Proteomics has the incredible potential to reveal the mechanisms underlying biology. In doing so, it may provide the raw materials needed to develop the next generation of more effective biomarkers, diagnostics, and drug targets. In short, proteomics can revolutionize biomedicine.
However, without the help of AI, achieving these aims will be slow-going. In this blog series, we discuss:
- Why proteomics needs AI, and why we need better proteomic data to train models of biology
- How Nautilus data is designed to be well-suited for AI integration
- How Nautilus’ AI-ready data may improve multiomic analyses
Today’s post covers how Nautilus’ AI-ready data may improve multiomic analyses.
Learn more about AI and biotech on the Translating Proteomics podcast.
Multiomics, AI, and the Nautilus Platform
Multiomics is the study of biology through the integration of genomic, transcriptomic, proteomic, and any other ‘omic data. Multomic efforts provide the most complete views of biology achievable, but it can be difficult to integrate proteomic data generated by today’s technologies into these studies – even using AI.
Genomics and transcriptomics technologies are mature. They provide highly accurate measurements of well-defined nucleic acid sequences that are suited for AI analysis. Proteomics technologies, on the other hand, lag in terms of the depth of information about each molecule and confidence in abundance measurements. Researchers can use multomic methods to correlate changes in nucleic acid sequences to changes in “protein” levels, but neither the specific molecules impacted, nor the degree of impact are as clear as they are in genomics and transcriptomics. Even if there are changes to protein-coding genes or transcripts detected by other technologies, most proteomic technologies today cannot detect the expected changes in protein sequence.
At an even simpler level, naming conventions across the ‘omes are not well standardized. Thus, it’s hard to assess gene, transcript, and protein correlations from an informatic perspective. Overall, proteomic data may be too messy for AI-based techniques to parse important connections between the ‘omes accurately or efficiently.
Single-molecule proteomics data generated by Iterative Mapping on the Nautilus Platform should alleviate many of these issues. This is because Iterative Mapping is designed to accurately reveal what protein molecules are impacted by genomic and transcriptomic alterations. The platform should also provide more accurate and precise measurements of the scale of these impacts.
In addition, because Iterative Mapping involves the repeated interrogation of intact protein molecules with diverse probes, it should enable researchers to incorporate probes that detect the protein sequence changes expected from genomic and transcriptomic data. It should also be able to assess the prevalence of these protein sequence changes at scale. In this way, the Nautilus Platform will hopefully enable researchers to leverage AI and establish far clearer connections between the omes.
Finally, at Nautilus we are keenly aware of the need to standardize proteomic data and believe single-molecule technologies like ours give researchers the unique opportunity to fully define the single molecules they detect in a standardized way that can be easily associated with genomic and transcriptomic data. In fact, the scale of data we aim to generate demands it. As such, we are currently working with leaders in proteomics to establish standards for single-molecule protein measurements. Such initiatives should further improve the AI-based analysis and interpretation of mutliomic data by making it possible to consistently associate changes in well-defined DNA, RNA, and protein molecules and not nebulous entities like “proteins” or “protein groups.”
The future of AI and biology
We have designed our platform with AI in mind. In doing so, we hope the data it generates will be used to train models that identify next-generation biomarkers, drug targets, and diagnostics. Being trained with uniquely high-quality data at scale, we hope these models will make biology more efficient by making less follow-up work necessary and will ultimately make drug development far more successful. Truly, we believe that the data generated by our platform will revolutionize biomedicine.
Contact us to learn how you can employ Iterative Mapping in your research.
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