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1.  Integrating shotgun proteomics and mRNA expression data to improve protein identification 
Bioinformatics  2009;25(11):1397-1403.
Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.
Results: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by ∼40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19–63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.
Availability and Implementation: Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from http://www.marcottelab.org/MSpresso/.
Contact: marcotte@icmb.utexas.edu; miranker@cs.utexas.edu
Supplementary Information: Supplementary data website: http://www.marcottelab.org/MSpresso/.
doi:10.1093/bioinformatics/btp168
PMCID: PMC2682515  PMID: 19318424
2.  mspire: mass spectrometry proteomics in Ruby 
Bioinformatics  2008;24(23):2796-2797.
Summary: Mass spectrometry-based proteomics stands to gain from additional analysis of its data, but its large, complex datasets make demands on speed and memory usage requiring special consideration from scripting languages. The software library ‘mspire’—developed in the Ruby programming language—offers quick and memory-efficient readers for standard xml proteomics formats, converters for intermediate file types in typical proteomics spectral-identification work flows (including the Bioworks .srf format), and modules for the calculation of peptide false identification rates.
Availability: Freely available at http://mspire.rubyforge.org. Additional data models, usage information, and methods available at http://bioinformatics.icmb.utexas.edu/mspire
Contact: marcotte@icmb.utexas.edu
doi:10.1093/bioinformatics/btn513
PMCID: PMC2639276  PMID: 18930952
3.  Revisiting the negative example sampling problem for predicting protein–protein interactions 
Bioinformatics  2011;27(21):3024-3028.
Motivation: A number of computational methods have been proposed that predict protein–protein interactions (PPIs) based on protein sequence features. Since the number of potential non-interacting protein pairs (negative PPIs) is very high both in absolute terms and in comparison to that of interacting protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs for training and validation. Hence, the need arises for subset sampling for negative PPIs.
Results: We clarify that there are two fundamentally different types of subset sampling for negative PPIs. One is subset sampling for cross-validated testing, where one desires unbiased subsets so that predictive performance estimated with them can be safely assumed to generalize to the population level. The other is subset sampling for training, where one desires the subsets that best train predictive algorithms, even if these subsets are biased. We show that confusion between these two fundamentally different types of subset sampling led one study recently published in Bioinformatics to the erroneous conclusion that predictive algorithms based on protein sequence features are hardly better than random in predicting PPIs. Rather, both protein sequence features and the ‘hubbiness’ of interacting proteins contribute to effective prediction of PPIs. We provide guidance for appropriate use of random versus balanced sampling.
Availability: The datasets used for this study are available at http://www.marcottelab.org/PPINegativeDataSampling.
Contact: yungki@mail.utexas.edu; marcotte@icmb.utexas.edu
Supplementary Information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr514
PMCID: PMC3198576  PMID: 21908540
4.  Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer 
Bioinformatics  2016;32(9):1373-1379.
Motivation: Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets.
Results: Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targets for therapy and are found to be involved in splicing, translation and protein folding. In particular, many NSCLC cell lines were especially sensitive to the loss of components of the LSm2-8 protein complex or the CCT/TRiC chaperonin. Different vulnerabilities were also found for different cell line subgroups. Furthermore, the predicted vulnerability of a single adenocarcinoma cell line to loss of the Wnt pathway was experimentally validated with screening of small-molecule Wnt inhibitors against an extensive cell line panel.
Availability and implementation: The clustering algorithm is implemented in Python and is freely available at https://bitbucket.org/youngjh/nsclc_paper.
Contact: marcotte@icmb.utexas.edu or jon.young@utexas.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btw010
PMCID: PMC4848405  PMID: 26755624
5.  Inductive matrix completion for predicting gene–disease associations 
Bioinformatics  2014;30(12):i60-i68.
Motivation: Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies—for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies—for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene–disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive.
Results: Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better—it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has <15% chance. We demonstrate that the inductive method is particularly effective for a query disease with no previously known gene associations, and for predicting novel genes, i.e. genes that are previously not linked to diseases. Thus the method is capable of predicting novel genes even for well-characterized diseases. We also validate the novelty of predictions by evaluating the method on recently reported OMIM associations and on associations recently reported in the literature.
