Development of technologies for elucidating the function of cryptic intergenic regions within genomes

This project aims to revolutionize genome annotation by creating new technologies to detect and annotate genomic features currently hidden within intergenic regions. Intergenic features are underexplored and often overlooked by popular genome annotation software despite their vital importance to genome functioning. For example, existing automatic annotation programs miss small proteins due to minimum size cutoffs and are unable to detect non-coding RNAs that are unrepresented in curated databases. These omissions leave the appearance of empty intergenic space that, in fact, contains genomic features. The problem of incomplete annotations is particularly acute for prokaryotic genomes, which are immensely diverse and heavily reliant on automated annotations. This proposal seeks to overcome current limitations, such as the difficulty of detecting ambiguous or rare features, by leveraging machine learning, comparative genomics, and new methodologies for identifying non-coding RNAs, small proteins, and regulatory elements. The proposed technologies will not only improve the accuracy of genome annotations but also enable the discovery of new genomic features that could have significant implications for biomedical research, including the identification of new drug targets. In Aim 1, we will develop a program for finding protein coding genes in prokaryotic genomes that draws upon novel signatures of true genes to improve accuracy over existing methods. In Aim 2, we will identify undiscovered non-coding RNAs by leveraging machine learning with creative signals that differentiate non- coding RNAs from intergenic space. In Aim 3, we will apply comparative genomics methods to clarify the probable function of enigmatic non-coding RNAs. Collectively, these technologies will shed light on unannotated intergenic space and democratize genome annotation by providing an advanced software for comprehensively annotating a broader range of organisms. Project Number: 1R01GM163155-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Erik Wright | Institution: BAYLOR COLLEGE OF MEDICINE, HOUSTON, TX | Award Amount: $1,920,000 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 MGG-R (90)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11275313