Commercial RNA-seq data analysis software allows researchers, biotechnology, and pharmaceutical companies to rapidly analyze RNA-seq data with streamlined, user-friendly tools. These tools suit scientists with any level of data analysis expertise and can accelerate research and drug discovery workflows.
This article breaks down the pros and cons of commercial RNA-seq analysis software to help you choose the best one for your data analysis pipeline.
9 factors you should consider when choosing an RNA-seq analysis and visualization software
What is RNA-seq data analysis software?
RNA-seq analysis can be split into two main phases after data acquisition.
Firstly, the acquired reads must be mapped to a reference genome during data pre-processing. Advances in the efficiency of mapping algorithms, combined with increased computing power, mean that this analysis stage is no longer a significant bottleneck.
In contrast, the data discovery phase now represents the main bottleneck for researchers. This is key to the exploration, visualization, and accurate interpretation of experiments. However, hundreds of thousands of transcriptomes are routinely compared in single-cell RNA-seq experiments, and the sheer amount of available multi-omics data can become overwhelming.
Here, we focus on RNA-seq analysis software specialized for this data discovery phase.
These software consists of bioinformatic tools that enable biotechnology and pharmaceutical companies with varying levels of bioinformatics experience to explore, visualize and analyze RNA-seq data with interactive, user-friendly interfaces.
They are suitable for the different types of analysis required for transcriptomic data generated in research and drug discovery pipelines.
Numerous commercial RNA-seq analysis tools are available with diverse capabilities and features. For example, some platforms allow users to also analyze different types of omics data, such as proteomics, alongside their RNA-seq analyses, while others specialize in only RNA-seq data.
The choice of platform ultimately depends on your data analysis needs.
Factors to consider with commercial RNA-seq analysis software
You should consider various factors before choosing a specific commercial RNA-seq analysis software.
Here, we outline some of the key advantages and disadvantages to weigh up.
1. Data Security
It is paramount to ensure that data is protected.
Most commercial software are highly secure, with multiple layers of data protection to ensure data integrity across the entire analysis pipeline, from storage and RNA-seq data visualization to data sharing.
These layers extend to secure password access, encryption of stored data, internet standard ‘Transport Layer Security’ to ensure encrypted communications across the internet and administrator-controlled role-based access for tight regulation over who can do what within the platform. Software also often conform to global data protection and privacy standards.
2. Reproducibility
Reproducibility is fundamental to scientific research. It lends confidence to findings and indicates that the initial data and analysis pipeline was robust.
Despite this, a “reproducibility crisis” has been described in bioinformatics workflows. This is primarily due to insufficient documentation of pipeline requirements or frequent updates to essential tools or libraries, which affect subsequent analyses (Kanwal et al., 2017; Kulkarni et al., 2018).
In commercial software, entire pipelines are extensively documented and the version of each tool used, along with each action performed in a workflow, are carefully recorded.
As a result, you can be confident that independent researchers can reproduce your findings in the future.
3. Standardization
Several bioinformatic approaches are available to analyze bulk RNA-seq and single-cell RNA-seq data, but there is no one size fits all solution (Conesa et al., 2016; Luecken and Theis, 2019).
Bioinformatic tools are often written in different programming languages, which makes standardization difficult.
However, commercial RNA-seq platforms offer users various robust analysis tools regardless of programming language. Users can compare each tool with numerous data visualization options and once they are satisfied with their pipeline, they can easily apply the standardized workflow to subsequent datasets.
4. Maintenance
Compared to complex pipelines reliant on code or tools which require constant maintenance, commercial software requires no maintenance.
It is always ready for your next RNA-seq analysis and allows you to spend more time exploring your data to drive novel findings.
5. Ease of use and speed
Most commercial RNA-seq analysis solutions are accessible to everyone, regardless of your bioinformatic experience.
Many platforms, such as the Omics Playground, can be tailored to your level of bioinformatic skills, from complete beginner to expert (Akhmedov et al., 2020).
For example, all analyses can be performed with a few clicks to generate publication-ready plots. This is thanks to intuitive user interfaces and interactive data visualization. Omics Playground also enables skilled bioinformaticians to offload repetitive analysis tasks to biologists while maintaining oversight on data input and quality and freeing up time to focus on more creative coding tasks.
For those with more bioinformatic knowledge, some platforms, such as Partek, offer coding options to perform more bespoke analyses or unload everyday analysis tasks.
6. Accessibility
Commercial RNA-seq data analysis software are often available as ‘software as a service’ (SaaS). This means you can access your data analysis with cloud-based apps online via a subscription rather than only on an individual computer.
This contrasts with non-SaaS software that users must install locally, as SaaS avoids the hassle of maintaining versions, continual updates, and cumbersome data and analysis sharing.
SaaS has many benefits for omics data, such as secure real-time sharing of data analyses with colleagues, instant access worldwide, and different subscription models for various requirements.
7. Cost
For research teams who generate small amounts of data, the cost of these commercial platforms may not be justified for only occasional use.
