Biology subjects

Duitama, J.

Publications and source records attributed to Duitama, J..

12 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

Single Cell Transcriptomics of Refractory Epilepsy patients in Colombia

Maintaining electrical signaling homeostasis in the human neocortex relies on cell-type specific gene expression programs. However, when these programs are disrupted, the resulting imbalances can contribute to the pathogenesis of neurological disorders like epilepsy. Genetic factors are particularly implicated in a specific subtype of epilepsy known as refractory epilepsy or drug-resistant epilepsy (RE/DRE). This study shows the main results of the analysis of single cell transcriptomics for five pediatric RE patients in Colombia. A total of six samples obtained through surgical resection were analyzed by single-nuclei RNA sequencing (snRNA-seq). The genome of one patient was sequenced using high fidelity long-read sequencing. Functional enrichment of differentially expressed genes (DEGs) revealed glia-driven dysregulation of synaptic signaling, impaired glial-neuronal communication, and altered expression of genes related to neurotransmitter transport and calcium signaling. Activation of taste receptors in neurons was associated with neuroinflammatory processes. Structural variants were detected in genes associated with alterations of expression in specific cell types. This new data resource increases the diversity of information needed to develop new strategies for diagnosis of refractory epilepsy.

neuroscience↗

bioRxiv2025

Breast Cancer Clustering Integrating Complete Gene Expression Profiles and Genetic Ancestry

Breast cancer (BC) remains the leading cause of cancer-related mortality among women globally. Precise subtyping of BC is critical for optimizing treatment strategies. This study explored the capacity of bulk RNA- seq data to improve breast cancer characterization by analysis of complete expression profiles. We analyzed RNA-seq 274 tumor samples and six healthy tissue samples from diverse geographical origins. Using over 9,800 SNPs directly genotyped from RNA-seq data, we successfully predicted broad genetic ancestry, identifying European, African, Asian, South Asian, and Admixed American origins. Molecular subtyping through PAM50 showed ambiguous classifications for about half of the samples, underscoring the limitations of current molecular diagnostic tools. Unsupervised clustering separated tumors in three main clusters. Cluster C1 demonstrated immune activation and inflammatory response pathways, while C2 highlighted metabolic and immune interaction processes. Cluster C3 exhibited enriched metabolic regulation and adipokine signaling pathways. In silico drug sensitivity analysis identified potential therapeutic strategies, including vinorelbine and AZD6482, with cluster-specific efficacy. Our findings emphasize the integration of ancestry-informed data and complete transcriptomic profiles to redefine BC subtyping. These insights offer a foundation for more equitable, ancestry-informed therapeutic strategies and highlight the importance of diversity in cancer research.

cancer biology↗

bioRxiv2024

New algorithms for unsupervised cell clusteringfrom scRNA-seq data

The identification of cell types is a basic step of the pipeline for Single-Cell RNA sequencing data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: the high dimensional nature of the data, the sparse nature of the gene expression matrix, and the presence of technical noise that can introduce false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds a k-MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps in which each step calculates and stores the edges of minimum spanning trees over different subgraphs obtained removing edges selected in previous iterations. The Louvain algorithm is executed on the k-MST graph for cell clustering. We also explored alternatives based on neural networks in which an autoencoder is used to learn the parameters of a Gaussian mixture model, aiming to improve the handling of clusters with different shapes and sizes. Benchmark experiments with simulated data and public datasets show that the algorithms proposed in this work have competitive accuracy, compared to previous solutions, but also that sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Moreover, we performed further experiments with scRNA-data taken from a patient with refractory epilepsy. The AE-GMM model achieved the best accuracy for this dataset, and the k-MST ranked first among methods that do not require previous information on the expected number of clusters.

bioinformatics↗

bioRxiv2024

QTL Mapping for Pod Quality and Yield Traits in Snap Bean (Phaseolus vulgaris L.)

Pod quality and yield traits in snap bean (Phaseolus vulgaris L.) influence consumer preferences, crop adoption by farmers, and the ability of the product to be commercially competitive locally and globally. The objective of the study was to identify the quantitative trait loci (QTL) for pod quality and yield traits in a snap x dry bean recombinant inbred line (RIL) population. A total of 184 F6 RILs derived from a cross between Vanilla (snap bean) and MCM5001 (dry bean) were grown in three field sites in Kenya and one greenhouse environment in Davis, CA, USA. They were genotyped at 5,951 single nucleotide polymorphisms (SNPs), and composite interval mapping was conducted to identify QTL for 16 pod quality and yield traits, including pod wall fiber, pod string, pod size, and harvest metrics. A combined total of 44 QTL were identified in field and greenhouse trials. The QTL for pod quality were identified on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, and Pv07, and for pod yield were identified on Pv08. Co-localization of QTL was observed for pod quality and yield traits. Some identified QTL overlapped with previously mapped QTL for pod quality and yield traits, with several others identified as novel. The identified QTL can be used in future marker-assisted selection in snap bean.

plant biology↗

bioRxiv2023

A nearly complete and phased genome assembly of a Colombian Trypanosoma cruzi TcI strain and the evolution of gene families

Chagas is an endemic disease in tropical regions of Latin America, caused by the parasite Trypanosoma cruzi. High intraspecies variability and genome complexity have been challenges for the development of genomic variation databases, needed to conduct studies in evolution, population genomics, and identification of genomic elements related to virulence and drug resistance in T. cruzi. Here we present a chromosome-level phased assembly of a T. cruzi strain (Dm25), isolated from a reservoir of the species Didelphis marsupialis located at the Tolima department in Colombia, and belonging to the TcI DTU. We obtained a primary haplotype composed of 32 chromosomes, 30 of them assembled in a single contig, and one complete copy of the maxicircle. While 29 chromosomes show a large collinearity with the assembly of the Brazil A4 strain, three chromosomes with a high density of repeat elements show a large divergence, compared to the Brazil A4 assembly. Considering that the distribution of heterozygous sites suggest that Dm25 is diploid, we assembled a second haplotype for 31 chromosomes, achieving an average of three contigs per chromosome. Nucleotide and protein evolution statistics indicate that T. cruzi Marinkellei separated before the diversification of T. cruzi in the known DTUs. Interchromosomal paralogs of dispersed gene families and histones appeared before but at the same time have a more strict purifying selection, compared to other repeat families. Previously unreported large tandem arrays of protein kinases and histones were identified in this assembly. Over one million variants obtained from Illumina reads aligned to the primary assembly clearly separate the main DTUs. We expect that this new assembly will be a valuable resource for further studies on evolution and functional genomics of Trypanosomatids.

genomics↗

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↗

bioRxiv2022

New algorithms for accurate and efficient de-novo genome assembly from long DNA sequencing reads

Producing de-novo genome assemblies for complex genomes is possible thanks to long-read DNA sequencing technologies. However, maximizing the quality of assemblies based on long reads is a challenging task that requires the development of specialized data analysis techniques. In this paper, we present new algorithms for assembling long-DNA sequencing reads from haploid and diploid organisms. The assembly algorithm builds an undirected graph with two vertices for each read based on minimizers selected by a hash function derived from the k-mers distribution. Statistics collected during the graph construction are used as features to build layout paths by selecting edges, ranked by a likelihood function that is calculated from the inferred distributions of features on a subset of safe edges. For diploid samples, we integrated a reimplementation of the ReFHap algorithm to perform molecular phasing. The phasing procedure is used to remove edges connecting reads assigned to different haplotypes and to obtain a phased assembly by running the layout algorithm on the filtered graph. We ran the implemented algorithms on PacBio HiFi and Nanopore sequencing data taken from bacteria, yeast, Drosophila, rice, maize, and human samples. Our algorithms showed competitive efficiency and contiguity of assemblies, as well as superior accuracy in some cases, as compared to other currently used software. We expect that this new development will be useful for researchers building genome assemblies for different species.

bioinformatics↗

bioRxiv2022

Buforin II-Escherichia coli's DNA interactome: Detailed biophysical characterization revealed nanoscale complexes likely formed by DNA supercoiling

Antimicrobial peptides (AMPs) have emerged as exciting alternatives to the alarming increase of multiresistant bacteria due to their high activity against them through mechanisms that are thought to largely avoid resistance in the long term. Buforin II (BUFII) is an antibacterial peptide hypothesized to kill bacteria by crossing their membranes to interact with intracellular molecules and interrupt key processes for survival. In particular, interactions with DNA have been considered crucial for triggering cell death mechanisms. However, such interactions are still unknown, and thus far, no reports are available describing BUFII-DNA complexes. Here, we describe a complete biophysical study of the interaction between BUFII and Escherichia coli gDNA via spectrofluorimetric, spectroscopic, and microscopic techniques, complemented with whole-genome sequencing. The E. colis DNA-BUFII interactome was isolated by an in vitro pull-down method aided by BUFII-magnetite nanobioconjugates. Our results demonstrated that DNA-BUFII formed round-shape nanoscale complexes by strong electrostatic interactions, likely occurring nonspecifically throughout the entire bacterial genome. Further sequencing of the isolated DNA fragments corroborated this notion and led to hypothesize that BUFII is possibly responsible for inducing DNAs supercoiling. Other evidence for this idea was provided by the significant DNA conformational changes observed upon interaction with BUFII. Even though the evidence found fails to describe the complete action mechanism of BUFII in vivo, our findings pave the way to engineer DNA-peptide supramolecular complexes very precisely, which might find application in the field of gene therapy delivery.