Availability: Source code and datasets can be downloaded from http://bigdata.ices.utexas.edu/project/gene-disease.
Contact: naga86@cs.utexas.edu
doi:10.1093/bioinformatics/btu269
PMCID: PMC4058925  PMID: 24932006
6.  Application of new multi-resolution methods for the comparison of biomolecular electrostatic properties in the absence of global structural similarity 
In this paper we present a method for the multi-resolution comparison of biomolecular electrostatic potentials without the need for global structural alignment of the biomolecules. The underlying computational geometry algorithm uses multi-resolution attributed contour trees (MACTs) to compare the topological features of volumetric scalar fields. We apply the MACTs to compute electrostatic similarity metrics for a large set of protein chains with varying degrees of sequence, structure, and function similarity. For calibration, we also compute similarity metrics for these chains by a more traditional approach based upon 3D structural alignment and analysis of Carbo similarity indices. Moreover, because the MACT approach does not rely upon pairwise structural alignment, its accuracy and efficiency promises to perform well on future large-scale classification efforts across groups of structurally-diverse proteins. The MACT method discriminates between protein chains at a level comparable to the Carbo similarity index method; i.e., it is able to accurately cluster proteins into functionally-relevant groups which demonstrate strong dependence on ligand binding sites. The results of the analyses are available from the linked web databases http://ccvweb.cres.utexas.edu/MolSignature/ and http://agave.wustl.edu/similarity/. The MACT analysis tools are available as part of the public domain library of the Topological Analysis and Quantitative Tools (TAQT) from the Center of Computational Visualization, at the University of Texas at Austin (http://ccvweb.csres.utexas.edu/software). The Carbo software is available for download with the open-source APBS software package at http://apbs.sf.net/.
doi:10.1137/050647670
PMCID: PMC2561295  PMID: 18841247
electrostatic; contour tree; similarity; clustering; Poisson-Boltzmann
7.  A dynamic data structure for flexible molecular maintenance and informatics 
Bioinformatics  2010;27(1):55-62.
Motivation: We present the ‘Dynamic Packing Grid’ (DPG), a neighborhood data structure for maintaining and manipulating flexible molecules and assemblies, for efficient computation of binding affinities in drug design or in molecular dynamics calculations.
Results: DPG can efficiently maintain the molecular surface using only linear space and supports quasi-constant time insertion, deletion and movement (i.e. updates) of atoms or groups of atoms. DPG also supports constant time neighborhood queries from arbitrary points. Our results for maintenance of molecular surface and polarization energy computations using DPG exhibit marked improvement in time and space requirements.
Availability: http://www.cs.utexas.edu/~bajaj/cvc/software/DPG.shtml
Contact: bajaj@cs.utexas.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq627
PMCID: PMC3008647  PMID: 21115440
8.  Structural and functional protein network analyses predict novel signaling functions for rhodopsin 
Proteomic analyses, literature mining, and structural data were combined to generate an extensive signaling network linked to the visual G protein-coupled receptor rhodopsin. Network analysis suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking.
Using a shotgun proteomic approach, we identified the protein inventory of the light sensing outer segment of the mammalian photoreceptor.These data, combined with literature mining, structural modeling, and computational analysis, offer a comprehensive view of signal transduction downstream of the visual G protein-coupled receptor rhodopsin.The network suggests novel signaling branches downstream of rhodopsin to cytoskeleton dynamics and vesicular trafficking.The network serves as a basis for elucidating physiological principles of photoreceptor function and suggests potential disease-associated proteins.
Photoreceptor cells are neurons capable of converting light into electrical signals. The rod outer segment (ROS) region of the photoreceptor cells is a cellular structure made of a stack of around 800 closed membrane disks loaded with rhodopsin (Liang et al, 2003; Nickell et al, 2007). In disc membranes, rhodopsin arranges itself into paracrystalline dimer arrays, enabling optimal association with the heterotrimeric G protein transducin as well as additional regulatory components (Ciarkowski et al, 2005). Disruption of these highly regulated structures and processes by germline mutations is the cause of severe blinding diseases such as retinitis pigmentosa, macular degeneration, or congenital stationary night blindness (Berger et al, 2010).