However, as researchers generate and access more and more data, a subscription to commercial RNA-seq analysis software may be more cost-effective in the long term because of the speed and consistency of analyses.
Furthermore, many platforms offer multiple subscription tiers tailored for users with different requirements. For example, BigOmics offers basic, professional, and enterprise subscriptions.
8. Learning curve
The learning curve may vary depending on your chosen RNA-seq analysis platform. For example, Omics Playground is easy to use for scientists with no coding experience but also for bioinformaticians.
Other software, such as Partek, may have a steeper learning curve if users are inexperienced with bioinformatics. However, most platforms allow the user to tailor the software to their levels of expertise.
9. Data analysis options
Most software incorporate an impressive array of analysis tools and data visualization plots.
However, commercial RNA-seq analysis software may not be the most appropriate choice if researchers require more bespoke, non-standard RNA-seq data analyses or visualizations.
Furthermore, different platforms support the analysis of different types of data. With Omics Playground, for example, you can analyze both transcriptomics and proteomics datasets. However, ChiP-seq data analysis is not supported so a different platform may be more appropriate.
Commercial versus open-source RNA-seq analysis software
An increasing number of open-source, free software perform similar data analyses and visualizations as commercial RNA-seq data analysis software.
RNA-seq data analysis tools such as iSEE, ASAP, eVITTA, and gEAR are entirely free, and all have intuitive interfaces (Gardeux et al., 2017; Rue-Albrecht et al., 2018; Orvis et al., 2021; Cheng et al., 2021).
Despite this, these tools have some distinct drawbacks.
Lack of support, maintenance and upgrades may all lead to a short app lifespan and future reproducibility issues, while types of possible analyses are often limited.
Commercial software address these issues, with the added benefits of standardization and data security measures. These are often highly desirable for regulated pharmaceutical and biotechnology companies.
Furthermore, free versions of commercial software are often available for sporadic users. With Omics Playground, for example, you can analyze up to three datasets with a basic account.
Conclusion
Commercial RNA-seq analysis software removes bottlenecks associated with the data analysis stage of transcriptomic pipelines.
They allow biotechnology and pharmaceutical companies to accelerate their research pipelines by removing time-consuming data analysis with easy-to-use, streamlined tools. The increased ease and speed of analysis gives researchers more time to interpret results to drive the decision-making process in the research environment.
These platforms use cloud computing to provide all-in-one environments for teams or worldwide collaborations while enabling everyone to better understand and contribute to the analysis steps taken as a whole.
For example, with Omics Playground, the expansive range of options available for analyzing current and past datasets avoids the effort required when using single tools for specific tasks. All tools are in one place to provide a ‘one-stop shop’ to streamline RNA-seq data analysis and generate rapid biological insight for everyone.
Your time is too valuable to be spent on routine RNA-seq and proteomics analysis. With Omics Playground you can generate figures in a few clicks and finally focus on discovery. Let us show you how!
References
Akhmedov, M. et al. (2020) ‘Omics Playground: A comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data’, NAR Genomics and Bioinformatics, 2(1), p.lqz019. Available at: https://doi.org/10.1093/nargab/lqz019.
Cheng, X., et al. (2021) ‘eVITTA: a web-based visualization and inference toolbox for transcriptome analysis’. Nucleic acids research, 49(W1), pp.W207-W215. Available at: https://doi.org/10.1093/nar/gkab366.
Conesa, A. et al. (2016) ‘A survey of best practices for RNA-seq data analysis’, Genome Biology, 17(1), pp. 1-19. Available at: https://doi.org/10.1186/s13059-016-0881-8.
Gardeux, V. et al. (2017). ‘ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data’, Bioinformatics, 33(19), pp.3123-3125. Available at: https://doi.org/10.1093/bioinformatics/btx337.
Kanwal, S. et al. (2017) ‘Investigating reproducibility and tracking provenance – A genomic workflow case study’, BMC Bioinformatics, 18(1), pp. 1-14. Available at: https://doi.org/10.1186/s12859-017-1747-0.
Kulkarni, N. et al. (2018) ‘Reproducible bioinformatics project: A community for reproducible bioinformatics analysis pipelines’, BMC Bioinformatics, 19(10), pp. 5-13. Available at: https://doi.org/10.1186/s12859-018-2296-x.
Luecken, M.D. and Theis, F.J. (2019) ‘Current best practices in single‐cell RNA‐seq analysis: a tutorial’, Molecular Systems Biology, 15(6), e8746. Available at: https://doi.org/10.15252/msb.20188746.
Orvis, J., et al. (2021). ‘gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration’. Nature methods, 18(8), pp.843-844. Available at: https://doi.org/10.1038/s41592-021-01200-9.
Rue-Albrecht, K., et al. (2018) ‘iSEE: interactive summarized experiment explorer.’ F1000Research, 7(741), p.741. Available at: https://doi.org/10.12688/f1000research.14966.1.