ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

The Effect of Using an Inappropriate Protein Database for Proteomic Data Analysis

A recent study by Bromenshenk et al., published in PLoS One (2010), used proteomic analysis to identify peptides purportedly of Iridovirus and Nosema origin; however the validity of this finding is controversial. We show here through re-analysis of a subset of this data that many of the spectra identified by Bromenshenk et al. as deriving from Iridovirus and Nosema proteins are actually products from Apis mellifera honey bee proteins. We find no reliable evidence that proteins from Iridovirus and Nosema are present in the samples that were re-analyzed. This article is also intended as a learning exercise for illustrating some of the potential pitfalls of analysis of mass spectrometry proteomic data and to encourage authors to observe MS/MS data reporting guidelines that would facilitate recognition of analysis problems during the review process

RAId_aPS: MS/MS analysis with multiple scoring functions and spectrum-specific statistics

Statistically meaningful comparison/combination of peptide identification results from various search methods is impeded by the lack of a universal statistical standard. Providing an E-value calibration protocol, we demonstrated earlier the feasibility of translating either the score or heuristic E-value reported by any method into the textbook-defined E-value, which may serve as the universal statistical standard. This protocol, although robust, may lose spectrum-specific statistics and might require a new calibration when changes in experimental setup occur. To mitigate these issues, we developed a new MS/MS search tool, RAId_aPS, that is able to provide spectrum-specific E-values for additive scoring functions. Given a selection of scoring functions out of RAId score, K-score, Hyperscore and XCorr, RAId_aPS generates the corresponding score histograms of all possible peptides using dynamic programming. Using these score histograms to assign E-values enables a calibration-free protocol for accurate significance assignment for each scoring function. RAId_aPS features four different modes: (i) compute the total number of possible peptides for a given molecular mass range, (ii) generate the score histogram given a MS/MS spectrum and a scoring function, (iii) reassign E-values for a list of candidate peptides given a MS/MS spectrum and the scoring functions chosen, and (iv) perform database searches using selected scoring functions. In modes (iii) and (iv), RAId_aPS is also capable of combining results from different scoring functions using spectrum-specific statistics. The web link is http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid_aps/index.html. Relevant binaries for Linux, Windows, and Mac OS X are available from the same page.Comment: 34 pages, 10 figures, 1 supplementary information file (RAId_aPS_support.pdf). To view the supplementary file, please download and extract the gzipped tar source file listed under "Other formats

A novel online food recall checklist for use in an undergraduate student population : a comparison with diet diaries

Peer reviewedPublisher PD

Phenotypic Evidence of Emerging Ivermectin Resistance in Onchocerca volvulus

Onchocerciasis, commonly known as river blindness, is caused by the filarial nematode Onchocerca volvulus and is transmitted by a blackfly vector. Over 37 million people are thought to be infected, with over 90 million at risk. Infection predominantly occurs in sub-Saharan Africa. Foci also exist in the Arabian Peninsula and Central and South America. Ivermectin, the sole pharmaceutical available for mass chemotherapy, has been used on a community basis for annual or semi-annual treatment since 1987. Multiple treatments with ivermectin kill the microfilariae that are responsible for the pathology of onchocerciasis. More importantly, ivermectin suppresses the reproductive activity of the adult female worms, thus delaying or preventing the repopulation of the skin with new microfilariae and thereby reducing transmission. This study extends earlier reports of sub-optimal responses to ivermectin by examining repopulation levels of microfilaria one year after treatment, worm burdens per nodule, the age structure of adult female worms recovered from nodules, and the reproductive status of adult female worms 90 days after ivermectin treatment. In some communities which have shown a pattern of sub-optimal response to treatment, the data is consistent with an emergence of ivermectin non response or resistance manifested by a loss of the effect of ivermectin on the suppression of parasite reproduction

Genetic Selection of Low Fertile Onchocerca volvulus by Ivermectin Treatment

Onchocerca volvulus is the causative agent of onchocerciasis, or “river blindness”. Ivermectin has been used for mass treatment of onchocerciasis for up to 18 years, and recently there have been reports of poor parasitological responses to the drug and evidence of drug resistance. Drug resistance has a genetic basis. In this study, genetic changes in β-tubulin, a gene associated with ivermectin resistance in nematodes, were seen in parasites obtained from the patients exposed to repeated ivermectin treatment compared with parasites obtained from the same patients before any exposure to ivermectin. Furthermore, the extent of the genetic changes was dependent on the level of ivermectin treatment exposure. This genetic selection was associated with a lower reproductive rate in the female parasites. The data indicates that this genetic selection is for a population of O. volvulus that is more tolerant to ivermectin. This selection could have implications for the development of ivermectin resistance in O. volvulus and for the ongoing onchocerciasis control programmes. Monitoring for the possible development and spread of ivermectin resistance, as part of the control programmes, should be implemented so that any foci of resistant parasites can be treated by alternative control measures

Score regularization for peptide identification

<p>Abstract</p> <p>Background</p> <p>Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.</p> <p>Results</p> <p>In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.</p> <p>Conclusions</p> <p>The score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: <url>http://bioinformatics.ust.hk/SRPI.rar</url>.</p

The Suppressor of AAC2 Lethality SAL1 Modulates Sensitivity of Heterologously Expressed Artemia ADP/ATP Carrier to Bongkrekate in Yeast

The ADP/ATP carrier protein (AAC) expressed in Artemia franciscana is refractory to bongkrekate. We generated two strains of Saccharomyces cerevisiae where AAC1 and AAC3 were inactivated and the AAC2 isoform was replaced with Artemia AAC containing a hemagglutinin tag (ArAAC-HA). In one of the strains the suppressor of ΔAAC2 lethality, SAL1, was also inactivated but a plasmid coding for yeast AAC2 was included, because the ArAACΔsal1Δ strain was lethal. In both strains ArAAC-HA was expressed and correctly localized to the mitochondria. Peptide sequencing of ArAAC expressed in Artemia and that expressed in the modified yeasts revealed identical amino acid sequences. The isolated mitochondria from both modified strains developed 85% of the membrane potential attained by mitochondria of control strains, and addition of ADP yielded bongkrekate-sensitive depolarizations implying acquired sensitivity of ArAAC-mediated adenine nucleotide exchange to this poison, independent from SAL1. However, growth of ArAAC-expressing yeasts in glycerol-containing media was arrested by bongkrekate only in the presence of SAL1. We conclude that the mitochondrial environment of yeasts relying on respiratory growth conferred sensitivity of ArAAC to bongkrekate in a SAL1-dependent manner. © 2013 Wysocka-Kapcinska et al