Traditionally, signal transduction networks have been studied by combining biochemical and genetic experiments addressing the relations among a small number of components. More recently, large throughput experiments using different techniques like two hybrid or co-immunoprecipitation coupled to mass spectrometry have added a new level of complexity (Ito et al, 2001; Gavin et al, 2002, 2006; Ho et al, 2002; Rual et al, 2005; Stelzl et al, 2005). However, in these studies, space, time, and the fact that many interactions detected for a particular protein are not compatible, are not taken into consideration. Structural information can help discriminate between direct and indirect interactions and more importantly it can determine if two or more predicted partners of any given protein or complex can simultaneously bind a target or rather compete for the same interaction surface (Kim et al, 2006).
In this work, we build a functional and dynamic interaction network centered on rhodopsin on a systems level, using six steps: In step 1, we experimentally identified the proteomic inventory of the porcine ROS, and we compared our data set with a recent proteomic study from bovine ROS (Kwok et al, 2008). The union of the two data sets was defined as the ‘initial experimental ROS proteome'. After removal of contaminants and applying filtering methods, a ‘core ROS proteome', consisting of 355 proteins, was defined.
In step 2, proteins of the core ROS proteome were assigned to six functional modules: (1) vision, signaling, transporters, and channels; (2) outer segment structure and morphogenesis; (3) housekeeping; (4) cytoskeleton and polarity; (5) vesicles formation and trafficking, and (6) metabolism.
In step 3, a protein-protein interaction network was constructed based on the literature mining. Since for most of the interactions experimental evidence was co-immunoprecipitation, or pull-down experiments, and in addition many of the edges in the network are supported by single experimental evidence, often derived from high-throughput approaches, we refer to this network, as ‘fuzzy ROS interactome'. Structural information was used to predict binary interactions, based on the finding that similar domain pairs are likely to interact in a similar way (‘nature repeats itself') (Aloy and Russell, 2002). To increase the confidence in the resulting network, edges supported by a single evidence not coming from yeast two-hybrid experiments were removed, exception being interactions where the evidence was the existence of a three-dimensional structure of the complex itself, or of a highly homologous complex. This curated static network (‘high-confidence ROS interactome') comprises 660 edges linking the majority of the nodes. By considering only edges supported by at least one evidence of direct binary interaction, we end up with a ‘high-confidence binary ROS interactome'. We next extended the published core pathway (Dell'Orco et al, 2009) using evidence from our high-confidence network. We find several new direct binary links to different cellular functional processes (Figure 4): the active rhodopsin interacts with Rac1 and the GTP form of Rho. There is also a connection between active rhodopsin and Arf4, as well as PDEδ with Rab13 and the GTP-bound form of Arl3 that links the vision cycle to vesicle trafficking and structure. We see a connection between PDEδ with prenyl-modified proteins, such as several small GTPases, as well as with rhodopsin kinase. Further, our network reveals several direct binary connections between Ca2+-regulated proteins and cytoskeleton proteins; these are CaMK2A with actinin, calmodulin with GAP43 and S1008, and PKC with 14-3-3 family members.
In step 4, part of the network was experimentally validated using three different approaches to identify physical protein associations that would occur under physiological conditions: (i) Co-segregation/co-sedimentation experiments, (ii) immunoprecipitations combined with mass spectrometry and/or subsequent immunoblotting, and (iii) utilizing the glycosylated N-terminus of rhodopsin to isolate its associated protein partners by Concanavalin A affinity purification. In total, 60 co-purification and co-elution experiments supported interactions that were already in our literature network, and new evidence from 175 co-IP experiments in this work was added. Next, we aimed to provide additional independent experimental confirmation for two of the novel networks and functional links proposed based on the network analysis: (i) the proposed complex between Rac1/RhoA/CRMP-2/tubulin/and ROCK II in ROS was investigated by culturing retinal explants in the presence of an ROCK II-specific inhibitor (Figure 6). While morphology of the retinas treated with ROCK II inhibitor appeared normal, immunohistochemistry analyses revealed several alterations on the protein level. (ii) We supported the hypothesis that PDEδ could function as a GDI for Rac1 in ROS, by demonstrating that PDEδ and Rac1 co localize in ROS and that PDEδ could dissociate Rac1 from ROS membranes in vitro.
