Top-down proteomics is the analysis of intact proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down LC-MS/MS datasets is rapidly increasing due to advances in instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compared to conventional bottom-up data. To take full advantage of the increasing data quality, there is an urgent need to develop algorithms and software tools for confident proteoform identification and quantification. In this study, we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.

Supplementary data are available at Bioinformatics online.

The bacterial pathogen Legionella pneumophila creates an intracellular niche permissive for its replication by extensively modulating host cell functions using hundreds of effector proteins delivered via its Dot/Icm secretion system 1 . Among these, members of the SidE Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches.

This Data Descriptor announces the submission to public repositories of the PNNL Biodiversity Library, a large collection of global proteomics data for 112 bacterial and archaeal organisms. The data comprises 35,162 tandem mass spectrometry (MS/MS) datasets from ~10 years of research. All data has been searched, annotated and organized in a consistent manner to promote reuse by the community. Protein identifications were cross-referenced with KEGG functional annotations which allows for pathway oriented investigation. We present the data as a freely available community resource. A variety of data re-use options are described for computational modelling, proteomics assay design and bioengineering. Instrument data and analysis files are available at ProteomeXchange via the MassIVE partner repository under the identifiers PXD001860 and MSV000079053.