A5047772808- Gene Regulatory Network Analysis
- Single-cell and spatial transcriptomics
- Cancer-related gene regulation
- RNA Research and Splicing
- Epigenetics and DNA Methylation
- Cell Image Analysis Techniques
- Fungal Biology and Applications
- RNA modifications and cancer
- Gene expression and cancer classification
- Receptor Mechanisms and Signaling
- Bioinformatics and Genomic Networks
- RNA and protein synthesis mechanisms
- SARS-CoV-2 detection and testing
- Microbial Metabolism and Applications
- Biosensors and Analytical Detection
- SARS-CoV-2 and COVID-19 Research
- CRISPR and Genetic Engineering
- Fermentation and Sensory Analysis
- Escherichia coli research studies
- Inflammatory mediators and NSAID effects
- Influenza Virus Research Studies
- Genomics and Chromatin Dynamics
- Enzyme Structure and Function
- Amyotrophic Lateral Sclerosis Research
- Circadian rhythm and melatonin
New York Genome Center
2025
New York University
2019-2023
University at Buffalo, State University of New York
2010-2019
The University of Texas Medical Branch at Galveston
1990-1991
A major challenge in controlling the COVID-19 pandemic is high false-negative rate of commonly used RT-PCR methods for SARS-CoV-2 detection clinical samples. Accurate particularly challenging samples with low viral loads that are below limit (LoD) standard one- or two-step methods. In this study, we implemented a three-step approach and quantification employs reverse transcription, targeted cDNA preamplification, nano-scale qPCR based on commercially available microfluidic chip. Using...
Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene networks typically constructed from data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the state of thousands individual cells in a single experiment, offering advantages combinatorial experimental design, large numbers independent measurements, accessing interaction...
Gene regulatory networks define relationships between transcription factors and target genes within a biological system, reconstructing them is essential for understanding cellular growth function. Methods inferring from genomics data have evolved rapidly over the last decade in response to advances sequencing technology machine learning. The scale of collection has increased dramatically; largest genome-wide gene expression datasets grown thousands measurements millions single cells, new...
The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the this that impute missing values, address sampling issues quantify correct noise. In spite such efforts, no consensus on best practices has established all current approaches vary substantially based available empirical tests. k-Nearest Neighbor Graph (kNN-G) is often used infer identities of, relationships between, cells basis many widely...
Half of all amyotrophic lateral sclerosis (ALS) patients demonstrate a spectrum cognitive and behavioral changes over the course disease, but mechanisms underlying this heterogeneity remain unclear. We assemble high-resolution cellular map prefrontal cortex regions ALS brain by integrating spatial single-nucleus transcriptomic profiles cognitively stratified patient cohort that includes non-neuropathological controls. find programs characteristic ALS, including frequent gliotic response....
Cotranscriptional recruitment of pre-mRNA splicing factors to their genomic targets facilitates efficient and ordered assembly a mature messenger ribonucleoprotein particle (mRNP).However, how the cotranscriptional is regulated remains largely unknown.Here, we demonstrate that protein arginine methylation plays novel role in regulating this process Saccharomyces cerevisiae.Our data show Hmt1, major type I methyltransferase, methylates Snp1, U1 small nuclear RNP (snRNP)specific protein,...
Abstract Background Modeling of gene regulatory networks (GRNs) is limited due to a lack direct measurements genome-wide transcription factor activity (TFA) making it difficult separate covariance and interactions. Inference interactions TFA requires aggregation complementary evidence. Estimating explicitly problematic as disconnects GRN inference estimation unable account for, for example, contextual factor-transcription interactions, other higher order features. Deep-learning offers...
Signaling levels within sensory neurons must be tightly regulated to allow cells integrate information from multiple signaling inputs and respond new stimuli. Herein we report a role for the cGMP-dependent protein kinase EGL-4 in negative regulation of G protein-coupled nociceptive chemosensory signaling. C. elegans lacking function are hypersensitive their behavioral response low concentrations bitter tastant quinine exhibit an elevated calcium flux ASH quinine. We provide first direct...
Protein arginine methylation regulates diverse functions of eukaryotic cells, including gene expression, the DNA damage response, and circadian rhythms. We showed that residues within third intracellular loop human D2 dopamine receptor, which are conserved in DOP-3 receptor nematode Caenorhabditis elegans, were methylated by protein methyltransferase 5 (PRMT5). By mutating these residues, we further their enhanced receptor-mediated inhibition cyclic adenosine monophosphate (cAMP) signaling...
Abstract Motivation Gene regulatory networks define relationships between transcription factors and target genes within a biological system, reconstructing them is essential for understanding cellular growth function. Methods inferring from genomics data have evolved rapidly over the last decade in response to advances sequencing technology machine learning. The scale of collection has increased dramatically; largest genome-wide gene expression datasets grown thousands measurements millions...
Abstract The modeling of gene regulatory networks (GRNs) is limited due to a lack direct measurements features in genome-wide screens. Most GRN inference methods are therefore forced model relationships between genes and their targets with expression as proxy for the upstream independent features, complicating validation predictions produced by frameworks. Separating covariance influence requires aggregation complementary sets evidence, such transcription factor (TF) binding target...
Protein arginine methylation is a PTM catalyzed by an evolutionarily conserved family of enzymes called protein methyltransferases (PRMTs), with PRMT1 being the most member this enzyme family. This modification has emerged to be important regulator functions. To better understand role PRMTs in cellular pathways and functions, we have carried out proteomic profiling experiment comprehensively identify physical interactors Hmt1, budding yeast homolog for human PRMT1. Using dual-enzymatic...
Abstract Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene networks typically constructed from data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the state of thousands individual cells in a single experiment, offering advantages combinatorial experimental design, large numbers independent measurements, accessing...
Cholera toxin (CT) stimulated adenylate cyclase and a phospholipase which elevated levels of 3.5‐cyclic adenosine monophosphate (cAMP) arachidonic acid (AA). The AA was quickly converted to prostaglandins (PGs) via the cyclo‐oxygenase pathway. Chloroquine exerted minimal inhibition cAMP in CT‐treated cells, although CT‐induced release [ 3 H]AA PGs blocked completely when drug added concentrations as low 0.1 mM (30 μγ ml). Inhibition complete chloroquine before or within 30 min after CT....
Abstract Background A major challenge in controlling the COVID-19 pandemic is high false-negative rate of commonly used standard RT-PCR methods for SARS-CoV-2 detection clinical samples. Accurate particularly challenging samples with low viral loads that are below limit (LoD) one- or two-step methods. Methods We implement a three-step approach and quantification employs reverse transcription, targeted cDNA preamplification nano-scale qPCR based on Fluidigm 192.24 microfluidic chip. validate...
Protein arginine methylation is an important means by which protein function can be regulated. In the budding yeast, this modification catalyzed major methyltransferase Hmt1. Here, we provide evidence that Hmt1-mediated of Rpc31, a subunit RNA polymerase III, plays context-dependent roles in tRNA gene transcription: under conditions optimal for growth, it positively regulates transcription, and setting stress, promotes robust transcriptional repression. context Rpc31 allows its interaction...
Abstract The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the this that impute missing values, address sampling issues quantify correct noise. In spite such efforts, no consensus on best practices has established all current approaches vary substantially based available empirical tests. k-Nearest Neighbor Graph (kNN-G) is often used infer identities of, relationships between, cells basis many widely...