When choosing RNA-Seq analysis software, many people first check whether it can calculate expression values from FASTQ files, perform differential expression analysis, create volcano plots and heatmaps, and proceed to GO analysis and pathway analysis.
Of course, these are important functions. However, analysis requires more than that. It is also necessary to check whether the obtained results are reliable, rather than artifacts caused by data distortion or batch effects, and whether the analysis is extracting expression patterns that match the research objective.
With AI-based code generation and browser-based automated analysis tools where users can run analyses simply by clicking a button, the barrier to executing RNA-Seq analysis is likely to become even lower. However, making analysis easier to execute does not mean that the results are correct. On the contrary, pipelines that simply run well-known tools without checking the nature of the data, or without considering whether the research objective matches the assumptions of the statistical model, may increase the risk of producing large numbers of plausible-looking results.
AI will further accelerate the automation of RNA-Seq analysis
Until recently, performing RNA-Seq analysis required learning Linux commands, R, Python, statistical methods, and how to use various tools.
Today, however, AI can generate commands for running Salmon or kallisto, R code for creating a gene-level counts table with tximport, scripts for calculating p-values with edgeR or DESeq2, and code for creating volcano plots and heatmaps.
This change will further accelerate the automation of RNA-Seq analysis. Tasks that used to be major barriers, such as writing code or running tools, will become less difficult than before.
However, it is important to remember that being able to generate code is not the same as being able to perform a correct analysis. AI-generated code can execute the requested procedure. But it does not automatically determine whether that procedure is appropriate for the data.
Real RNA-Seq data are not as clean as statistical models assume
Statistical methods used in RNA-Seq analysis assume that the data satisfy certain conditions. For example, the data are assumed to be normalized in a way that allows samples to be compared, variation in the low-count region is assumed to be handled appropriately, and extreme outliers or strong batch effects are assumed not to dominate the results.
In reality, however, RNA-Seq data are not always as clean and well-behaved as statistical models assume. RNA quality, sample amount, cellular composition, library preparation, sequencing depth, mapping rate, variation among low-expression genes, outliers, batch effects, and many other factors can affect the results.
In particular, RNA-Seq data obtained from public databases, biopsy-derived samples, low-input RNA-Seq, or samples with different cellular compositions often show large differences in data quality and distribution between samples. For such data, it is essential to examine the state of the data before applying statistical methods.
Even so, if data are simply given to AI-generated code or automated analysis tools, plausible-looking results can still be obtained. The problem is that, from the output alone, it is not possible to tell whether those results truly reflect biological differences, or whether they reflect data quality issues or distributional bias.
DEG analysis is not as simple as choosing edgeR, DESeq2, or limma
In RNA-Seq differential expression analysis, methods such as edgeR, DESeq2, and limma are widely used. These are important statistical methods, and they have played a major role in RNA-Seq analysis. However, real RNA-Seq data often contain factors that can lead to missed findings or misinterpretation if statistical models are applied without checking the data.
Therefore, choosing one well-known method does not always lead to the correct list of differentially expressed genes. DEG analysis is not simply a matter of calculating p-values.
In practical analysis, the expression patterns you want to find depend on the research objective.
Do you want to identify genes with large changes?
Do you want to find low-expression genes that show ON/OFF-like changes under specific conditions?
Do you want to detect stable changes with small within-group variance?
Do you want to examine changes in a paired design while accounting for individual differences?
Depending on the objective, the region of the data to examine and the appropriate method can change.
Therefore, in DEG analysis, it is necessary to consider which expression patterns you want to extract according to the research objective, and to combine preprocessing, filtering, statistical methods, and visual inspection accordingly.
RNA-Seq analysis software needs more than the ability to generate results
When choosing RNA-Seq analysis software, you should look not only at which analyses can be run automatically, but also at how the data can be examined.
For example, it is important to be able to check the distribution before and after normalization, the state of the low-count region, differences in dynamic range between samples, outliers visible in PCA or clustering, expression patterns visible in heatmaps, and the distribution of genes extracted by differential expression analysis.
For differential expression results as well, it is not enough to simply list genes with small p-values. You need to check how strongly those genes are expressed, which samples show the changes, whether the results are affected by variation in the low-count region, and whether the expression patterns are biologically interpretable.
A practical workflow combines automation with data inspection
Of course, automation itself is not the problem. FASTQ file processing, creation of expression tables, p-value calculation with edgeR or DESeq2, and graph generation can be made more efficient by using AI or existing tools. Automating routine processes can be a great help in moving the analysis forward.
The problem is treating the output of automated processing as the conclusion without comparing it with the state of the data and the research objective.
In practical settings where large amounts of data need to be processed, it is necessary to obtain results efficiently through automated processing, while also visualizing and checking those results and judging whether they are consistent with the research objective.
Points to check when choosing RNA-Seq analysis software
In addition to standard analysis functions, we recommend checking whether the software allows you to examine the following points.
- Whether Gene Counts, TPM, FPKM, and RPKM can be handled separately and appropriately
- Whether the data distribution can be checked before and after normalization
- Whether you can check if a specific expression pattern reflects data distortion
- Whether the state of the low-count region and missing values can be examined
- Whether differential expression results can be checked as expression patterns, not only as p-values
- Whether GO analysis and pathway analysis results can be connected back to the original data
- Whether results obtained from other analysis tools can be compared and integrated
- Whether analysis methods and extraction criteria can be revised according to the research objective
Subio Platform is RNA-Seq analysis software for examining data and making informed decisions
Subio Platform is RNA-Seq analysis software that can import expression data such as Gene Counts, TPM, and FPKM, and allows users to proceed with normalization, filtering, PCA, clustering, heatmaps, checking differential expression results, GO analysis, and pathway analysis without programming.
What Subio Platform emphasizes is carefully proceeding through statistical analysis and biological interpretation while checking the state of the data.
It is also important to review, share, and re-analyze results
In RNA-Seq analysis, it is also important not to let an analysis result end as a one-time output. Results should be reviewable later, shared with others, and re-analyzed under different conditions. If analysis steps and display states can be saved together, it becomes easier to share results within a lab or for another researcher to re-examine the same data.
With Subio Platform, analysis data can be accumulated in a reusable form and shared as an SSA file, so that the same analysis result can be opened immediately for review and re-analysis. Turning analysis data into reusable assets and sharing them in this way is important not only for individual analysis work, but also for improving the analytical capability of the entire laboratory. This point is discussed in What You Can Do Without Plug-ins: The Free Basic Features of Subio Platform for Omics Analysis .
Subio Platform is an analysis environment for carefully proceeding with RNA-Seq analysis while checking the state of the data, sharing and reusing the obtained results, and using them as a basis for the next analysis or validation.