Biology subjects

Vats, P.

Publications and source records attributed to Vats, P..

9 recordsLinked to original sources

bioRxiv2025

Integrating Genomics into Multimodal EHR Foundation Models

AO_SCPLOWBSTRACTC_SCPLOWThis paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the models predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architectures versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.

bioinformatics↗

bioRxiv2025

Parabricks: GPU Accelerated Universal Pan-Instrument Genomics Analysis Software Suite

Next-generation sequencing (NGS) has transformed genomics, enabling breakthroughs in biotechnology, healthcare, and pharmaceuticals. However, exponential data growth-outpacing Moores Law-presents critical computational challenges for secondary analysis. We introduce Parabricks, a freely accessible, GPU-accelerated software suite supporting diverse workflows, including wholegenome, exome, transcriptome, and methylation analysis. Compatible across major short read and long read sequencing platforms, it reduces processing time from hours to minutes while maintaining concordance with established standards, this acceleration translates to significant cost savings. Deployable on-premises or through cloud platforms, Parabricks ensures scalable, genomic processing. Integrated AI-driven workflows and deep learning models further enhance precision, offering a universal solution for high-throughput genomic research and clinical diagnostics.

bioinformatics↗

bioRxiv2025

DeepSAP: Improved RNA-Seq Alignment by Integrating Transcriptome Guidance with Transformer-Based Splice Junction Scoring

Advancements in high-throughput sequencing have revolutionized the field of transcriptomics, pro-viding unprecedented insights into gene expression, splicing events, and fusions. Despite these advancements, the analysis of RNA-seq data remains challenging due to the presence of complex splice junctions, multi-mapped reads, and chimeric events. In this study, we present DeepSAP, an innovative approach that improves the accuracy of RNA-seq alignment by integrating Transcriptome-Guided Genomic Alignment, as implemented in GSNAP, with improved splice junction scoring, predicted by a transformer-based deep learning model. Our work demonstrates synergy between these methods, resulting in enhanced detection of splice junctions, identification of indels, and resolution of complex splicing patterns. On a standard benchmark of human simulated datasets, DeepSAP achieves the highest mean F1 score (0.971) for splice junction detection, outperforming DRAGEN (0.933), novoSplice (0.914), STAR (0.821), HISAT2 (0.662), and Subjunc (0.770). By integrating the unique capabilities of transcriptome-guided alignment and large language models, our splice junction scoring approach captures intricate sequence patterns surrounding splice donor and acceptor sites, providing significant advancement in RNA-seq data analysis.

bioinformatics↗

bioRxiv2024

Characterization of Collaborative Cross mouse founder strain CAST/EiJ as a novel model for lethal COVID-19

Mutations in SARS-CoV-2 variants of concern (VOCs) have expanded the viral host range beyond primates, and a limited range of other mammals, to mice, affording the opportunity to exploit genetically diverse mouse panels to model the broad range of responses to infection in patient populations. Here we surveyed responses to VOC infection in genetically diverse Collaborative Cross (CC) founder strains. Infection of wild-derived CC founder strains produced a broad range of viral burden, disease susceptibility and survival, whereas most other strains were resistant to disease despite measurable lung viral titers. In particular, CAST/EiJ, a wild-derived strain, developed high lung viral burdens, more severe lung pathology than seen in other CC strains, and a dysregulated cytokine profile resulting in morbidity and mortality. These inbred mouse strains may serve as a valuable platform to evaluate therapeutic countermeasures against severe COVID-19 and other coronavirus pandemics in the future.

immunology↗

bioRxiv2024

A de novo variant in PAK2 detected in an individual with Knobloch type 2 syndrome

P21-activated kinase 2 (PAK2) is a serine/threonine kinase essential for a variety of cellular processes including signal transduction, cellular survival, proliferation, and migration. A recent report proposed monoallelic PAK2 variants cause Knobloch syndrome type 2 (KNO2)--a developmental disorder primarily characterized by ocular anomalies. Here, we identified a novel de novo heterozygous missense variant in PAK2, NM_002577.4:c.1273G>A, p.(D425N), by whole genome sequencing in an individual with features consistent with KNO2. Notable clinical phenotypes include global developmental delay, congenital retinal detachment, mild cerebral ventriculomegaly, hypotonia, FTT, pyloric stenosis, feeding intolerance, patent ductus arteriosus, and mild facial dysmorphism. The p.(D425N) variant lies within the protein kinase domain and is predicted to be functionally damaging by in silico analysis. Previous clinical genetic testing did not report this variant due to unknown relevance of PAK2 variants at the time of testing, highlighting the importance of reanalysis. Our findings also substantiate the candidacy of PAK2 variants in KNO2 and expand the KNO2 clinical spectrum.

