Biology subjects

Pham, T. M.

Publications and source records attributed to Pham, T. M..

13 recordsLinked to original sources

bioRxiv2026

A Practical Framework for Constructing Population-Specific and Alternate-Contig-Aware Genome References: A case study of Vietnam

Current studies in human genomics typically rely on the standard genome reference GRCh38 which is known to be biased toward populations of European ancestry and therefore has limitations when applied to other populations. Although various graph-based pangenome references were constructed for several populations to deal with this bias, their usage in practice is currently still limited compared to linear genome references. Here we present a framework for constructing a population-specific genome reference using GRCh38 as backbone with alternate-contig awareness to enhance genomic data analysis in the target population. We demonstrated the advantages of our framework using both public and in-house Vietnamese whole-genome sequencing (WGS) datasets. Genomic variants derived from high-coverage WGS data of the 1000 Vietnamese Genomes Project (VN1K) were imported into our framework to build a Vietnamese-specific Genome Reference (VGR). VGR was then compared to GRCh38 in read alignment and variant calling using high-coverage WGS data of 99 Vietnamese individuals (KHV) from the 1000 Genomes Project (1kGP). Using Omni array genotyping data from 99 KHV samples as an independent benchmark, we found that VGR improved variant-calling precision and reduced false-positive calls compared to GRCh38. Our framework could be easily used for other populations as long as they have a variant database similar to VN1K. Our code is publicly available at github.com/VinGenome/VGR

genomics↗

bioRxiv2026

kmerRRR: A k-mer based tool for functional genomics in Repeat Rich Regions

Highly repetitive sequences pose problems for genome assembly and analysis. While advances in long-read sequencing technologies have helped reveal the organization of repetitive genomic sequences at unprecedented resolution, their functional characterization remains difficult because molecular assays that probe protein-DNA interactions and characterize expression often rely on short read sequencing. The repetitive nature of these regions poses major challenges for methods relying on sequence mapping, which is exacerbated for short reads. Repetitive genome regions often have low mappability, leading to substantial information loss during downstream filtering. To address this challenge, we developed a bioinformatic tool--kmerRRR--that leverages k-mer frequency analyses to enhance the mappability of repetitive regions. KmerRRR compares k-mer frequencies within user-defined loci to their frequencies across the genome to identify repetitive sequences that are overrepresented locally relative to the global background. This approach quantifies locus uniqueness, allowing users to distinguish sequences that are globally repetitive from those that are repetitive, but restricted to specific genomic loci. We demonstrated the utility of this method by reanalyzing chromatin profiling data from human, Drosophila, and Arabidopsis centromeres and small RNA sequencing data. Our results show that incorporating local k-mer ratio information enhances read retention and signal interpretation within repetitive regions, thereby recovering biologically meaningful information that is typically lost in conventional analyses. The tool is freely available under MIT license in github: (https://github.com/LarracuenteLab/kmerRRR).

genomics↗

bioRxiv2025

Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis

MotivationWe propose DRIFT, a framework that integrates spatial context into the input representations for foundation models by leveraging diffusion on spatial graphs derived from spatial transcriptomics (ST) data. ST captures gene expression profiles while preserving spatial context, enabling downstream analysis tasks such as cell-type annotation, clustering, and cross-sample alignment. However, due to its emerging nature, there are very few foundation models that can utilize ST data to generate embeddings generalizable across multiple tasks. Meanwhile, well-documented foundational models trained on large-scale single-cell gene expression (scRNA-seq) data have demonstrated generalizable performance across scRNA-seq assays, tissues, and tasks; however, they do not leverage the spatial information in ST data. We use heat kernel diffusion to propagate embeddings across spatial neighborhoods, incorporating the local neighborhood context of the ST data while preserving the transcriptomic representations learned by state-of-the-art single-cell foundation models. ResultsWe systematically benchmark five foundational models (both scRNA-seq and ST-based) across key ST tasks such as annotation, alignment, and clustering, ensuring a comprehensive evaluation of our proposed framework. Our results show that DRIFT significantly improves the performance of existing foundational models on ST data over specialized state-of-the-art methods. Overall, DRIFT is an effective, accessible, and generalizable framework that bridges the gap toward universal models for modeling spatial transcriptomics. Availability and ImplementationCode and data available at https://github.com/rsinghlab/DRIFT. Contactritambhara@brown.edu Supplementary informationSupplementary notes are provided with the manuscript.

bioinformatics↗

bioRxiv2025

SCOT+: A Comprehensive Software Suite for Single-Cell alignment Using Optimal Transport

SummaryNew advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport+ (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ has a generic optimal transport solution that can be reduced to multiple different OT optimization procedures, each of which provide state-of-the-art single-cell alignment performance. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Implementation and AvailabilityOur algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/).

bioinformatics↗

bioRxiv2025

VN1K: a genome graph-based and function-driven multi-omics and phenomics resource for the Vietnamese population

Vietnam, the 16th most populated nation, remains profoundly underrepresented in global genomic databases. Here, we present VN1K, a first-ever comprehensive and well-curated resource of multi-omics data with a wide-range of phenotypic information of 1,011 unrelated Vietnamese individuals. High-depth short-read whole-genome sequencing data were generated for all samples along with various - omic data, including microarray, long-read whole-genome sequencing, and RNA sequencing. Using a high-sensitivity variant detection pipeline, which included a pangenome graph reference and a deep-learning framework, we identified nearly 40 million variants of which 8.5 million are novel with nearly 900 thousand short insertions/deletions and 39 thousand structural variants. Specifically, VN1K featured a first-ever whole-genome methylation profile based on long read sequencing. A genotype imputation panel was also created with the highest accuracy on the Vietnamese population. Variants with significantly different allele frequencies in the Vietnamese population compared to others were found to be functionally significant, especially in genes associated with immune diseases (HLA-B, KIR3DL3, KIR2DL1, KIR2DL4) or drug responses (CYP2C19, CYP2D6, VKORC1, CYP2B6). We were also able to map various loci related to hepatitis B virus infection as well as six disease traits, including triglyceride levels, LDL-C, serum glucose levels, HbA1c, and levels of two liver enzymes (ALT and AST). VN1K dataset is accessible via genome.vinbigdata.org, an integrated platform with both linear and graph-based genome browser for facilitating data exploration, research, and applications in precision medicine.

genomics↗

bioRxiv2025

Functional microRNA targeting without seed pairing

MicroRNAs (miRNAs) associate with Argonaute (AGO) proteins to serve as guides, directing binding to partially complementary sites in mRNAs, ultimately causing post-transcriptional repression. Complementarity to the miRNA seed region (miRNA nucleotides 2-7) is typically both necessary and sufficient for repression. Here, we investigate unusual sites with extensive complementarity to the miRNA 3' region (nucleotide 9 and onwards) but without complementarity to the seed. Top 3'-only sites bind as well as top canonical sites and impart similar repression, which can be further boosted by as few as 2-3 additional pairs to the miRNA seed. Despite these similarities, 3'-only sites have slower association and dissociation rates than seed-matched sites. They also impart different conformations to bound AGO-miRNA complexes than do seed-matched sites, and individual miRNAs differ substantially with respect to how well they bind their respective 3'-only sites. Thus, pairing to the seed is not required for binding and repression, or for a target to gain access to the 3' region of the guide. Overall, for those miRNAs which recognize 3'-only sites, those sites are estimated to constitute 0.5-1% of the endogenous targetome, a proportion resembling that of other rare but functional site types, such as 3'-compensatory sites. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/641270v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1e6045corg.highwire.dtl.DTLVardef@2859eborg.highwire.dtl.DTLVardef@12761beorg.highwire.dtl.DTLVardef@820fe6_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2024

Synaptic gene expression is coordinated across development by conserved chromatin regulators

Synapses are the primary sites of communication in the nervous system. Synapse formation requires the simultaneous expression of hundreds of proteins across cells. Through genomic and functional studies in Drosophila, we investigated the regulatory mechanisms underlying the spatiotemporal coordination of synaptic gene expression during neurodevelopment. We find that chromatin opening is coordinated across hundreds of synaptic genes immediately preceding transcription, whereas repression of these genes outside developmental periods of synaptogenesis is regulated by distinct mechanisms. We identify the conserved DEAF1 transcription factor (TF) as a direct repressor of synaptic genes. Disruption of DEAF1 or pioneer TF CLAMP, which promotes DEAF1 binding, increases synaptic gene expression across neuronal subtypes, leading to excess synapse formation. DEAF1, which is linked to syndromic intellectual disability, is also sufficient to block synaptogenesis. These findings reveal the importance of coordinated synaptic gene repression in sculpting neuronal connectivity and broaden our understanding of gene regulation in neurodevelopment.

neuroscience↗

bioRxiv2024

Tensor Kernel Learning for Classification of Alzheimer's Conditions using Multimodal Data

