Computational identification and characterization of noncoding RNA-encoded peptides: tools, databases, and in silico strategies.
Noncoding RNAs (ncRNAs) used to be written off as biological noise. Now, they’re front and center in peptide research. Scientists are using computational tools to hunt down ncRNA-encoded peptides (ncPEPs)—tiny peptides hidden in stretches of RNA once thought to do nothing.
Amino Acids
by Shenoy N, Karkare A, Gangadhar V et al.
“Computational identification and characterization of noncoding RNA-encoded peptides: tools, databases, and in silico strategies. Shenoy N(1), Karkare A(1), Gangadhar V(1), Padubidri SR(1), Rao S(1), Dsouza LA(1), Joshi MB(2), Damerla RR(3), Gandhi NS(4), Mallya S(5). Author information: (1)Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India. (2)Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India. (3)Department of Medical Genetics, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India. (4)Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. (5)Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India. sandeep.mallya@manipal.edu. Once dismissed as transcriptional artifacts, noncoding RNAs (ncRNAs) have gained recognition in recent years for their ability to participate in gene regulation, as well as their ability to encode functional molecules referred to as ncRNA-encoded peptides (ncPEPs). The discovery of ncPEPs has opened new avenues in proteomics and genomics research, revealing biological mechanisms that were previously unexplored. This review presents an extensive overview of the computational tools, databases, and in silico strategies used to identify ncRNA-encoded peptides across all major ncRNA classes, including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and primary microRNAs (pri-miRNAs). Furthermore, we outline publicly available databases that compile experimentally validated and computationally predicted ncPEPs across multiple species, enabling systematic annotation and cross-referencing of candidate peptides. By highlighting the current challenges and emerging methodologies, we emphasize how computational methods continue to advance our ability to uncover hidden functional peptides within the noncoding transcriptome. These developments provide a framework for validating ncPEPs and elucidating their biological significance across diverse systems. © 2026. The Author(s). Conflict of interest statement: Declarations. Conflict of interest: The authors declare that they have no competing interests. Ethical approval: The present study is a review; therefore, it does not require approval from the ethical committee/scientific body.”
A new review out of Manipal Academy takes a hard look at the latest in silico strategies for discovering these ncPEPs. The paper breaks down the expanding toolkit for peptide researchers: machine learning models, advanced sequence analysis, and comprehensive databases that compile both predicted and experimentally verified ncPEPs from lncRNAs, circRNAs, and even primary microRNAs.
Why does this matter? These ncRNA-encoded peptides are showing up everywhere—across multiple species and biological systems. They’re not just curiosities. They’re turning out to have real biological functions, from regulating gene expression to influencing cell behavior. Computational pipelines are making it easier to annotate, cross-reference, and prioritize which ncPEPs are worth a closer look in the lab.
Key takeaway: The field is moving fast. Here’s what’s powering the acceleration:
New public databases catalog thousands of candidate ncPEPs for researchers to mine.
Algorithms are getting sharper at filtering real peptides from background noise.
Cross-species comparisons are helping researchers spot conserved, likely functional ncPEPs.
The computational side is doing the heavy lifting up front, so wet-lab researchers can focus on validating the most promising hits. If you’re looking for a map of current tools and resources, this review is a solid starting point.
For more on general peptide research, check the peptide research index. The future of peptide discovery is looking a lot less random and a lot more data-driven.
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