MOTIVATION: Algorithms for sparse data require fast search and subset selection capabilities for the determination of point neighborhoods. A natural data representation for such cases are space partitioning data structures. However, the associated range queries assume noise-free observations and cannot take into account observation-specific uncertainty estimates that are present in e.g. modern mass spectrometry data. In order to accommodate the inhomogeneous noise characteristics of sparse real-world datasets, point queries need to be reformulated in terms of box intersection queries, where box sizes correspond to uncertainty regions for each observation. RESULTS: This contribution introduces libfbi, a standard C++, header-only template implementation for fast box intersection in an arbitrary number of dimensions, with arbitrary data types in each dimension. The implementation is applied to a data aggregation task on state-of-the-art liquid chromatography/mass spectrometry data, where it shows excellent run time properties. AVAILABILITY: The library is available under an MIT license and can be downloaded from http://software.steenlab.org/libfbi. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The qualitative and quantitative characterization of protein abundance profiles over a series of time points or a set of environmental conditions is becoming increasingly important. Using isobaric mass tagging experiments, mass spectrometry-based quantitative proteomics deliver accurate peptide abundance profiles for relative quantitation. Associated data analysis workflows need to provide tailored statistical treatment that (i) takes the correlation structure of the normalized peptide abundance profiles into account and (ii) allows inference of protein-level similarity. We introduce a suitable distance measure for relative abundance profiles, derive a statistical test for equality and propose a protein-level representation of peptide-level measurements. This yields a workflow that delivers a similarity ranking of protein abundance profiles with respect to a defined reference. All procedures have in common that they operate based on the true correlation structure that underlies the measurements. This optimizes power and delivers more intuitive and efficient results than existing methods that do not take these circumstances into account. RESULTS: We use protein profile similarity screening to identify candidate proteins whose abundances are post-transcriptionally controlled by the Anaphase Promoting Complex/Cyclosome (APC/C), a specific E3 ubiquitin ligase that is a master regulator of the cell cycle. Results are compared with an established protein correlation profiling method. The proposed procedure yields a 50.9-fold enrichment of co-regulated protein candidates and a 2.5-fold improvement over the previous method. AVAILABILITY: A MATLAB toolbox is available from http://hci.iwr.uni-heidelberg.de/mip/proteomics.

MOTIVATION: Time-resolved hydrogen exchange (HX) followed by mass spectrometry (MS) is a key technology for studying protein structure, dynamics and interactions. HX experiments deliver a time-dependent distribution of deuteration levels of peptide sequences of the protein of interest. The robust and complete estimation of this distribution for as many peptide fragments as possible is instrumental to understanding dynamic protein-level HX behavior. Currently, this data interpretation step still is a bottleneck in the overall HX/MS workflow. RESULTS: We propose HeXicon, a novel algorithmic workflow for automatic deuteration distribution estimation at increased sequence coverage. Based on an L(1)-regularized feature extraction routine, HeXicon extracts the full deuteration distribution, which allows insight into possible bimodal exchange behavior of proteins, rather than just an average deuteration for each time point. Further, it is capable of addressing ill-posed estimation problems, yielding sparse and physically reasonable results. HeXicon makes use of existing peptide sequence information, which is augmented by an inferred list of peptide candidates derived from a known protein sequence. In conjunction with a supervised classification procedure that balances sensitivity and specificity, HeXicon can deliver results with increased sequence coverage. AVAILABILITY: The entire HeXicon workflow has been implemented in C++ and includes a graphical user interface. It is available at http://hci.iwr.uni-heidelberg.de/software.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. RESULTS: This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra. CONCLUSION: Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from (http://hci.iwr.uni-heidelberg.de/mip/proteomics/).