Biology subjects

Gkanogiannis, A.

Publications and source records attributed to Gkanogiannis, A..

4 recordsLinked to original sources

bioRxiv2025

Uncovering the Genetic Blueprint of the UAE's National Tree: Genomic Evidence to Guide Prosopis cineraria (L.) Druce Conservation

Prosopis cineraria (L.) Druce is a keystone tree species of the arid and semi-arid regions of the West and South Asia, with critical ecological, cultural, and consevation significance. In the United Arab Emirates (UAE) and other regions of the Arabian Peninsula this beneficial tree is called Ghaf. Despite its importance, genomic resources and population-level diversity data for the tree remain limited. Here, we present the first comprehensive population genomics study of Ghaf, based on whole-genome re-sequencing of 204 individual trees collected across the UAE. Following Single Nucleotide Polymorphism (SNP) discovery and stringent filtering, we analyzed 57, 183 high-quality LD-pruned SNPs to assess population structure, diversity, and gene flow. Principal component analysis (PCA), sparse non-negative matrix factorization (sNMF), and discriminant analysis of principal components (DAPC) revealed four well-defined genetic clusters, broadly corresponding to geographic origins. Genetic diversity varied significantly among groups, with observed heterozygosity (Ho), inbreeding coefficients (F), and nucleotide diversity ({pi}) showing strong population-specific trends. Genome-wide Fixation index F ST scans identified multiple highly differentiated genomic regions, enriched for genes involved in stress response, transport, and signaling. Functional enrichment using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam annotations indicated overrepresentation of protein kinase activity, ATP binding, and hormone signaling pathways. TreeMix analysis revealed gene flow into one of the genetic clusters from both others, suggesting historical admixture and geographic connectivity. To support data interpretation and stakeholder engagement, we developed two web-based tools for interactive visualization of sample geolocation and genetic structure. This work provides foundational insights into the genetic landscape of P. cineraria (L.) Druce, supporting conservation planning and long-term monitoring in the region.

genomics↗

bioRxiv2024

Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations

MotivationGenotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., "noisy" data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. AvailabilityNOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags.

bioinformatics↗

bioRxiv2023

Nuclear MTHFD2 secures mitosis progression by preserving centromere integrity

Subcellular compartmentalization of metabolic enzymes may elicit specific cellular functions by establishing a unique metabolic environment. Indeed, the nuclear translocation of certain metabolic enzymes is required for epigenetic regulation and gene expression control. Here, we reveal that, in cancer cells, the mitochondrial enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) localizes in the nucleus during the G2-M phase of the cell cycle to secure mitosis progression. Nuclear MTHFD2 interacts with proteins involved in mitosis regulation and centromere stability, including the methyltransferases KMT5A and DNMT3B. Loss of MTHFD2 induces centromere overexpression and severe methylation defects and impedes correct mitosis completion. As a consequence, MTHFD2 deficient cells accumulate chromosomal aberrations arising from chromosome congression and segregation defects. Blocking the catalytic nuclear function of MTHFD2 recapitulates the phenotype observed in MTHFD2 deficient cells, attributing to nuclear MTHFD2 an enzymatic active role in controlling mitosis. Our discovery uncovers a nuclear moonlighting role for the cancer target MTHFD2, and emphasizes that cancer metabolism rewiring may encompass the relocation of metabolic enzymes to alternative subcellular compartments.

cell biology↗

bioRxiv2019

NOISYmputer: genotype imputation in bi-parental populationsfor noisy low-coverage next-generation sequencing data

MotivationLow-coverage next-generation sequencing (LC-NGS) methods can be used to genotype bi-parental populations. This approach allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints, and minimize mapping intervals for quantitative-trait locus analysis.\n\nThe main issues with these genotyping methods are (1) poor performance at heterozygous loci, (2) a high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself.\n\nRecent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e. \"noisy\" data). Here, we present an algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomic regions, corrects erroneous data, and imputes missing data. We compare its performance with Tassel-FSFHap, LB-Impute, and Genotype-Corrector using simulated data and three real datasets: a rice single seed descent (SSD) population genotyped by genotyping by sequencing (GBS) by whole genome sequencing (WGS), and a sorghum SSD population genotyped by GBS.\n\nAvailabilityNOISYmputer, a Microsoft Excel-Visual Basic for Applications program that implements the algorithm, is available at mapdisto.free.fr. It runs in Apple macOS and Microsoft Windows operating systems.\n\nSupplementary files: Download link

bioinformatics↗