Brendan MacLean

Carafe2 generates high-quality in silico spectral libraries for timsTOF DIA proteomics by fine-tuning deep learning models on DIA data, outperforming DDA-trained models in multiple applications.

The paper develops scalable targeted proteomics assays using the Stellar mass spectrometer for analyzing biofluids, demonstrating high sensitivity and multiplexing capabilities.

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.

This paper evaluates a prototype Orbitrap Astral Zoom mass spectrometer for quantitative proteomics, showing improved performance in ion detection and quantitative precision compared to the standard Orbitrap Astral MS.

The paper introduces a new MS platform with enhanced acquisition rates and real-time chromatogram alignment, enabling high multiplex targeted proteomics with improved sensitivity and throughput.

The paper introduces a framework for quality control in quantitative proteomics, combining internal and external QC measures with tools for data analysis and deposition.

Koina provides an open-source, accessible ML/DL prediction service for proteomics, improving integration with existing tools like FragPipe.

This paper introduces a workflow for improved XL-MS data analysis using PASEF, integrating MeroX and Skyline for better identification of cross-linked products.

The paper develops a multidimensional lipid library using Skyline to annotate lipidomic data and applies it to identify lipid markers for smoke inhalation injury severity.

Avant-garde improves DIA data analysis by refining signals, reducing interference, and controlling false discovery rates through a data-driven approach.

The paper creates a proteomic resource profiling 90 drugs in six cell lines, revealing cell-specific responses and enabling integration with transcriptional data to identify therapeutic opportunities.