Early, timely, and accurate assessment of Alzheimers disease (AD), particularly at its earlier stage - mild cognitive impairment (MCI) -, is central to detecting, managing, and potentially treating the disease. The biological underpinnings of AD, however, is multifaceted, from genetic variations, abnormal protein accumulation, to irregular brain functions and structure. A joint analysis of these data, therefore, may offer potentially new insights about AD-related biomarkers and AD prediction. But such explorations must confront the complexity of these data: heterogeneity, multimodality and high-dimensionality. Here, to address these challenges, we propose a new machine-learning method, namely the tensor kernel learning (TKL), leveraging tensor methods and kernel learning, to enhance AD assessment by enhancing multi-modal data integration. More specifically, TKL first uses CP/PARAFAC decomposition and graph diffusion to fuse multiple kernels learned from four complementary data modalities (MRI, PET, CSF, and SNP data). We then used a supervised kernel for a kernel SVM classifier to identify potential patients. To evaluate the effectiveness of TKL, we apply it to data from the Alzheimers Disease Neuroimaging Initiative (ADNI; the sample size n = 331 subjects), including cognitively normal individuals (CN), MCI subjects, and AD patients. TKL improves AD classification performance in both linear and nonlinear combinations, achieving accuracies of 91.31% for CN vs. AD, 81.45% for CN vs. MCI, and 78.27% for AD vs. MCI, compared to 85.48%, 70.89%, and 73.51% using the best single modality. Additionally, TKL reveals clearer, more structured patterns in the data, enhancing interpretation and understanding of the relationships among different modalities. Detailed implementation details of our method can be found at: https://github.com/thanhvd18/Tensor-Kernel-Learning-matlab.

neuroscience↗

bioRxiv2023

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

We show that in silico design of DNA secondary structures is improved by extending the base pairing alphabet beyond A-T and G-C to include the pair between 2-amino-8-(1-{beta}-D-2-deoxyribofuranosyl)-imidazo-[1,2-a]-1,3,5-triazin-(8H)-4-one and 6-amino-3-(1-{beta}-D-2-deoxyribofuranosyl)-5-nitro-(1H)-pyridin-2-one, simply P and Z. To obtain the thermodynamic parameters needed to include P-Z pairs in the designs, we performed 47 optical melting experiments and combined the results with previous work to fit a new set of free energy and enthalpy nearest neighbor folding parameters for P-Z pairs and G-Z wobble pairs. We find that G-Z pairs have stability comparable to A-T pairs and therefore should be considered quantitatively by structure prediction and design algorithms. Additionally, we extrapolated the set of loop, terminal mismatch, and dangling end parameters to include P and Z nucleotides. These parameters were incorporated into the RNAstructure software package for secondary structure prediction and analysis. Using the RNAstructure Design program, we solved 99 of the 100 design problems posed by Eterna using the ACGT alphabet or supplementing with P-Z pairs. Extending the alphabet reduced the propensity of sequences to fold into off-target structures, as evaluated by the normalized ensemble defect (NED). The NED values were improved relative to those from the Eterna example solutions in 91 of 99 cases where Eterna-player solutions were provided. P-Z-containing designs had average NED values of 0.040, significantly below the 0.074 of standard-DNA-only designs, and inclusion of the P-Z pairs decreased the time needed to converge on a design. This work provides a sample pipeline for inclusion of any expanded alphabet nucleotides into prediction and design workflows.

