US HUPO 2024: Bridging ‘Omics to Function
March 9 – 13, 2024 | Portland, USA

P08.21: Spectrum-centric analysis of data-independent acquisition (DIA) data

Lightning Talk: Lightning Session 1 | Monday, March 11, 2024 | 11:00 AM - 12:15 PM
Date and Time: Monday, March 11, 2024 | 1:30 – 3:00 PM
Session: Poster Session 1

DIA spectra are inherently chimeric as they contain fragment ions from multiple peptide precursors, some of which will be shared. Peptide-centric approaches typically remove these shared fragment ions based on correlations with precursor ions over retention time. We have previously developed a technique to deconvolute chimeric DDA spectra that does not rely on such correlations and can distribute shared fragment ion intensities between precursors instead of discarding them. We demonstrate that this approach extends to any chimeric MS2 spectrum, enabling the consistent analysis of DDA and DIA data.

P08.19: Hands-off spectrum-centric processing of parallel reaction monitoring (PRM) data

Date and Time: Monday, March 11, 2024 | 1:30 – 3:00 PM
Session: Poster Session 1

Parallel reaction monitoring (PRM) is a targeted technique where full fragmentation spectra from predefined isolation windows are monitored over retention time. Identification is typically performed by correlating data with a spectral library, and quantification uses area under the curve from extracted ion chromatograms (XICs) of chosen fragment ions, requiring expert knowledge and frequent manual refinement. Here, we present an automated algorithm for PRM data analysis, abstracting manual fine tuning in the development or execution of targeted assays.

P08.20: Diving deeper into phosphoproteomes through a prediction-based deconvolution algorithms

Date and Time: Tuesday, March 12, 2024 | 1:30 – 3:00 PM
Session: Poster Session 2

The analysis of phoshopeptides requires accurate identification and correct localization of the post-translational modification within the peptide. Here, we extend the capabilities of our deep-learning framework to accurately predict physicochemical properties of phosphorylated peptides and demonstrate the usefulness of these predictions in our search algorithm enabling the analysis of such analytes.