Daniel Jacobson

The study examines how climate gradients affect Populus trichocarpa stress responses and microbial communities across different soil niches.

This study uses genome shuffling in Bacillus subtilis to map quantitative trait loci for complex phenotypes, identifying high-confidence QTLs of moderate size.

The paper uses single-cell RNA sequencing and ATAC-seq to analyze host responses to HCoV-229E infection, revealing heterogeneous transcriptional and regulatory changes across infected cells.

The paper investigates how chromatin dynamics and gene expression change after epigenetic drug treatment, finding locus-specific chromatin remodeling and gene expression alterations.

This study identifies key genetic loci associated with stomatal and leaf traits in Populus trichocarpa that contribute to climate adaptation, showing smaller stomata in genotypes from arid regions.

The paper uses long-read sequencing to analyze telomere-to-telomere assemblies in Populus trichocarpa, revealing complex KCS gene variation and its association with helitron transposons.

The paper analyzes 739 Thlaspi arvense accessions to uncover genetic variation and adaptation mechanisms, identifying genomic regions linked to agronomic traits and climate resilience.

This study identifies natural variation in waterlogging tolerance among pennycress accessions, finding some accessions survive prolonged waterlogging with minimal impact on seed yield.

RWRtoolkit enables multi-omic network analysis using random walks on multiplex networks across any species, providing tools for topological analysis and statistical evaluation.

This study examines how waterlogging affects two pennycress lines, showing differences in gene expression, morphology, and yield, with MN106 showing stronger upregulation of hypoxia-related genes.

MENTOR is a network-based algorithm that partitions gene sets into functions and identifies biological functions in Populus trichocarpa associated with microbial abundance using supervised and unsupervised learning.

The paper introduces network analysis methods to identify exogenous compounds that trigger specialized metabolite production in fungi and distinguish unknown analytes.

This paper develops a novel feature set and public resource for predicting CRISPR-Cas9 sgRNA efficiency using quantum chemical tensors and explainable-AI methods.

The paper uses exascale computing to generate high-resolution global geoclimate zones by clustering environmental similarity data from 71 variables over 50 years.

The paper identifies high-resolution environmental zones and predicts longitudinal climatic processes using vector comparison and network analysis methods.

This study characterizes how SARS-CoV-2 3CLpro cleaves NEMO, revealing structural and functional insights that explain its high fitness in humans.

This paper conducts a multi-ancestry GWAS meta-analysis of suicide attempt, identifying 12 genome-wide significant loci and finding shared genetic risks with ADHD, smoking, and risk tolerance.

This study relaxes GWAS significance thresholds and uses GRIN to identify additional genes linked to suicidal behavior, which replicate across cohorts and show stronger biological connections.

The paper identifies structural variants (SVs) in ASD families and links them to molecular pathways and genes associated with non-verbal ASD, using explainable AI for subgroup identification.

The paper uses a systems biology approach to analyze COVID-19 data and identifies non-respiratory infection pathways as key to its global spread.

The paper introduces Maestro, a network analysis method that identifies synchronized genetic activities in Alzheimer's disease, finding a pattern associated with AD that is significantly more common in cases than controls.

The paper integrates genomic, metabolomic, and network data to identify target genes involved in lignin biosynthesis/degradation in Populus trichocarpa.