A5026276346- RNA and protein synthesis mechanisms
- Genomics and Chromatin Dynamics
- RNA Research and Splicing
- Genetic Mapping and Diversity in Plants and Animals
- Innovative Microfluidic and Catalytic Techniques Innovation
- Electrowetting and Microfluidic Technologies
- Genetics, Aging, and Longevity in Model Organisms
- Advanced biosensing and bioanalysis techniques
- Genetics, Bioinformatics, and Biomedical Research
- Microfluidic and Capillary Electrophoresis Applications
- Chemical Synthesis and Analysis
- Monoclonal and Polyclonal Antibodies Research
- Nutrition, Genetics, and Disease
- GABA and Rice Research
- Computational Drug Discovery Methods
- Gene Regulatory Network Analysis
- Ecosystem dynamics and resilience
- Glycosylation and Glycoproteins Research
- Seed Germination and Physiology
- Microbial Metabolic Engineering and Bioproduction
- CRISPR and Genetic Engineering
- Protein Structure and Dynamics
- Machine Learning in Materials Science
- Fungal and yeast genetics research
- Genetic Associations and Epidemiology
Nvidia (United States)
2024
Stanford University
2016-2020
Rockford University
2017
University of Utah
2015
Northwestern University
2014-2015
Abstract The genetic variants underlying complex traits are often elusive even in powerful model organisms such as Caenorhabditis elegans with controlled backgrounds and environmental conditions. Two major contributing factors are: (1) the lack of statistical power from measuring phenotypes small numbers individuals, (2) use phenotyping platforms that do not scale to hundreds individuals prone noisy measurements. Here, we generated a new resource 359 recombinant inbred strains augments...
Abstract Motivation Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating predictive importance of individual features (nucleotides or motifs) any input sequence its output prediction from DNN model. However, these do not...
Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements TF-DNA binding energies can improve the accuracy predictions TF occupancy and downstream vivo shed light on how transcriptional networks rewired throughout evolution. Here, we present a sequencing-based assay analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable providing quantitative...
Abstract Expression quantitative trait locus (eQTL) mapping provides a powerful means to identify functional variants influencing gene expression and disease pathogenesis. We report the identification of cis-eQTLs from 7,051 post-mortem samples representing 44 tissues 449 individuals as part Genotype-Tissue (GTEx) project. find cis-eQTL for 88% all annotated protein-coding genes, with one-third having multiple independent effects. numerous tissue-specific cis-eQTLs, highlighting unique...
The R package COPASutils provides a logical workflow for the reading, processing, and visualization of data obtained from Union Biometrica Complex Object Parametric Analyzer Sorter (COPAS) or BioSorter large-particle flow cytometers. Data these powerful experimental platforms can be unwieldy, leading to difficulties in ability process visualize using existing tools. Researchers studying small organisms, such as Caenorhabditis elegans, Anopheles gambiae, Danio rerio, devices will benefit this...
High-throughput protein screening is a critical technique for dissecting and designing function. Libraries these assays can be created through number of means, including targeted or random mutagenesis template sequence direct DNA synthesis. However, mutagenic library construction methods often yield vastly more nonfunctional than functional variants and, despite advances in large-scale synthesis, individual synthesis each desired prohibitively expensive. Consequently, many protein-screening...
Abstract Microfluidic technologies have been used across diverse disciplines ( e.g. high-throughput biological measurement, fluid physics, laboratory manipulation) but widespread adoption has limited due to the lack of openly disseminated resources that enable non-specialist labs make and operate their own devices. Here, we report open-source build a pneumatic setup capable operating both single multilayer (Quake-style) microfluidic devices with programmable scripting automation. This can...
Abstract Motivation Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded associated with transcription factor binding and chromatin accessibility. Several feature attribution methods have been developed for estimating predictive importance of individual features (nucleotides or motifs) any input sequence its output prediction from DNN model. However, these do not...
Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements TF-DNA binding energies can improve the accuracy predictions TF occupancy and downstream vivo further shed light on how transcriptional networks rewired throughout evolution. Here, we present a novel sequencing-based assay analysis pipeline capable providing quantitative estimates for more than one million DNA...
Deciphering gene regulatory networks is a central problem in computational biology. Here, we explore the use of multi-modal neural to learn predictive models expression that include cis and trans components. We stress response budding yeast Saccharomyces cerevisiae. Our achieve high performance substantially outperform other state-of-the-art methods such as boosting algorithms pre-defined cis-regulatory features. model learns several regulators including well-known master regulators. our...
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language (pLM) hundreds of graphical processing units (GPUs). We introduce the BioNeMo Framework facilitate AI across GPUs. Its modular design allows integration individual components, such as data loaders, into existing workflows is open community contributions....
Computational methods for generating molecules with specific physiochemical properties or biolog- ical activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. In this work, we introduce novel approach utilizes Reinforcement Learning paradigm, called proximal policy optimization (PPO), optimizing chemical in the latent space of pre-trained deep model. Working model allows us to bypass need explicitly defining...
Abstract Motivation High-throughput protein screening is a critical technique for dissecting and designing function. Libraries these assays can be created through number of means, including targeted or random mutagenesis template sequence direct DNA synthesis. However, mutagenic library construction methods often yield vastly more non-functional than functional variants and, despite advances in large-scale synthesis, individual synthesis each desired prohibitively ex-pensive. Consequently,...
High-throughput protein screening is a critical technique for dissecting and designing function. Libraries these assays can be created through number of means, including targeted or random mutagenesis template sequence direct DNA synthesis. However, mutagenic library construction methods often yield vastly more nonfunctional than functional variants and, despite advances in large-scale synthesis, individual synthesis each desired prohibitively expensive. Consequently, many protein-screening...