In RNA-Seq and microarray analysis, GO analysis and pathway analysis are often performed after extracting differentially expressed genes. For many years, these methods have been used as representative tools for biological interpretation at the end of omics data analysis.
However, this way of using GO and pathway analysis may rapidly become outdated.
This does not mean that GO analysis or pathway analysis will disappear. In papers and formal reports, tables of GO terms and pathways will likely continue to be used. However, the process of reading those tables one by one and building a biological story from them is likely to be replaced by AI-based summarization and comparison with the literature.
AI Will Start Reading GO and Pathway Analysis Results
Until now, researchers have reviewed tables of GO and pathway analysis results, selected terms that seemed important, and interpreted them by comparing them with papers and existing biological knowledge.
With AI, however, long lists of term names, gene lists, expression patterns, sample information, disease names, cell types, experimental conditions, and existing literature can be combined and summarized in a more readable form.
For example, please see the video below.
In the video above, only cluster-specific gene lists are provided to AI. At present, however, it is often more useful to provide AI with the GO and pathway analysis results for each cluster as well, and to proceed with interpretation through an interactive dialogue. This makes it easier to organize the results in a way that reflects the biological context. In the future, AI may perform the necessary enrichment analysis directly from gene lists and support interpretation based on those results.
As this type of workflow becomes more common, the practice of manually reading GO and pathway analysis tables one by one will gradually become less central in day-to-day analysis.
Even So, GO and Pathway Analysis Will Remain
On the other hand, dialogue with AI has a reproducibility problem.
Even if the same gene list is given to AI, the explanation may change depending on the AI model, the prompt, the information being referenced, and the way the question is asked. Even when using the same AI system, the exact same answer is not always returned.
For this reason, in papers and official reports, it is still necessary to record, in a reproducible form, which database was used, which statistical method was applied, and which GO terms or pathways were significant.
Tables from GO analysis and pathway analysis will likely remain necessary as reproducible supporting evidence in papers and reports.
A Split May Emerge: Tables in Papers, AI in Practice
If we only read papers in the future, GO analysis and pathway analysis may appear to be used in the same way as before.
However, in actual analysis practice, AI will likely be used more and more for deciding how to read those tables, which terms should be emphasized, and what kind of biological story can be built from the results.
In other words, for those learning RNA-Seq data analysis from now on, it will be important not only to learn how to read GO and pathway analysis tables, but also how to ask questions to AI and how to handle the answers it provides.
AI-generated answers can contain errors. Even when an explanation sounds plausible, the supporting evidence may be weak when checked against the original data or the literature.
Therefore, what matters is not to believe AI uncritically. What matters is to become familiar with using AI, and to check its answers by returning to the original data and the literature.
AI-Assisted Interpretation Must Be Checked Against the Original Data
AI can summarize GO and pathway analysis results. However, it does not automatically guarantee the quality of the original data.
Do the extracted genes truly reflect differences between sample groups? Are the results affected by variation among low-expression genes? Are outlier samples or batch effects influencing the result? Do normalization or filtering conditions greatly change the result?
If these points are not checked before asking AI to summarize the results, AI may simply return a plausible-sounding explanation.
What will matter in the AI era of omics analysis is not to accept AI-generated answers as they are. The important skill will be the ability to check AI-assisted interpretations against the original expression data, PCA, clustering, heatmaps, sample information, and gene-by-gene expression patterns.
Conclusion
The era of treating GO and pathway analysis as the final goal is coming to an end. In the future, omics interpretation will increasingly be carried out together with AI, while checking the results against the original data.
