Biology subjects

Gemayel, K.

Publications and source records attributed to Gemayel, K..

4 recordsLinked to original sources

bioRxiv2026

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide association studies are the largest source of genetic evidence for disease risk but linking non-coding variants to effector genes remains a significant barrier to identifying causal targets. Current gene-mapping approaches suffer from proximity bias, largely ignoring distal genes. Here we introduce Rosalind, a DNA foundation model fine-tuned on human genetic variation from GTEx, that directly predicts variant-gene regulatory relationships from sequence without relying on nearest-gene heuristics. We demonstrate Rosalinds accuracy through extensive benchmarking, apply it to multiple complex traits to establish broad utility, and provide experimental validation in osteoporosis using a translational osteoblast assay. We demonstrate that genes distal to osteoporosis risk variants were significantly more likely to alter a bone formation phenotype than nearest genes. Together, these results highlight deep learning-based regulatory models as a general and scalable framework for translating novel genetic insights to drug discovery.

genetics↗

bioRxiv2022

MetaGeneMark-2: Improved Gene Prediction in Metagenomes

Accurate prediction of protein-coding genes in metagenomic contigs presents a well-known challenge. Particularly difficult is to identify short and incomplete genes as well as positions of translation initiation sites. It is frequently assumed that initiation of translation in prokaryotes is controlled by a ribosome binding site (RBS), a sequence with the Shine-Dalgarno (SD) consensus situated in the 5 UTR. However, [~]30% of the 5,007 genomes, representing the RefSeq collection of prokaryotic genomes, have either non-SD RBS sequences or no RBS site due to physical absence of the 5 UTR (the case of leaderless transcription). Predictions of the gene 3 ends are much more accurate; still, errors could occur due to the use of incorrect genetic code. Hence, an effective gene finding algorithm would identify true genetic code in a process of the sequence analysis. In this work prediction of gene starts was improved by inferring the GC content dependent generating functions for RBS sequences as well as for promoter sequences involved in leaderless transcription. An additional feature of the algorithm was the ability to identify alternative genetic code defined by a reassignment of the TGA stop codon (the only stop codon reassignment type known in prokaryotes). It was demonstrated that MetaGeneMark-2 made more accurate gene predictions in metagenomic sequences than several existing state-of-the-art tools.

bioinformatics↗

bioRxiv2020

StartLink+: Prediction of Gene Starts in Prokaryotic Genomes by an Algorithm Integrating Independent Sources of Evidence

Algorithms of ab initio gene finding were shown to make sufficiently accurate predictions in prokaryotic genomes. Nonetheless, for up to 15-25% of genes per genome the gene start predictions might differ even when made by the supposedly most accurate tools. To address this discrepancy, we have introduced StartLink+, an approach combining ab initio and multiple sequence alignment based methods. StartLink+ makes predictions for a majority of genes per genome (73% on average); in tests on sets of genes with experimentally verified starts the StartLink+ accuracy was shown to be 98-99%. When StartLink+ predictions made for a large set of prokaryotic genomes were compared with the database annotations we observed that on average the gene start annotations deviated from the predictions for ~5% of genes in AT-rich genomes and for 10-15% of genes in GC-rich genomes.

bioinformatics↗

bioRxiv2017

Improved Prokaryotic Gene Prediction Yields Insights into Transcription and Translation Mechanisms on Whole Genome Scale

In a conventional view of the prokaryotic genome organization promoters precede operons and RBS sites with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of pre-computed heuristic models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as non-canonical RBS patterns. To assess the accuracy of GeneMarkS-2 we used genes validated by COG annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of [~]5,000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts.\n\n[Supplemental material is available for this article].

genomics↗