Biology subjects

Gaitan, N.

Publications and source records attributed to Gaitan, N..

3 recordsLinked to original sources

bioRxiv2026

Genic Position and Methylation Context Shape DNA Methylation-Expression Relationships in Rice Internode Development

Elongating rice internodes present a developmental gradient from dividing meristem to mature cells, providing an elegant pseudo-time course for study of plant vegetative development. We tested the hypothesis that DNA methylation regulates gene expression during rice internode development by integrating RNA-seq and bisulfite DNA sequencing across eight internode segments. Previously described topologically associated chromatin domain borders aligned with transcription start sites of constitutive expressed genes. CpG and CHG differential methylation was enriched in young segments, consistent with maintenance methylation; whereas CHH methylation showed similar differential abundance in young and old segments. CHH and CHG methylation in upstream regions, CpG methylation within gene bodies, and any methylation in 5' and 3' untranslated regions were permissive of moderate to high gene expression. Very low expression was associated with CpG methylation upstream, CHG and CHH methylation within gene bodies, and CpG and CHG methylation downstream. A nonrandom subset of genes, including cell wall-related glycoside hydrolases, lignin and tricin biosynthesis enzymes, and WD40 proteins, showed methylation-expression correlations, with expression changes enriched in triple-marked elements. These results suggest that internode phenotypes of DNA methylation machinery mutants relate to alteration of specific target genes, opening approaches for grass culm improvement for lodging resistance and biomass production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/737558v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@b726eaorg.highwire.dtl.DTLVardef@5bd017org.highwire.dtl.DTLVardef@1bac873org.highwire.dtl.DTLVardef@1be3474_HPS_FORMAT_FIGEXP M_FIG C_FIG Significance statementStems of cereals and other grasses have an important function in supporting grain production and serving as a source of carbon for the bioeconomy. This study supports methylation of the cytosine bases of DNA as a regulator of chromatin structure and gene expression in the stems of rice. Different methylation contexts are implicated in specific roles in both positive and negative gene expression regulation that might be leveraged to improve stem properties.

plant biology↗

bioRxiv2026

Anonymized Somatic Tumor Twins (STTs) enable open genome data sharing and use in research and clinical oncology

The study of somatic variants from tumor genomes is fundamental to cancer research and clinical decision-making. However, existing data protection frameworks impose restrictions on the use and sharing of these variants in conjunction with sensitive germline information. To overcome these challenges, we developed GenomeAnonymizer, the first method to anonymize short-read DNA sequences from tumor-normal pairs. This generates Somatic Tumor Twins (STTs), an anonymized version of the original data that preserves the donors privacy while retaining somatic tumor information and sequencing noise. This method successfully removed all detectable germline variants from the 47 PCAWG-Pilot samples. We further demonstrate that Whole-Genome Sequencing (WGS) STTs preserve more than 98% of the original somatic variants, enabling reliable downstream analysis that replicates somatic-related findings from the original samples, including cancer driver genes, mutational signatures, and intratumor heterogeneity. Importantly, we also show that STTs can reproduce the identification of actionable genes and downstream clinical interpretations and decision-making. We generated a cancer cohort of STTs matched with synthetic clinical data that could be openly shared and used across projects and centers worldwide. This paradigm-shifting approach will accelerate discovery and clinical translation in oncology and enable the robust benchmarking of genome analysis and large-scale data infrastructures.

bioinformatics↗

bioRxiv2022

A graph clustering algorithm for detection and genotyping of structural variants from long reads

Structural variants (SV) are polymorphisms defined by their length (>50 bp). The usual types of SVs are deletions, insertions, translocations, inversions, and copy number variants. SV detection and genotyping is fundamental given the role of SVs in phenomena such as phenotypic variation and evolutionary events. Thus, methods to identify SVs using long read sequencing data have been recently developed. We present an accurate and efficient algorithm to predict SVs from long-read sequencing data. The algorithm starts collecting evidence (Signatures) of SVs from read alignments. Then, signatures are clustered based on a Euclidean graph with coordinates calculated from lengths and genomic positions. Clustering is performed by the DBSCAN algorithm, which provides the advantage of delimiting clusters with high resolution. Clusters are transformed into SVs and a Bayesian model allows to precisely genotype SVs based on their supporting evidence. This algorithm is integrated in the single sample variants detector of the Next Generation Sequencing Experience Platform (NGSEP), which facilitates the integration with other functionalities for genomics analysis. For benchmarking, our algorithm is compared against different tools using VISOR for simulation and the GIAB SV dataset for real data. For indel calls in a 20x depth Nanopore simulated dataset, the DBSCAN algorithm performed better, achieving an F-score of 98%, compared to 97.8 for Dysgu, 97.8 for SVIM, 97.7 for CuteSV, and 96.8 for Sniffles. We believe that this work makes a significant contribution to the development of bioinformatic strategies to maximize the use of long read sequencing technologies.

bioinformatics↗