Biology subjects

Moslemi, C.

Publications and source records attributed to Moslemi, C..

4 recordsLinked to original sources

bioRxiv2026

In vivo validation of predicted fitness effects at single-base resolution in a Brachypodium distachyon mutant population

Computational tools, including biological language models (LMs), show substantial promise in predicting the impact of genetic variants on plant fitness. However, validating variant effect predictions (VEP) requires experimental populations where genetic variation consists of discrete point mutations rather than segregating recombination blocks. In this study, we generated a novel population of Brachypodium distachyon mutant lines to evaluate the accuracy of VEP at single-base resolution. These lines were advanced through single-seed descent for five generations (M1 to M5), with whole-genome sequencing performed at M2 and M5 and phenotypic measurements recorded at M3 and M4. Using state-of-the-art VEP models, we predicted the functional impact of missense protein-coding variants and gene-proximal non-coding variants. We validated these predictions by estimating the effect of mutations on whole-plant measurements (burden tests) and their probability of fixation from M2 to M5 (purging tests). Among missense variants, the protein LM ESM showed superior predictive accuracy compared to the bioinformatic standard SIFT and the genomic LM PlantCAD. Notably, the relationship between VEP scores and allele fixation suggested a log-linear relationship between VEP scores and variant fitness. Among gene-proximal variants, PlantCAD appeared more accurate than supervised models of regulatory activity, such as chromatin accessibility (a2z) and RNA abundance (PhytoExpr). Collectively, our findings highlight the utility of state-of-the-art VEP tools as predictors of fitness and demonstrate the potential of mutant populations to evaluate computational tools for precision breeding applications.

genomics↗

bioRxiv2026

Genomic language models improve cross-species gene expression prediction and accurately capture regulatory variant effects in Brachypodium mutant lines

Predicting gene expression from cis-regulatory DNA sequences at the promoter and terminator regions is a central challenge in plant genomics. This capability is also a prerequisite for assessing the effects of regulatory mutations on gene expression. Here, we developed deep learning sequence-to-expression (S2E) models that leverage context-aware sequence embeddings from the PlantCaduceus genomic language model instead of one-hot encoding of sequences, to predict gene expression across 17 plant species. To further improve predictions, we integrated chromatin accessibility data as auxiliary regulatory features. First, we evaluated our models to predict gene expression on unseen gene families via cross-validation, demonstrating our models prediction accuracy across all species outperforms PhytoExpr, the current state-of-the-art (SOTA) S2E model in plants (Pearson R=0.82 vs. R=0.74). We then validated variant effect predictions using an experimental dataset across 796 Brachypodium mutant lines, specifically designed to test predictions at single-base resolution. Our models outperformed SOTA S2E models in predicting between-gene expression differences (regression coefficient {beta}=0.78 vs. {beta}=0.57). Remarkably, they also accurately predicted the effects of single-nucleotide mutations on within-gene expression, while SOTA S2E models showed only weak associations (regression coefficient {beta}=0.38 vs. {beta}=0.08). Our results demonstrated the value of context-aware DNA sequence embeddings for predicting regulatory variant effects in plants. They also reveal a persistent accuracy gap in S2E models when moving from between-gene to allelic variation, a challenge that needs to be addressed in future S2E studies.

bioinformatics↗

bioRxiv2019

Syntenizer 3000: Synteny-based analysis of orthologous gene groups

MotivationThe amorphous nature of genes combined with the prevalence of duplication events makes establishing correct genetic phylogenies challenging.\n\nSince homologous gene groups are traditionally formed on basis of sequence similarity, both orthologs and paralogs are often placed in the same gene group by existing tools. Certain tools such as PoFF take syntenic relationship of genes into consideration when forming gene groups. However, a method to form gene groups consisting of only true syntelogs has not yet been developed.\n\nIn order to obtain orthologous gene groups consisting of the most likely syntelogs we need a method to filter out paralogs. If one strain has two or more copies of the same gene in a gene group we want to keep only the true syntelog in the group, and remove the paralogous copies by distinguishing between the two using synteny analysis.\n\nResultsWe present a novel algorithm for measuring the degree of synteny shared between two genes and successfully disambiguate gene groups. This synteny measure is the basis for a number of other useful functions such as gene neighbourhood visualisation to inspect suspect gene groups, strain visualisation for assessing assembly quality and finding genomic areas of interest, and chromosome/plasmid classification of contigs in partially classified datasets.\n\nAvailabilityThe latest version of Syntenizer 3000 can be downloaded from the GitHub repository at https://github.com/kamiboy/Syntenizer3000/\n\nConsult the manual.pdf file in the repository for instructions on how to build and use the tool, as well as a in depth explanation of the algorithms utilised.

bioinformatics↗

bioRxiv2019

The genomic architecture of introgression amongsibling species of bacteria

BackgroundGene transfer between bacterial species is an important mechanism for adaptation. For example, sets of genes that confer the ability to form nitrogen-fixing root nodules on host plants have frequently moved between Rhizobium species. It is not clear, though, whether such transfer is exceptional, or if frequent inter-species introgression is typical. To address this, we sequenced the genomes of 196 isolates of the Rhizobium leguminosarum species complex obtained from root nodules of white clover (Trifolium repens). ResultsCore gene phylogeny placed the isolates into five distinct genospecies that show high intra-genospecies recombination rates and remarkably different demographic histories. Most gene phylogenies were largely concordant with the genospecies, indicating that recent gene transfer between genospecies was rare. In contrast, very similar symbiosis gene sequences were found in two or more genospecies, suggesting recent horizontal transfer. The replication and conjugative transfer genes of the plasmids carrying the symbiosis genes showed a similar pattern, implying that introgression occurred by conjugative plasmid transfer. The only other regions that showed strong phylogenetic discordance with the genospecies classification were two small chromosomal clusters, one neighbouring a conjugative transfer system. Phage-related sequences were observed in the genomes, but appeared to have very limited impact on introgression. ConclusionsIntrogression among these closely-related species has been very limited, confined to the symbiosis plasmids and a few chromosomal islands. Both introgress through conjugative transfer, but have been subject to different types of selective forces.

genomics↗