Playground!