In step 5, we use structural information to distinguish between mutually compatible (‘AND') or excluded (‘XOR') interactions. This enables breaking a network of nodes and edges into functional machines or sub-networks/modules. In the vision branch, both ‘AND' and ‘XOR' gates synergize. This may allow dynamic tuning of light and dark states. However, all connections from the vision module to other modules are ‘XOR' connections suggesting that competition, in connection with local protein concentration changes, could be important for transmitting signals from the core vision module.
In the last step, we map and functionally characterize the known mutations that produce blindness.
In summary, this represents the first comprehensive, dynamic, and integrative rhodopsin signaling network, which can be the basis for integrating and mapping newly discovered disease mutants, to guide protein or signaling branch-specific therapies.
Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein–protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.
doi:10.1038/msb.2011.83
PMCID: PMC3261702  PMID: 22108793
protein interaction network; rhodopsin signaling; structural modeling
9.  Assigning spectrum-specific P-values to protein identifications by mass spectrometry 
Bioinformatics  2011;27(8):1128-1134.
Motivation: Although many methods and statistical approaches have been developed for protein identification by mass spectrometry, the problem of accurate assessment of statistical significance of protein identifications remains an open question. The main issues are as follows: (i) statistical significance of inferring peptide from experimental mass spectra must be platform independent and spectrum specific and (ii) individual spectrum matches at the peptide level must be combined into a single statistical measure at the protein level.
Results: We present a method and software to assign statistical significance to protein identifications from search engines for mass spectrometric data. The approach is based on asymptotic theory of order statistics. The parameters of the asymptotic distributions of identification scores are estimated for each spectrum individually. The method relies on new unbiased estimators for parameters of extreme value distribution. The estimated parameters are used to assign a spectrum-specific P-value to each peptide-spectrum match. The protein-level confidence measure combines P-values of peptide-to-spectrum matches.
Conclusion: We extensively tested the method using triplicate mouse and yeast high-throughput proteomic experiments. The proposed statistical approach improves the sensitivity of protein identifications without compromising specificity. While the method was primarily designed to work with Mascot, it is platform-independent and is applicable to any search engine which outputs a single score for a peptide-spectrum match. We demonstrate this by testing the method in conjunction with X!Tandem.
Availability: The software is available for download at ftp://genetics.bwh.harvard.edu/SSPV/.
Contact: ssunyaev@rics.bwh.harvard.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr089
PMCID: PMC3072553  PMID: 21349864
10.  ScanRanker: Quality Assessment of Tandem Mass Spectra via Sequence Tagging 
Journal of proteome research  2011;10(7):2896-2904.
In shotgun proteomics, protein identification by tandem mass spectrometry relies on bioinformatics tools. Despite recent improvements in identification algorithms, a significant number of high quality spectra remain unidentified for various reasons. Here we present ScanRanker, an open-source tool that evaluates the quality of tandem mass spectra via sequence tagging with reliable performance in data from different instruments. The superior performance of ScanRanker enables it not only to find unassigned high quality spectra that evade identification through database search, but also to select spectra for de novo sequencing and cross-linking analysis. In addition, we demonstrate that the distribution of ScanRanker scores predicts the richness of identifiable spectra among multiple LC-MS/MS runs in an experiment, and ScanRanker scores assist the process of peptide assignment validation to increase confident spectrum identifications. The source code and executable versions of ScanRanker are available from http://fenchurch.mc.vanderbilt.edu.
doi:10.1021/pr200118r
PMCID: PMC3128668  PMID: 21520941
spectral quality; sequence tagging; bioinformatics; tandem mass spectrometry; cross-linking
11.  IDPicker 2.0: Improved Protein Assembly with High Discrimination Peptide Identification Filtering 
Journal of proteome research  2009;8(8):3872-3881.
