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PubMed2006

On the statistical analysis of the GS-NS0 cell proteome: imputation, clustering and variability testing.

We have undertaken two-dimensional gel electrophoresis proteomic profiling on a series of cell lines with different recombinant antibody production rates. Due to the nature of gel-based experiments not all protein spots are detected across all samples in an experiment, and hence datasets are invariably incomplete. New approaches are therefore required for the analysis of such graduated datasets. We approached this problem in two ways. Firstly, we applied a missing value imputation technique to calculate missing data points. Secondly, we combined a singular value decomposition based hierarchical clustering with the expression variability test to identify protein spots whose expression correlates with increased antibody production. The results have shown that while imputation of missing data was a useful method to improve the statistical analysis of such data sets, this was of limited use in differentiating between the samples investigated, and highlighted a small number of candidate proteins for further investigation.

Algorithms↗
PubMed2004

A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Amino Acid Sequence↗
PubMed2002

The application of 2D gel-based proteomics methods to the study of breast cancer.

The protein complement of breast cells consists of many thousands of proteins. Recent developments in 2D gel electrophoresis technology have made studies requiring the quantitative analysis of a differential proteome, such as comparison between normal and malignant cells or investigation of drug effects on cells, truly feasible. Computer software plays a central part in the comparisons between multiple gels required for such experiments. In addition, software tools allow patterns of coexpression of proteins to be studied, offering potential insights into protein regulation, interactions, and functions, especially when combined with complementary data on gene expression. In this paper, the technology and limitations of 2D gel-based proteomics are reviewed. Techniques for comparing sets of gels at a global level as well as identifying specific protein features that differentiate gels are discussed. Our own experience of studying the breast cell proteome is used to illustrate the difficulties and achievements of differential proteomics.

Breast Neoplasms↗
PubMedDate not supplied

Francisella tularensis proteome: low levels of ASB-14 facilitate the visualization of membrane proteins in total protein extracts.

Proteomic analysis of bacterial pathogens isolated from in vivo sources, such as infected tissues, provides many challenges not the least of which is the limited quantity of sample available for analysis. It is, therefore, highly desirable to develop a one-step cellular lysis and protein solubilization method that minimizes protein losses and allows the maximum possible coverage of the proteome. Here, we have used standard sample buffer constituents including urea, thiourea and DTT, but varied the detergent composition of the buffers in order to achieve the best quality of gels and the greatest spot resolution. We found that the most efficient solubilizing solution in this case consisted of 7 M urea, 2 M thiourea, 1% DTT, 0.5% amidosulfobetaine-14 (ASB-14) and 4% 3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS). Inclusion of low levels of ASB-14 in solutions allowed visualization of a subset of 24 new protein spots in the Live Vaccine Strain (LVS) of Francisella tularensis and 21 spots in a virulent A-strain of the pathogen. Further investigation showed that 15 of the 24 enriched LVS spots were membrane or membrane-associated proteins suggesting that the optimized lysis and solubilization solution aids in the detection of more hydrophobic proteins. This methodology is now being applied to the analysis of Francisella obtained from in vivo sources.

Bacterial Proteins↗
PubMedDate not supplied

Predicting enzyme subclass by functional domain composition and pseudo amino acid composition.

As a continuous effort to use the sequence approach to identify enzymatic function at a deeper level, investigations are extended from the main enzyme classes (Protein Sci. 2004, 13, 2857-2863) to their subclasses. This is indispensable if we wish to understand the molecular mechanism of an enzyme at a deeper level. For each of the 6 main enzyme classes (i.e., oxidoreductase, transferase, hydrolase, lyase, isomerase, and ligase), a subclass training dataset is constructed. To reduce homologous bias, a stringent cutoff was imposed that all the entries included in the datasets have less than 40% sequence identity to each other. To catch the core feature that is intimately related to the biological function, the sample of a protein is represented by hybridizing the functional domain composition and pseudo amino acid composition. On the basis of such a hybridization representation, the FunD-PseAA predictor is established. It is demonstrated by the jackknife cross-validation tests that the overall success rate in identifying the 21 subclasses of oxidoreductases is above 86%, and the corresponding rates in identifying the subclasses of the other 5 main enzyme classes are 94-97%. The high success rates imply that the FunD-PseAA predictor may become a useful tool in bioinformatics and proteomics of the post-genomic era.

Amino Acid Sequence↗
PubMed2004

VirGen: a comprehensive viral genome resource.

VirGen is a comprehensive viral genome resource that organizes the 'sequence space' of viral genomes in a structured fashion. It has been developed with the objective of serving as an annotated and curated database comprising complete genome sequences of viruses, value-added derived data and data mining tools. The current release (v1.1) contains 559 complete genomes in addition to 287 putative genomes of viruses belonging to eight viral families for which the host range includes animals and plants. Viral genomes in VirGen are annotated using sequence-based Bioinformatics approaches. The genomic data is also curated to identify 'alternate names' of viral proteins, where available. VirGen archives the results of comparisons of genomes, proteomes and individual proteins within and between viral species. It is the first resource to provide phylogenetic trees of viral species computed using whole-genome sequence data. The module of predicted B-cell antigenic determinants in VirGen is an attempt to link the genome to its vaccinome. Comparative genome analysis data facilitate the study of genome organization and evolution of viruses, which would have implications in applied research to identify candidates for the design of vaccines and antiviral drugs. VirGen is a relational database and is available at http://bioinfo. ernet.in/virgen/virgen.html.

Antigens, Viral↗
PubMed2005

A robust, streamlined, and reproducible method for proteomic analysis of serum by delipidation, albumin and IgG depletion, and two-dimensional gel electrophoresis.

Serum is a readily available source for diagnostic assays, but the identification of disease-specific serum biomarkers has been impeded by the dominance of human serum albumin and immunoglobulins (Igs) in the serum proteome. There is a need to reduce the technical variation in serum processing and analysis to allow for a reproducible analysis of large cohorts. To this end, we have developed a rapid and reproducible procedure for sample preparation and high-resolution two-dimensional gel electrophoresis to analyze human serum. Serum is centrifuged at high speed to remove lipids and aggregated proteins, incubated with protein G resin to remove IgG, precipitated with NaCl/ethanol to deplete albumin, and slowly resolubilized in a sodium dodecyl sulfate (SDS)/N-(2-hydroxyethyl)piperazine-2'-(2-ethanesulfonic acid) (HEPES) buffer. The delipidated and IgG/albumin depleted serum proteins are focused on pH 4-7 linear large immobilized pH gradient strips, and then resolved by Bis-Tris SDS-polyacrylamide gel electrophoresis. The robustness and reproducibility of the optimized procedure was determined for three individual serum samples on three consecutive days. An image analysis of the nine silver-stained gels demonstrated that the intensity and localization of protein spots are highly reproducible. Our IgG and albumin depletion procedure will aid in screening the patient sera for normal biological variation and disease-specific biomarkers.

Blood Proteins↗
PubMed2004

Mitochondriomics or what makes us breathe.

Mitochondria perform several fundamental cellular processes in higher eukaryotes including oxidative phosphorylation, Fe/S cluster formation and apoptosis. Dysfunction of the organelle is associated with a wide range of human diseases. To gain a better understanding of mitochondrial function, several recent proteomic, genetic, transcriptomic and bioinformatic approaches have set out to determine the complete set of mitochondrially located proteins in yeast, plants and mammals. Here, we review these studies and discuss the advances and limitations of individual strategies. Integration of various approaches proves to be a successful and useful way to identify the mitochondrial proteome with high sensitivity and specificity. The most comprehensive dataset is available for Saccharomyces cerevisiae, giving an estimated number of 700 different proteins located in mitochondria.

Animals↗
PubMed2005

Molecular weight assessment of proteins in total proteome profiles using 1D-PAGE and LC/MS/MS.

BACKGROUND: The observed molecular weight of a protein on a 1D polyacrylamide gel can provide meaningful insight into its biological function. Differences between a protein's observed molecular weight and that predicted by its full length amino acid sequence can be the result of different types of post-translational events, such as alternative splicing (AS), endoproteolytic processing (EPP), and post-translational modifications (PTMs). The characterization of these events is one of the important goals of total proteome profiling (TPP). LC/MS/MS has emerged as one of the primary tools for TPP, but since this method identifies tryptic fragments of proteins, it has not generally been used for large-scale determination of the molecular weight of intact proteins in complex mixtures. RESULTS: We have developed a set of computational tools for extracting molecular weight information of intact proteins from total proteome profiles in a high throughput manner using 1D-PAGE and LC/MS/MS. We have applied this technology to the proteome profile of a human lymphoblastoid cell line under standard culture conditions. From a total of 1 x 10(7) cells, we identified 821 proteins by at least two tryptic peptides. Additionally, these 821 proteins are well-localized on the 1D-SDS gel. 656 proteins (80%) occur in gel slices in which the observed molecular weight of the protein is consistent with its predicted full-length sequence. A total of 165 proteins (20%) are observed to have molecular weights that differ from their predicted full-length sequence. We explore these molecular-weight differences based on existing protein annotation. CONCLUSION: We demonstrate that the determination of intact protein molecular weight can be achieved in a high-throughput manner using 1D-PAGE and LC/MS/MS. The ability to determine the molecular weight of intact proteins represents a further step in our ability to characterize gene expression at the protein level. The identification of 165 proteins whose observed molecular weight differs from the molecular weight of the predicted full-length sequence provides another entry point into the high-throughput characterization of protein modification.

Journal Article↗
PubMed2004

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

Breast Neoplasms↗
PubMed2005

A query language for biological networks.

MOTIVATION: Many areas of modern biology are concerned with the management, storage, visualization, comparison and analysis of networks, but no appropriate query language for such complex data structures yet exists. RESULTS: We have designed and implemented the pathway query language (PQL) for querying large protein interaction or pathway databases. PQL is based on a simple graph data model with extensions reflecting properties of biological objects. Queries match subgraphs in the database based on node properties and paths between nodes. The syntax is easy to learn for anybody familiar with SQL. As an important feature, a query may require a certain structure in the database to exist but return a different subgraph. We have tested PQL queries on networks of up to 16,000 nodes and found it to scale very well. AVAILABILITY: The code is available on request from the author.

Computational Biology↗
PubMed2004

Preparation of human heart for laser microdissection and proteomics.

Proteomics generates information on expressed proteins, and laser microdissection (LMD) is a method that allows enrichment of specific cell types from complex heterogeneous tissue. Together they provide a powerful tool for functional genomic research. Here, we have investigated (i) the effects of fixation and staining on cardiac proteins separated by two-dimensional gel electrophoresis (2-DE) and (ii) feasibility of using LMD to separately prepare myocytes and blood vessels for 2-DE gel analysis. This is the first such study of human heart. The effect of fixation (ethanol or acetone), staining with haematoxylin and eosin in the presence and absence of xylene, and antibody staining was investigated. Proteins were separated by 2-DE and spots detected by silver staining. Quantitative spot analysis showed that contractile proteins were preserved under all conditions, and no significant differences were found when the groups studied were compared with the control group. However, there were differences in the visual quality of the gel patterns. LMD provided enough protein from blood vessels and myocytes to run one large-format (18 x 24 cm) 2-D gel for each subset of cells. Collection of this material took 70 h (approximately 2800 blood vessels and 17,000 myocytes) and resulted in tissue-specific gel patterns for these two structures. In conclusion, the use of haematoxylin and eosin staining without xylene provided the best morphology and did not significantly affect protein spot number.

Cells, Cultured↗
PubMed2006

Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data.

BACKGROUND: Translating a known metabolic network into a dynamic model requires reasonable guesses of all enzyme parameters. In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made. RESULTS: We compute posterior distributions of kinetic parameters within a Bayesian framework, based on integration of kinetic, thermodynamic, metabolic, and proteomic data. The structure of the metabolic system (i.e., stoichiometries and enzyme regulation) needs to be known, and the reactions are modelled by convenience kinetics with thermodynamically independent parameters. The parameter posterior is computed in two separate steps: a first posterior summarises the available data on enzyme kinetic parameters; an improved second posterior is obtained by integrating metabolic fluxes, concentrations, and enzyme concentrations for one or more steady states. The data can be heterogeneous, incomplete, and uncertain, and the posterior is approximated by a multivariate log-normal distribution. We apply the method to a model of the threonine synthesis pathway: the integration of metabolic data has little effect on the marginal posterior distributions of individual model parameters. Nevertheless, it leads to strong correlations between the parameters in the joint posterior distribution, which greatly improve the model predictions by the following Monte-Carlo simulations. CONCLUSION: We present a standardised method to translate metabolic networks into dynamic models. To determine the model parameters, evidence from various experimental data is combined and weighted using Bayesian parameter estimation. The resulting posterior parameter distribution describes a statistical ensemble of parameter sets; the parameter variances and correlations can account for missing knowledge, measurement uncertainties, or biological variability. The posterior distribution can be used to sample model instances and to obtain probabilistic statements about the model's dynamic behaviour.

Bayes Theorem↗
PubMed2002

Plant genomics: present state and a perspective on future developments.

The year 2001 may well be called the Year of the Human Genome. Less in the limelight, but equally exciting for plant scientists, is the rapid progress in plant genomics. With relatively modest resources, a lot has been achieved. The Arabidopsis genomic sequence (125 megabases [Mb]) is essentially finished, and rice sequencing is progressing rapidly. For many species, expressed sequence tag (EST) resources are plentiful, allowing broad inter-specific comparisons. At the same time, development of integrated physical-genetic maps for large-genome crop species is not progressing as rapidly as desired, while resources for the complete sequencing of these crops are not likely to become available. Some important plant genomes are so large that their complete sequencing may not be practical for many years. Significant plant genome research is concentrated in industry, and not freely available, creating some frustration in the academic community. Growing interest is anticipated in the development of metabolic profiling technologies, RNA profiling, proteomics and integrated systems approaches to plant biology.

Computational Biology↗
PubMed2005

Biological master games: using biologists' reasoning to guide algorithm development for integrated functional genomics.

We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework.

Algorithms↗
PubMedDate not supplied

Open source system for analyzing, validating, and storing protein identification data.

This paper describes an open-source system for analyzing, storing, and validating proteomics information derived from tandem mass spectrometry. It is based on a combination of data analysis servers, a user interface, and a relational database. The database was designed to store the minimum amount of information necessary to search and retrieve data obtained from the publicly available data analysis servers. Collectively, this system was referred to as the Global Proteome Machine (GPM). The components of the system have been made available as open source development projects. A publicly available system has been established, comprised of a group of data analysis servers and one main database server.

Computational Biology↗