A Skyline Plugin for Pathway-Centric Data Browsing

Abstract

Abstract. For targeted proteomics to be broadly adopted in biological laboratories as a routine experimental protocol, wet-bench biologists must be able to approach selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) assay design in the same way they approach biological experimental design. Most often, biological hypotheses are envisioned in a set of protein interactions, networks, and pathways. We present a plugin for the popular Skyline tool that presents public mass spectrometry data in a pathway-centric view to assist users in browsing available data and determining how to design quantitative experiments. Selected proteins and their underlying mass spectra are imported to Skyline for further assay design (transition selection). The same plugin can be used for hypothesis-driven dataindependent acquisition (DIA) data analysis, again utilizing the pathway view to help narrow down the set of proteins that will be investigated. The plugin is backed by the Pacific Northwest National Laboratory (PNNL) Biodiversity Library, a corpus of 3 million peptides from >100 organisms, and the draft human proteome. Users can upload personal data to the plugin to use the pathway navigation prior to importing their own data into Skyline.

Ancient Regulatory Role of Lysine Acetylation in Central Metabolism

“…Final annotation for multiple-sequence alignments was hand curated to remove weak scoring paralogs and proteins with alternate annotation (e.g., at UniProt or RefSeq). Multiple-sequence alignments were performed using Muscle ( 35 ) via a web application programming interface (API) ( 36 ), and peptide identifications were mapped visually onto multiple-sequence alignments using the peptide homology viewer ( 37 ). The phylogenetic tree which is part of Fig.…”

A rapid methods development workflow for high-throughput quantitative proteomic applications

“…Attempts to reduce the number of potential number of candidates by using computational methods (e.g., ESPP [13], PeptidePicker [14], PeptideSieve [15]) or empirical selection peptides based on previous data (e.g., from PRIDE [16], PeptideAtlas [17]) are promising but additional factors such as different experimental conditions, data acquisition methods, variable retention times, and low abundance of the proteins of interest often limit the successful application of these methods. Community resources such as SRMAtlas [12], PRIDE [16], Panorama [18], or the BioDiversity Library [19,20], a collection of proteomic data comprising of over 100 bacterial and archaeal organisms, as well as commercial software tools such as Spectrum Mill and the newly released SpectroDive have been built to overcome these challenges, yet significant methods development is typically still necessary. Likewise, research by Prakash and co-workers showed how spectral libraries are powerful way to select SRM transitions and confirm the identity of peptides in SRM methods [21].…”