biophysics↗

bioRxiv2022

NGSEP 4: Efficient and Accurate Identification of Orthogroups and Whole Genome Alignment

Whole-genome alignment allows researchers to understand the genomic structure and variations among the genomes. Approaches based on direct pairwise comparisons of DNA sequences require large computational capacities. As a consequence, pipelines combining tools for orthologous gene identification and synteny have been developed. In this manuscript, we present the latest functionalities implemented in NGSEP 4, to identify orthogroups and perform whole genome alignments. NGSEP implements functionalities for identification of clusters of homologus genes, synteny analysis and whole genome alignment, and visualization. Our results showed that the NGSEP algorithm for ortholog identification has competitive accuracy and better efficiency in comparison to commonly used tools. The implementation also includes a visualization of the whole genome alignment based on synteny of the orthogroups that were identified, and a reconstruction of the pangenome based on frequencies of the orthogroups among the genomes. Finally, our software includes a new graphical user interface. We expect that these new developments will be very useful for several studies in evolutionary biology and population genomics.

bioinformatics↗

bioRxiv2022

Loss of pod strings in common bean is associated with gene duplication, retrotransposon insertion, and overexpression of PvIND

Regulation of fruit development has been central in the evolution and domestication of flowering plants. In common bean (Phaseolus vulgaris L.), a major global staple crop, the two main economic categories are distinguished by differences in fiber deposition in pods: a) dry beans with fibrous and stringy pods; and b) stringless snap/green beans with reduced fiber deposition, but which frequently revert to the ancestral stringy state. To better understand control of this important trait, we first characterized developmental patterns of gene expression in four phenotypically diverse varieties. Then, using isogenic stringless/revertant pairs of six snap bean varieties, we identified strong overexpression of the common bean ortholog of INDEHISCENT (PvIND) in non-stringy types compared to their string-producing counterparts. Microscopy of these pairs indicates that PvIND overexpression is associated with overspecification of weak dehiscence zone cells throughout the entire pod vascular sheath. No differences in PvIND DNA methylation were correlated with pod string phenotype. Sequencing of a 500 kb region surrounding PvIND in the stringless snap bean cultivar Hystyle revealed that PvIND had been duplicated into two tandem repeats, and that a Ty1-copia retrotransposon was inserted between these tandem repeats, possibly driving PvIND overexpression. Further sequencing of stringless/revertant isogenic pairs and diverse materials indicated that these sequence features had been uniformly lost in revertant types and were strongly predictive of pod phenotype, supporting their role in PvIND overexpression and pod string phenotype. SignificanceFruit dehiscence is a key trait for seed dissemination. In legumes, e.g., common bean, dehiscence relies on the presence of fibers, including pod "strings". Selections during domestication and improvement have decreased (dry beans) or eliminated (snap beans) fibers, but reversion to the fibrous state occurs frequently in snap beans. In this article, we document that fiber loss or gain is controlled by structural changes at the PvIND locus, a homolog of the Arabidopsis INDEHISCENT gene. These changes include a duplication of the locus and insertion/deletion of a retrotransposon, which are associated with significant changes in PvIND expression. Our findings shed light on the molecular basis of unstable mutations and provide potential solutions to an important pod quality issue. Competing Interest StatementThe authors have no competing interests.

plant biology↗

bioRxiv2020

Robust and efficient software for reference-free genomic diversity analysis of GBS data on diploid and polyploid species

Genotype-by-sequencing (GBS) is a widely used cost-effective technique to obtain large numbers of genetic markers from populations. Although a standard reference-based pipeline can be followed to analyze these reads, a reference genome is still not available for a large number of species. Hence, several research groups require reference-free approaches to generate the genetic variability information that can be obtained from a GBS experiment. Unfortunately, tools to perform de-novo analysis of GBS reads are scarce and some of the existing solutions are difficult to operate under different settings generated by the existing GBS protocols. In this manuscript we describe a novel algorithm to perform reference-free variants detection and genotyping from GBS reads. Non-exact searches on a dynamic hash table of consensus sequences allow to perform efficient read clustering and sorting. This algorithm was integrated in the Next Generation Sequencing Experience Platform (NGSEP) to integrate the state-of- the-art variants detector already implemented in this tool. We performed benchmark experiments with three different real populations of plants and animals with different structures and ploidies, and sequenced with different GBS protocols at different read depths. These experiments show that NGSEP has comparable and in some cases better accuracy and always better computational efficiency compared to existing solutions. We expect that this new development will be useful for several research groups conducting population genetic studies in a wide variety of species.

bioinformatics↗