Brook L. Nunn

Proteomic insights into the photobiology of the Hawaiian rice coral Montipora capitata in response to decreased light intensity

This study uses DIA proteomics to analyze how Hawaiian rice coral Montipora capitata and its symbionts respond to decreased light intensity, identifying changes in protein abundance linked to metabolic and structural adaptations.

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Carafe generates high-quality in silico spectral libraries for DIA proteomics by training deep learning models directly on DIA data, improving fragment ion intensity prediction and peptide detection.

Resilience in a time of stress: revealing the molecular underpinnings of coral survival following thermal bleaching events

This study identifies molecular biomarkers of coral resilience to thermal bleaching by comparing proteomic, microbiome, and physiological traits of surviving and dying corals.

Hydrocarbon Metabolism and Petroleum Seepage as Ecological and Evolutionary Drivers for Cycloclasticus

This study identifies two Cycloclasticus variants with distinct hydrocarbon metabolism capabilities linked to oceanic and seep environments, revealing their ecological roles in hydrocarbon cycling.

Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle:High-Resolution Sampling Every 4 Hours Over 22 Days

The paper describes a high-resolution microbial metagenome dataset collected over 22 days during a phytoplankton bloom cycle, including gene abundances and time-matched environmental data.

Survey of metaproteomics software tools for functional microbiome analysis.

This paper evaluates six metaproteomics tools for functional annotation using GO terms, finding differences in sensitivity and specificity, and integrates two into Galaxy for broader accessibility.

A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases

The paper introduces a sectioning method to improve peptide spectrum matching in large genome-guided protein sequence databases by increasing sensitivity while maintaining acceptable false discovery rates.