Tandem mass spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. A number of database searching algorithms have been developed to assign peptide sequences to tandem mass spectra. Assembling the peptide identifications to proteins, however, is a challenging issue because many peptides are shared among multiple proteins. IDPicker is an open-source protein assembly tool that derives a minimum protein list from peptide identifications filtered to a specified False Discovery Rate. Here, we update IDPicker to increase confident peptide identifications by combining multiple scores produced by database search tools. By segregating peptide identifications for thresholding using both the precursor charge state and the number of tryptic termini, IDPicker retrieves more peptides for protein assembly. The new version is more robust against false positive proteins, especially in searches using multispecies databases, by requiring additional novel peptides in the parsimony process. IDPicker has been designed for incorporation in many identification workflows by the addition of a graphical user interface and the ability to read identifications from the pepXML format. These advances position IDPicker for high peptide discrimination and reliable protein assembly in large-scale proteomics studies. The source code and binaries for the latest version of IDPicker are available from http://fenchurch.mc.vanderbilt.edu/.
doi:10.1021/pr900360j
PMCID: PMC2810655  PMID: 19522537
bioinformatics; parsimony; protein assembly; protein inference; false discovery rate
12.  DACTAL: divide-and-conquer trees (almost) without alignments 
Bioinformatics  2012;28(12):i274-i282.
Motivation: While phylogenetic analyses of datasets containing 1000–5000 sequences are challenging for existing methods, the estimation of substantially larger phylogenies poses a problem of much greater complexity and scale.
Methods: We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset. DACTAL combines iteration with a novel divide-and-conquer approach, so that each iteration begins with a tree produced in the prior iteration, decomposes the taxon set into overlapping subsets, estimates trees on each subset, and then combines the smaller trees into a tree on the full taxon set using a new supertree method. We prove that DACTAL is guaranteed to produce the true tree under certain conditions. We compare DACTAL to SATé and maximum likelihood trees on estimated alignments using simulated and real datasets with 1000–27 643 taxa.
Results: Our studies show that on average DACTAL yields more accurate trees than the two-phase methods we studied on very large datasets that are difficult to align, and has approximately the same accuracy on the easier datasets. The comparison to SATé shows that both have the same accuracy, but that DACTAL achieves this accuracy in a fraction of the time. Furthermore, DACTAL can analyze larger datasets than SATé, including a dataset with almost 28 000 sequences.
Availability: DACTAL source code and results of dataset analyses are available at www.cs.utexas.edu/users/phylo/software/dactal.
Contact: tandy@cs.utexas.edu
doi:10.1093/bioinformatics/bts218
PMCID: PMC3371850  PMID: 22689772
13.  Network-based inference from complex proteomic mixtures using SNIPE 
Bioinformatics  2012;28(23):3115-3122.
Motivation: Proteomics presents the opportunity to provide novel insights about the global biochemical state of a tissue. However, a significant problem with current methods is that shotgun proteomics has limited success at detecting many low abundance proteins, such as transcription factors from complex mixtures of cells and tissues. The ability to assay for these proteins in the context of the entire proteome would be useful in many areas of experimental biology.
Results: We used network-based inference in an approach named SNIPE (Software for Network Inference of Proteomics Experiments) that selectively highlights proteins that are more likely to be active but are otherwise undetectable in a shotgun proteomic sample. SNIPE integrates spectral counts from paired case–control samples over a network neighbourhood and assesses the statistical likelihood of enrichment by a permutation test. As an initial application, SNIPE was able to select several proteins required for early murine tooth development. Multiple lines of additional experimental evidence confirm that SNIPE can uncover previously unreported transcription factors in this system. We conclude that SNIPE can enhance the utility of shotgun proteomics data to facilitate the study of poorly detected proteins in complex mixtures.
