MaxDIA enables library-based and library-free data-independent acquisition proteomics.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries.

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python.

Sleep-wake cycles drive daily dynamics of synaptic phosphorylation.

The circadian clock drives daily changes of physiology, including sleep-wake cycles, through regulation of transcription, protein abundance, and function. Circadian phosphorylation controls cellular processes in peripheral organs, but little is known about its role in brain function and synaptic activity. We applied advanced quantitative phosphoproteomics to mouse forebrain synaptoneurosomes isolated across 24 hours, accurately quantifying almost 8000 phosphopeptides.

A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis.

Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.

Elucidation of Signaling Pathways from Large-Scale Phosphoproteomic Data Using Protein Interaction Networks.

Phosphoproteomic experiments typically identify sites within a protein that are differentially phosphorylated between two or more cell states. However, the interpretation of these data is hampered by the lack of methods that can translate site-specific information into global maps of active proteins and signaling networks, especially as the phosphoproteome is often undersampled. Here, we describe PHOTON, a method for interpreting phosphorylation data within their signaling context, as captured by protein-protein interaction networks, to identify active proteins and pathways and pinpoint functional phosphosites.

System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis.

The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread.