Joseph Nathaniel Paulson

The paper develops fluorescent probes to detect Paenibacillus thiaminolyticus in mixed populations by removing an S-layer that blocks probe access.

The paper identifies four immune clusters in pediatric nervous system tumors using RNA-seq data and validates them with multiple methods, highlighting the need for personalized immunogenomic profiling.

The paper introduces a method to infer differential richness in 16S rRNA surveys by accounting for spurious taxa generated during sequencing, using an R package called Prokounter.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

This study uses microbial topic modeling to identify associations between vaginal microbiome composition and intrapartum fever in Ugandan laboring women, improving prediction beyond clinical variables.

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

The paper introduces an R package for handling 16S rRNA databases and marker-gene survey data, providing consistent interfaces for database comparison and data structure development.

The paper identifies genomic variants associated with immune cell infiltration using histopathological image QTL analysis.

This study identifies cancer subtypes using somatic mutation data from The Cancer Genome Atlas, finding associations with prognostic outcomes and treatment options.

Metaviz provides an interactive web tool for exploring and visualizing metagenomic data through heatmaps, bar plots, and PCA scatter plots.

The paper develops a preprocessing and normalization pipeline for large-scale RNA-Seq data, addressing batch and tissue effects in heterogeneous datasets.

This study analyzes gene regulation across 38 tissues to show that transcription factor-target connections are more tissue-specific than genes, and that tissue-specific genes are not highly targeted in their networks but occupy bottleneck positions.

The paper compares gene expression and regulatory patterns between cell lines and their source tissues, finding reduced expression levels and altered transcription factor regulation in cell lines.

This paper introduces a normalization method for genomic data that accounts for biological differences in distributions while reducing technical variation.

The paper introduces a method using smoothing splines to analyze longitudinal microbial marker-gene data, identifying time periods of differential abundance across sample groups.

The paper uses a network-based approach to analyze eQTLs across 13 tissues, identifying cis- and trans-eQTLs and linking SNPs to biological processes and disease associations.

The paper identifies sexual dimorphism in gene expression and regulatory networks across 31 human tissues using GTEx data, showing that many genes and networks are sex-specific.