Availability and Implementation: An implementation for the R statistical computing environment named snipeR has been made freely available at http://genetics.bwh.harvard.edu/snipe/.
Contact: ssunyaev@rics.bwh.harvard.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/bts594
PMCID: PMC3509492  PMID: 23060611
14.  PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data 
Bioinformatics  2013;29(19):2435-2444.
Motivation: Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed.
Results: We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed ∼78% of the yeast mitogen-activated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified ∼27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase–phosphatase interactions overlapped with ∼59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from high-throughput data.
Availability: PhosphoChain is implemented in Java and available at http://virgo.csie.ncku.edu.tw/PhosphoChain/ or http://aitchisonlab.com/PhosphoChain
Contact: john.aitchison@systemsbiology.org or jchiang@mail.ncku.edu.tw
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt387
PMCID: PMC3777105  PMID: 23832245
15.  TIPP: taxonomic identification and phylogenetic profiling 
Bioinformatics  2014;30(24):3548-3555.
Motivation: Abundance profiling (also called ‘phylogenetic profiling’) is a crucial step in understanding the diversity of a metagenomic sample, and one of the basic techniques used for this is taxonomic identification of the metagenomic reads.
Results: We present taxon identification and phylogenetic profiling (TIPP), a new marker-based taxon identification and abundance profiling method. TIPP combines SAT\'e-enabled phylogenetic placement a phylogenetic placement method, with statistical techniques to control the classification precision and recall, and results in improved abundance profiles. TIPP is highly accurate even in the presence of high indel errors and novel genomes, and matches or improves on previous approaches, including NBC, mOTU, PhymmBL, MetaPhyler and MetaPhlAn.
Availability and implementation: Software and supplementary materials are available at http://www.cs.utexas.edu/users/phylo/software/sepp/tipp-submission/.
Contact: warnow@illinois.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu721
PMCID: PMC4253836  PMID: 25359891
16.  A comprehensive and scalable database search system for metaproteomics 
BMC Genomics  2016;17:642.
Background
Mass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations.
Results
Our approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed “Blazmass”) to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy and allowing for a more in-depth characterization of the functional landscape of the samples.
Conclusions
The combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomic search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-016-2855-3) contains supplementary material, which is available to authorized users.
doi:10.1186/s12864-016-2855-3
PMCID: PMC4986259  PMID: 27528457
Proteomics; Metaproteomics; Microbiome; Proteomic search engine; Database; MongoDB
17.  OnlineCall: fast online parameter estimation and base calling for illumina's next-generation sequencing 
Bioinformatics  2012;28(13):1677-1683.
Motivation: Next-generation DNA sequencing platforms are becoming increasingly cost-effective and capable of providing enormous number of reads in a relatively short time. However, their accuracy and read lengths are still lagging behind those of conventional Sanger sequencing method. Performance of next-generation sequencing platforms is fundamentally limited by various imperfections in the sequencing-by-synthesis and signal acquisition processes. This drives the search for accurate, scalable and computationally tractable base calling algorithms capable of accounting for such imperfections.
Results: Relying on a statistical model of the sequencing-by-synthesis process and signal acquisition procedure, we develop a computationally efficient base calling method for Illumina's sequencing technology (specifically, Genome Analyzer II platform). Parameters of the model are estimated via a fast unsupervised online learning scheme, which uses the generalized expectation–maximization algorithm and requires only 3 s of running time per tile (on an Intel i7 machine @3.07GHz, single core)—a three orders of magnitude speed-up over existing parametric model-based methods. To minimize the latency between the end of the sequencing run and the generation of the base calling reports, we develop a fast online scalable decoding algorithm, which requires only 9 s/tile and achieves significantly lower error rates than the Illumina's base calling software. Moreover, it is demonstrated that the proposed online parameter estimation scheme efficiently computes tile-dependent parameters, which can thereafter be provided to the base calling algorithm, resulting in significant improvements over previously developed base calling methods for the considered platform in terms of performance, time/complexity and latency.
