Hongwei Xie

Bayesian Nonparametric Model for the Validation of Peptide Identification in Shotgun Proteomics

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with PeptideProphet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

Proteome-wide Prediction of Signal Flow Direction in Protein Interaction Networks Based on Interacting Domains

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

Tmod: toolbox of motif discovery

SUMMARY Motif discovery is an important topic in computational transcriptional regulation studies. In the past decade, many researchers have contributed to the field and many de novo motif-finding tools have been developed, each may have a different strength. However, most of these tools do not have a user-friendly interface and their results are not easily comparable. We present a software called Toolbox of Motif Discovery (Tmod) for Windows operating systems. The current version of Tmod integrates 12 widely used motif discovery programs: MDscan, BioProspector, AlignACE, Gibbs Motif Sampler, MEME, CONSENSUS, MotifRegressor, GLAM, MotifSampler, SeSiMCMC, Weeder and YMF. Tmod provides a unified interface to ease the use of these programs and help users to understand the tuning parameters. It allows plug-in motif-finding programs to run either separately or in a batch mode with predetermined parameters, and provides a summary comprising of outputs from multiple programs. Tmod is developed in C++ with the support of Microsoft Foundation Classes and Cygwin. Tmod can also be easily expanded to include future algorithms. AVAILABILITY Tmod is available for download at http://www.fas.harvard.edu/~junliu/Tmod/.

Relationship between Sample Loading Amount and Peptide Identification and Its Effects on Quantitative Proteomics

The relationship between sample loading amount and peptide identification is crucial for the optimization of proteomics experiments, but few studies have addressed this matter. Herein, we present a systematic study using a replicate run strategy to probe the inherent influence of both peptide physicochemical properties and matrix effects on the relationship between peptide identification and sample loading amounts, as well as its applications in protein quantification. Ten replicate runs for a series of laddered loading amounts (ranging between 0.01 approximately 10 microg) of total digested proteins from Saccharomyces cerevisiae were performed with nanoscale liquid chromatography coupled with linear ion trap/Fourier transform ion cyclotron resonance (nanoLC-LTQ-FT) to obtain a nearly saturated peptide identification. This permitted us to differentiate the linear correlativity of peptide identification by the commonly used peptide quantitative index, the area of constructed ion chromatograms (XIC) (SA, from MS and tandem MS data) in the given experiments. The absolute loading amount of a given complex sample affected the final qualitative identification result; thus, optimization of the sample loading amount before every proteomics study was essential. Peptide physicochemical properties had little effect on the linear correlativity between SA-based peptide quantification and loading amount. The matrix effects, rather than the static physicochemical properties of individual peptides, affect peptide measurability. We also quantified the target protein by selecting peptides with good parallel linear correlativity based upon SA as signature peptides and revised the data by multiplying by the reciprocal of the slope coefficient. We found that this optimized the linear protein abundance relativity at every amount range and thus extended the linear dynamic range of label-free quantification. This empirical rule for linear peptide selection (ERLPS) can be adopted to correct comparison results in proteolytic peptide-based quantitative proteomics, such as accurate mass tag (AMT) and targeted quantitative proteomics, as well as in tag-labeled comparative proteomics.

LFQuant: A label-free fast quantitative analysis tool for high-resolution LC-MS/MS proteomics data

Database searching based methods for label‐free quantification aim to reconstruct the peptide extracted ion chromatogram based on the identification information, which can limit the search space and thus make the data processing much faster. The random effect of the MS/MS sampling can be remedied by cross‐assignment among different runs. Here, we present a new label‐free fast quantitative analysis tool, LFQuant, for high‐resolution LC‐MS/MS proteomics data based on database searching. It is designed to accept raw data in two common formats (mzXML and Thermo RAW), and database search results from mainstream tools (MASCOT, SEQUEST, and X!Tandem), as input data. LFQuant can handle large‐scale label‐free data with fractionation such as SDS‐PAGE and 2D LC. It is easy to use and provides handy user interfaces for data loading, parameter setting, quantitative analysis, and quantitative data visualization. LFQuant was compared with two common quantification software packages, MaxQuant and IDEAL‐Q, on the replication data set and the UPS1 standard data set. The results show that LFQuant performs better than them in terms of both precision and accuracy, and consumes significantly less processing time. LFQuant is freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/lfquant/.

SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods

SUMMARY With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses. AVAILABILITY AND IMPLEMENTATION SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.

A nonparametric model for quality control of database search results in shotgun proteomics

BackgroundAnalysis of complex samples with tandem mass spectrometry (MS/MS) has become routine in proteomic research. However, validation of database search results creates a bottleneck in MS/MS data processing. Recently, methods based on a randomized database have become popular for quality control of database search results. However, a consequent problem is the ignorance of how to combine different database search scores to improve the sensitivity of randomized database methods.ResultsIn this paper, a multivariate nonlinear discriminate function (DF) based on the multivariate nonparametric density estimation technique was used to filter out false-positive database search results with a predictable false positive rate (FPR). Application of this method to control datasets of different instruments (LCQ, LTQ, and LTQ/FT) yielded an estimated FPR close to the actual FPR. As expected, the method was more sensitive when more features were used. Furthermore, the new method was shown to be more sensitive than two commonly used methods on 3 complex sample datasets and 3 control datasets.ConclusionUsing the nonparametric model, a more flexible DF can be obtained, resulting in improved sensitivity and good FPR estimation. This nonparametric statistical technique is a powerful tool for tackling the complexity and diversity of datasets in shotgun proteomics.

Mass Measurement Errors of Fourier-Transform Mass Spectrometry (FTMS): Distribution, Recalibration, and Application

The hybrid linear trap quadrupole Fourier-transform (LTQ-FT) ion cyclotron resonance mass spectrometer, an instrument with high accuracy and resolution, is widely used in the identification and quantification of peptides and proteins. However, time-dependent errors in the system may lead to deterioration of the accuracy of these instruments, negatively influencing the determination of the mass error tolerance (MET) in database searches. Here, a comprehensive discussion of LTQ/FT precursor ion mass error is provided. On the basis of an investigation of the mass error distribution, we propose an improved recalibration formula and introduce a new tool, FTDR (Fourier-transform data recalibration), that employs a graphic user interface (GUI) for automatic calibration. It was found that the calibration could adjust the mass error distribution to more closely approximate a normal distribution and reduce the standard deviation (SD). Consequently, we present a new strategy, LDSF (Large MET database search and small MET filtration), for database search MET specification and validation of database search results. As the name implies, a large-MET database search is conducted and the search results are then filtered using the statistical MET estimated from high-confidence results. By applying this strategy to a standard protein data set and a complex data set, we demonstrate the LDSF can significantly improve the sensitivity of the result validation procedure.

Construction and Analyses of Human Large-Scale Tissue Specific Networks

Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network.

A Proteomics Strategy for the Identification of FAT10-Modified Sites by Mass Spectrometry

The ubiquitin-like protein FAT10 (HLA-F adjacent transcript 10) is uniquely expressed in mammals. The fat10 gene is encoded in the MHC class I locus in the human genome and is related to some specific processes, such as apoptosis, immune response, and cancer. However, biological knowledge of FAT10 is limited, owing to the lack of identification of its conjugates. FAT10 covalently modifies proteins in eukaryotes, but only a few substrates of FAT10 have been reported until now, and no FATylated sites have been identified. Here, we report the proteome-scale identification of FATylated proteins by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 175 proteins with high confidence as FATylated candidates. A total of 13 modified sites were identified for the first time by a modified search of the raw MS data. The modified sites were highly enriched with hydrophilic amino acids. Furthermore, the FATylation processes of hnRNP C2, PCNA, and PDIA3 were verified by a coimmunoprecipitation assay. We confirmed that most of the substrates were covalently attached to a FAT10 monomer. The functional distribution of the FAT10 targets suggests that FAT10 participates in various biological processes, such as translation, protein folding, RNA processing, and macromolecular complex assembly. These results should be very useful for investigating the biological functions of FAT10.