Valmir Carneiro Barbosa

Concurrency in heavily loaded neighborhood-constrained systems

Let G be a connected undirected graph in which each node corresponds to a process and two nodes are connected by an edge if the corresponding processes share a resource. We consider distributed computations in which processes are constantly demanding all of their resources in order to operate, and in which neighboring processes may not operate concurrently. We advocate that such a system is general enough for representing a large class of resource-sharing systems under heavy load. We employ a distributed scheduling mechanism based on acyclic orientations of G and investigate the amount of concurrency that it provides. We show that this concurrency is given by a number akin to Gs chromatic and multichromatic numbers, and that, among scheduling schemes which require neighbors in G to alternate in their turns to operate, ours is the one that potentially provides the greatest concurrency. However, we also show that the decision problem corresponding to optimizing concurrency is NP-complete.

Search engine processor: Filtering and organizing peptide spectrum matches

The search engine processor (SEPro) is a tool for filtering, organizing, sharing, and displaying peptide spectrum matches. It employs a novel three‐tier Bayesian approach that uses layers of spectrum, peptide, and protein logic to lead the data to converge to a single list of reliable protein identifications. SEPro is integrated into the PatternLab for proteomics environment, where an arsenal of tools for analyzing shotgun proteomic data is provided. By using the semi‐labeled decoy approach for benchmarking, we show that SEPro significantly outperforms a commercially available competitor.

XDIA: improving on the label-free data-independent analysis

SUMMARY XDIA is a computational strategy for analyzing multiplexed spectra acquired using electron transfer dissociation and collision-activated dissociation; it significantly increases identified spectra (approximately 250%) and unique peptides (approximately 30%) when compared with the data-dependent ETCaD analysis on middle-down, single-phase shotgun proteomic analysis. Increasing identified spectra and peptides improves quantitation statistics confidence and protein coverage, respectively. AVAILABILITY The software and data produced in this work are freely available for academic use at http://fields.scripps.edu/XDIA CONTACT: paulo@pcarvalho.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

YADA: a tool for taking the most out of high-resolution spectra

Summary: YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data. Availability: http://fields.scripps.edu/yada Contact: paulo@pcarvalho.com Supplementary information: Supplementary data are available at Bioinformatics online.

Improving the TFold test for differential shotgun proteomics

UNLABELLED We present an updated version of the TFold software for pinpointing differentially expressed proteins in shotgun proteomics experiments. Given an FDR bound, the updated approach uses a theoretical FDR estimator to maximize the number of identifications that satisfy both a fold-change cutoff that varies with the t-test P-value as a power law and a stringency criterion that aims to detect lowly abundant proteins. The new version has yielded significant improvements in sensitivity over the previous one. AVAILABILITY Freely available for academic use at http://pcarvalho.com/patternlab.

PatternLab: From Mass Spectra to Label‐Free Differential Shotgun Proteomics

PatternLab for proteomics is a self‐contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine; modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi‐labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches; and modules with novel features, such as SEProQ for enabling label‐free quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them. Curr. Protoc. Bioinform. 40:13.19.1‐13.19.18.

Analyzing Shotgun Proteomic Data with PatternLab for Proteomics

PatternLab for proteomics is a one‐stop shop computational environment for analyzing shotgun proteomic data. Its modules provide means to pinpoint proteins/peptides that are differentially expressed and those that are unique to a state. It can also cluster the ones that share similar expression profiles in time‐course experiments, as well as help in interpreting results according to Gene Ontology. PatternLab is user‐friendly, simple, and provides a graphical user interface. Curr. Protoc. Bioinform. 30:13.13.1‐13.13.15.

A distributed algorithm to find k -dominating sets

We consider a connected undirected graph G(n,m) with n nodes and m edges. A k-dominating set D in G is a set of nodes having the property that every node in G is at most k edges away from at least one node in D. Finding a k-dominating set of minimum size is NP-hard. We give a new synchronous distributed algorithm to find a k-dominating set in G of size no greater than [n/(k+1)]. Our algorithm requires O(k log* n) time and O(m log k+n log k log* n) messages to run. It has the same time complexity as the best currently known algorithm, but improves on that algorithms message complexity and is, in addition, conceptually simpler.

Generating all the acyclic orientations of an undirected graph

Abstract Let G be an undirected graph with n vertices, m edges and α acyclic orientations. We describe an algorithm for finding all these orientations in overall time O ((n+m)α) and delay complexity O (n(n+m)) . The space required is O (n+m) .