Availability: A C code implementation of our algorithm can be downloaded from http://www.cerc.utexas.edu/OnlineCall/
Contact: hvikalo@ece.utexas.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/bts256
PMCID: PMC3381969  PMID: 22569177
18.  ASTRAL: genome-scale coalescent-based species tree estimation 
Bioinformatics  2014;30(17):i541-i548.
Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions.
Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees.
Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral.
Contact: warnow@illinois.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu462
PMCID: PMC4147915  PMID: 25161245
19.  Protein-Protein Docking with F2Dock 2.0 and GB-Rerank 
PLoS ONE  2013;8(3):e51307.
Motivation
Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error.
Results
The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other.
Availability
The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml.
doi:10.1371/journal.pone.0051307
PMCID: PMC3590208  PMID: 23483883
20.  Detecting differential protein expression in large-scale population proteomics 
Bioinformatics  2014;30(19):2741-2746.
Motivation: Mass spectrometry (MS)-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides. Another issue is that different MS experiments of the same study have significantly varying numbers of peptides quantified, which can result in more missing peptide abundances in an experiment that has a smaller total number of quantified peptides. To detect as many biomarker proteins as possible, it is necessary to develop bioinformatics methods that appropriately handle these challenges.
Results: We propose a Significance Analysis for Large-scale Proteomics Studies (SALPS) that handles missing peptide intensity values caused by the two mechanisms mentioned above. Our model has a robust performance in both simulated data and proteomics data from a large clinical study. Because varying patients’ sample qualities and deviating instrument performances are not avoidable for clinical studies performed over the course of several years, we believe that our approach will be useful to analyze large-scale clinical proteomics data.
Availability and Implementation: R codes for SALPS are available at http://www.stanford.edu/%7eclairesr/software.html.
Contact: wenzhong.xiao@mgh.harvard.edu
Supplementary information: Supplementary materials are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu341
PMCID: PMC4173009  PMID: 24928210
21.  NeuroPedia: neuropeptide database and spectral library 
Bioinformatics  2011;27(19):2772-2773.
Summary: Neuropeptides are essential for cell–cell communication in neurological and endocrine physiological processes in health and disease. While many neuropeptides have been identified in previous studies, the resulting data has not been structured to facilitate further analysis by tandem mass spectrometry (MS/MS), the main technology for high-throughput neuropeptide identification. Many neuropeptides are difficult to identify when searching MS/MS spectra against large protein databases because of their atypical lengths (e.g. shorter/longer than common tryptic peptides) and lack of tryptic residues to facilitate peptide ionization/fragmentation. NeuroPedia is a neuropeptide encyclopedia of peptide sequences (including genomic and taxonomic information) and spectral libraries of identified MS/MS spectra of homolog neuropeptides from multiple species. Searching neuropeptide MS/MS data against known NeuroPedia sequences will improve the sensitivity of database search tools. Moreover, the availability of neuropeptide spectral libraries will also enable the utilization of spectral library search tools, which are known to further improve the sensitivity of peptide identification. These will also reinforce the confidence in peptide identifications by enabling visual comparisons between new and previously identified neuropeptide MS/MS spectra.
Availability: http://proteomics.ucsd.edu/Software/NeuroPedia.html
Contact: bandeira@ucsd.edu
Supplementary information: Supplementary materials are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr445
PMCID: PMC3179654  PMID: 21821666
22.  Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times 
Journal of proteome research  2013;12(12):10.1021/pr400705q.
In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes, but fragments only a fraction of them. In the subsequent analyses, only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all the peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features, and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate datasets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data, and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to re-analyze and increase the yield of existing datasets.
Principle contribution
A statistical framework that uses the unfragmented MS1-features to increase the confidence of the proteins identified in shotgun experiments.
doi:10.1021/pr400705q
PMCID: PMC3860378  PMID: 24074221
23.  Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification 
Bioinformatics  2008;24(13):i348-i356.
Motivation: Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms.