biophysics↗

bioRxiv2022

Direct Visualization of Translesion DNA Synthesis Polymerase IV at the Replisome

In bacterial cells, DNA damage tolerance is manifested by the action of translesion DNA polymerases that can synthesize DNA across template lesions that typically block the replicative DNA polymerase III. It has been suggested that one of these TLS DNA polymerases, DNA polymerase IV, can either act in concert with the replisome, switching places on the {beta} sliding clamp with DNA polymerase III to bypass the template damage, or act subsequent to the replisome skipping over the template lesion in the gap in nascent DNA left behind as the replisome continues downstream. Evidence exists in support of both mechanisms. Using single-molecule analyses we show that DNA polymerase IV associates with the replisome in a concentration-dependent manner and remains associated over long stretches of replication fork progression under unstressed conditions. This association slows the replisome, requires DNA polymerase IV binding to the {beta} clamp but not its catalytic activity, and is reinforced by the presence of the {gamma} subunit of the {beta} clamp-loading DnaX complex in the DNA polymerase III holoenzyme. Thus, DNA damage is not required for association of DNA polymerase IV with the replisome. We suggest that under stress conditions such as induction of the SOS response, the association of DNA polymerase IV with the replisome provides both a surveillance/bypass mechanism and a means to slow replication fork progression, thereby reducing the frequency of collisions with template damage and the overall mutagenic potential. SignificanceDamage to the nucleotide bases that make up the DNA in chromosomes creates a problem for their subsequent accurate duplication each time a cell divides. Typically, the cellular enzymatic machinery that replicates the DNA cannot copy a damaged base and specialized trans-lesion DNA polymerases, which are prone to making errors that result in mutations, are required to copy the damaged base, allowing replication to proceed. We demonstrate that the bacterial replisome, which is comprised of the enzymes required to replicate the chromosome, can associate with one of these specialized trans-lesion polymerases over long distances of replicated DNA. This association slows the speed of replication, thereby reducing the chance of mutations arising in the cell under conditions of stress.

biochemistry↗

bioRxiv2021

Hierarchical Regulation of Autophagy During Adipocyte Differentiation

We previously showed that some adipogenic transcription factors such as CEBPB and PPARG directly and indirectly regulate autophagy gene expression in adipogenesis. The order and the effect of these events are undetermined. In this study, we modeled the gene expression, DNA-binding of transcriptional regulators, and histone modifications during adipocyte differentiation and evaluated the effect of the regulators on gene expression in terms of direction and magnitude. Then, we identified the overlap of the transcription factors and co-factors binding sites and targets. Finally, we built a chromatin states model based on the histone marks and studied their relation with the factors binding. Adipogenic factors differentially regulated autophagy genes as part of the differentiation program. Co-regulators associated with specific transcription factors and preceded them to the regulatory regions. Transcription factors differed in the binding time and location, and their effect on expression was either localized or long-lasting. Adipogenic factors disproportionately targeted genes coding for autophagy-specific transcription factors. To sum, a hierarchical arrangement between adipogenic transcription factors and co-factors drives the regulation of autophagy during adipocyte differentiation.

systems biology↗

bioRxiv2020

Hexosamine biosynthetic pathway integrates circadian and metabolic signals to regulate daily rhythms in protein O-linked N-acetylglucosaminylation

The integration of circadian and metabolic signals is essential for maintaining robust circadian rhythms and ensuring efficient metabolism and energy use. Using Drosophila as an animal model, we showed that cellular protein O-linked N-acetylglucosaminylation (O-GlcNAcylation) exhibits robust 24-hour rhythm and is a key post-translational mechanism that regulates circadian physiology. We observed strong correlation between protein O-GlcNAcylation rhythms and clock-controlled feeding-fasting cycles, suggesting that O-GlcNAcylation rhythms are primarily driven by nutrient input. Interestingly, daily O-GlcNAcylation rhythms were severely dampened when we subjected flies to time-restricted feeding (TRF) at unnatural feeding time. This suggests the presence of a clock-regulated buffering mechanism that prevents excessive O-GlcNAcylation at non-optimal times of the day-night cycle. We found that this buffering mechanism is mediated by glutamine-fructose-6-phosphate amidotransferase (GFAT) activity, which is regulated through integration of circadian and metabolic signals. Finally, we generated a mathematical model to describe the key factors that regulate daily O-GlcNAcylation rhythm.

molecular biology↗

bioRxiv2018

Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models

Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence.\n\nWe developed a mathematical model including endogenous colonization, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patients stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besancon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients.\n\nBoth cross-transmission and endogenous transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge.\n\nImproved cleaning of the environment after discharge would have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.\n\nAuthor summaryUnderstanding the transmission dynamics of multi-drug resistant pathogens in intensive-care units is essential for designing successful infection control strategies. We developed a method that estimates the relative importance of several transmission routes of Pseudomonas aeruginosa (P. aeruginosa), a bacterium intrinsically resistant to multiple antibiotics and known to be a major contributor to hospital-acquired infections. Our model includes three different routes: endogenous colonization, cross-transmission and environmental contamination. Since moist environments may serve as natural reservoirs for P. aeruginosa, we focused our study on environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we disentangled environmental contamination during and after a patients stay. Previous studies were able to assess the role of environmental contamination for specific hospitals using laborious and costly genotyping methods. In contrast, we only used surveillance screening data to estimate the relative contributions of the considered transmission routes. Our results can be used to tailor or assess the effect of interventions. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.

epidemiology↗