genomics↗

bioRxiv2022

Zinc transporter somatic gene mutations cause primary aldosteronism

Primary aldosteronism (PA) is the most common form of endocrine hypertension and effects one in 50 adults. PA is characterized by inappropriately elevated aldosterone production via renin-independent mechanisms. Driver somatic mutations for aldosterone excess have been found in approximately 90% of aldosterone-producing adenomas (APAs). Using next-generation sequencing, we identified recurrent in-frame deletions in SLC30A1 in five APAs (p.L51_A57del, n=3; p.L49_L55del, n=2). SLC30A1 encodes the ubiquitous zinc efflux transporter ZnT1 (zinc transporter 1). The identified SLC30A1 variants are situated in close proximity of the zincbinding site (H43 and D47) in transmembrane domain II and likely cause abnormal ion transport. PA cases with the unique SLC30A1 mutations showed male dominance and demonstrated increased aldosterone and 18-oxo-cortisol concentrations. Functional studies of the mutant SLC30A151_57del variant in a doxycycline-inducible adrenal cell system revealed abnormal Na+ conductivity caused by the mutant, which in turn led to the depolarization of the resting membrane potential, and thus to the opening of voltage-gated calcium channels. This resulted in an increase in cytosolic Ca2+ activity, which stimulated CYP11B2 mRNA expression and aldosterone production. Collectively, these data implicate the first-in-field zinc transporter mutations as a dominant driver of aldosterone excess in PA.

genetics↗

bioRxiv2021

de novo variant calling identifies cancer mutation profiles in the 1000 Genomes Project

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. We developed a GPU-based workflow to rapidly call DNVs (HAT) and demonstrated its effectiveness by applying it to 4,216 Simons Simplex Collection (SSC) whole-genome sequenced parent-child trios from DNA derived from blood. In our SSC DNV data, we identified 78 {+/-} 15 DNVs per individual, 18% {+/-} 5% at CpG sites, 75% {+/-} 9% phased to the paternal chromosome of origin, and an average allele balance of 0.49. These calculations are all in line with DNV expectations. We sought to build a control DNV dataset by running HAT on 602 whole-genome sequenced parent-child trios from DNA derived from lymphoblastoid cell lines (LCLs) from the publicly available 1000 Genomes Project (1000G). In our 1000G DNV data, we identified 740 {+/-} 967 DNVs per individual, 14% {+/-} 4% at CpG sites, 61% {+/-} 11% phased to the paternal chromosome of origin, and an average allele balance of 0.41. Of the 602 trios, 80% had > 100 DNVs and we hypothesized the excess DNVs were cell line artifacts. Several lines of evidence in our data suggest that this is true and that 1000G does not appear to be a static reference. By mutation profile analysis, we tested whether these cell line artifacts were random and found that 40% of individuals in 1000G did not have random DNV profiles; rather they had DNV profiles matching B-cell lymphoma. Furthermore, we saw significant excess of protein-coding DNVs in 1000G in the gene IGLL5 that has already been implicated in this cancer. As a result of cell line artifacts, 1000G has variants present in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites. Our study elucidates important implications of the use of sequencing data from LCLs for both reference building projects as well as disease-related projects whereby these data are used in variant filtering steps.

genomics↗

bioRxiv2021

Identification of Differentially Expressed Genes and associated pathways common to Eyelid and Non-Ocular Basal Cell Carcinoma to understand the Molecular Biology of BCC

BackgroundEyelid BCC accounts for more than 90% of Eyelid malignant neoplasms. Various aberrant signalling pathways and genes in Non-Ocular BCC have been found whereas Eyelid bcc remains elusive. ObjectiveThis study aims to find the common DEGs of Eyelid and Non-Ocular BCC using bioinformatic analysis and text mining to gain more insights into the molecular aspects common to both BCC non-ocular and Eyelid BCC and to identify common potential prognostic markers. Material and methodThe Gene Expression profiles of Eyelid BCC (GSE103439) and Non-Ocular BCC (GSE53462) were obtained from the NCBI GEO database followed by identification of common DEGs. Protein-Protein interaction and Pathway Enrichment analysis of these screened genes was done using bioinformatic tools like STRING, Cytoscape and BiNGO, DAVID, KEGG respectively. ResultsA total of 181 genes were found common in both datasets. A PPI network was formed for the screened genes and 20 HUB genes were sorted which included CTNNB1, MAPK14, BTRC, EGFR, ADAM17. Pathway enrichment of HUB genes showed that they were dysregulated in carcinogenic and apoptotic pathways that seem to play a role in the progression of both the BCC. ConclusionThe result and findings of bioinformatic analysis highlighted the molecular pathways and genes enriched in both Eyelid BCC as well as Non-Ocular BCC. The identified pathways should be studied further to recognise common molecular events that would lead to the progression of BCC. This may provide a window to explore the prognostic and therapeutic strategies common to both BCC.

bioinformatics↗

bioRxiv2019

GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogens obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis (DPA) to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.\n\nAuthor SummaryMycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth.

microbiology↗