Results: We use a hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) approach to address these two problems. We train a set of DBNs on high-confidence peptide-spectrum matches. These DBNs, known collectively as Riptide, comprise a probabilistic model of peptide fragmentation chemistry. Examination of the distributions learned by Riptide allows identification of new trends, such as prevalent a-ion fragmentation at peptide cleavage sites C-term to hydrophobic residues. In addition, Riptide can be used to produce likelihood scores that indicate whether a given peptide-spectrum match is correct. A vector of such scores is evaluated by an SVM, which produces a final score to be used in peptide identification. Using Riptide in this way yields improved discrimination when compared to other state-of-the-art MS/MS identification algorithms, increasing the number of positive identifications by as much as 12% at a 1% false discovery rate.
Availability: Python and C source code are available upon request from the authors. The curated training sets are available at http://noble.gs.washington.edu/proj/intense/. The Graphical Model Tool Kit (GMTK) is freely available at http://ssli.ee.washington.edu/bilmes/gmtk.
Contact:noble@gs.washington.edu
doi:10.1093/bioinformatics/btn189
PMCID: PMC2665034  PMID: 18586734
24.  Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification 
Bioinformatics (Oxford, England)  2008;24(13):i348-i356.
Motivation
Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms.
Results
We use a hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) approach to address these two problems. We train a set of DBNs on high-confidence peptide-spectrum matches. These DBNs, known collectively as Riptide, comprise a probabilistic model of peptide fragmentation chemistry. Examination of the distributions learned by Riptide allows identification of new trends, such as prevalent a-ion fragmentation at peptide cleavage sites C-term to hydrophobic residues. In addition, Riptide can be used to produce likelihood scores that indicate whether a given peptide-spectrum match is correct. A vector of such scores is evaluated by an SVM, which produces a final score to be used in peptide identification. Using Riptide in this way yields improved discrimination when compared to other state-of-the-art MS/MS identification algorithms, increasing the number of positive identifications by as much as 12% at a 1% false discovery rate.
Availability
Python and C source code are available upon request from the authors. The curated training sets are available at http://noble.gs.washington.edu/proj/intense/. The Graphical Model Tool Kit (GMTK) is freely available at http://ssli.ee.washington.edu/bilmes/gmtk.
Contact
noble@gs.washington.edu
doi:10.1093/bioinformatics/btn189
PMCID: PMC2665034  PMID: 18586734
25.  Assessment of resolution parameters for CID-based shotgun proteomic experiments on the LTQ-Orbitrap mass spectrometer 
Shotgun proteomics has been used extensively for characterization of a number of proteomes. High resolution Fourier transform mass spectrometry (FTMS) has emerged as a powerful tool owing to its high mass accuracy and resolving power. One of its major limitations, however, is that the confidence level of peptide identification and sensitivity cannot be maximized simultaneously. Although it is generally assumed that higher resolution is better for peptide identifications, the precise effect of varying resolution as a parameter on peptide identification has not yet been systematically evaluated. We used the Escherichia coli proteome and a standard 48 protein mix to study the effect of different resolution parameters on peptide identifications in the setting of a shotgun proteomics experiment on an LTQ-Orbitrap mass spectrometer. We observed a higher number of peptide-spectrum matches (PSMs) whenever the MS scan was carried out by FT and the MS/MS in the ion-trap (IT) with the maximum PSMs obtained at an MS resolution of 30,000. In contrast, when samples were analyzed by FT for both MS and MS/MS, the number of PSMs was significantly lower (~40% as compared to FT-IT experiments) with the maximum PSMs obtained when both the MS and MS/MS resolution were set to 15,000. Thus, a 15K-15K resolution setting may provide the best compromise for studies where both speed and accuracy such as high-throughput post-translational analysis and de novo sequencing are important. We hope that our study will allow researchers to choose between different resolution parameters to achieve their desired results from proteomic analyses.
doi:10.1016/j.jasms.2010.04.011
PMCID: PMC3030983  PMID: 20483638
FTMS; duty cycle; E. coli proteome; PSM

Results 1-25 (1110201)