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Results for “Computational proteomics”

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PubMedDate not supplied

Proteomic 2DE database for spot selection, automated annotation, and data analysis.

We present a software solution that enables faster and more accurate data analysis of 2DE/MALDI TOF MS data. The software supports data analysis through a number of automated data selection functions and advanced graphical tools. Once protein identities are determined using MALDI TOF MS, automated data retrieval from online databases provides biological information. The software, called 2DDB, reduces analysis time to a fraction without losing any quality compared to more manual data analysis. The database contains over 100,000 data entries, and selected parts can be reached at http://2ddb.org.

Animals↗
PubMed2006

Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics.

Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein's subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases.

Animals↗
PubMed2005

Proteomic approaches in the search for disease biomarkers.

Significant technological advances in protein chemistry, physics and computer sciences in the last two decades have greatly improved protein separation methodologies, such as electrophoresis and chromatography, and have established mass spectrometry (MS) as an indispensable tool for protein study. The goal of this review is to provide a brief overview of the recent improvements in these methodologies and present examples from their application in proteome analysis and search for disease biomarkers.

Biomarkers↗
PubMed2006

[Advances in plant proteomics--I. Key techniques of proteome].

With the completion of genome sequences of the model plants, such as rice (Oryza sativa L.) and Arabidopsis thaliana, it has come into plant functional genomics era, which becomes a hard base of the appearance and development of plant proteomics. This review focuses on the background of proteomics, concept of proteomics and key techniques of proteomics. The key techniques of proteomics include separation, such as 2-DE (Two-Dimensional Electrophoresis), RP-HPLC (Reverse Phase High Performance Liquid Chromatography) and SELDI (Surface Enhanced Laser Desorption/ Ionization) protein chip, mass spectrometry, such as MALDI-TOF-MS (Matrix Assisted Laser Desorption/Ionization-Time Of Flight-Mass Spectrometry) and ESI-MS/MS (Electrospray Ionization Mass Spectrometry/Mass Spectrometry), databases related to proteomics, quantitative proteome, TAP (Tandem Affinity Purification) and yeast two-hybrid system. Challenges and prospects of proteomic techniques are discussed.

Computational Biology↗
PubMed2006

An SVM scorer for more sensitive and reliable peptide identification via tandem mass spectrometry.

Tandem mass spectrometry (MS/MS) has become increasingly important and indispensable in high-throughput proteomics for identifying complex protein mixtures. Database searching is the standard method to accomplish this purpose. A key sub-routine, peptide identification, is used to generate a list of candidate peptides from a protein database according to an experimental MS/MS spectrum, and then validate these candidate peptides for protein identification. Although currently there are many algorithms for peptide identification, most of them either lack an effective validation module or only validate the first-ranked peptide, thus leading to a low identification reliability or sensitivity. This paper proposes a new algorithm, named pepReap, to overcome the above drawbacks. It consists of a two-layered scoring scheme based on machine learning. The first layer is a rough scoring function which uses some simple and heuristic factors to measure the degree of the matches between an experimental MS/MS spectrum and the candidate peptides; thus a ranked list of candidate peptides is generated at a relatively low computational cost. The second layer is a fine scoring function which re-ranks the candidate peptides generated in the first layer and determines which one among them is the true positive. The fine scoring function was designed based on support vector machines (SVMs) using more comprehensive factors, such as the correlations between ions, the mass matching errors of fragment and peptide ions, etc. Consequently, the SVM classifier serves as not only a scorer but also a validation module. Experimental comparison with the popular SEQUEST algorithm coupled with threshold validation criteria on a reported dataset demonstrates that the pepReap algorithm achieves higher performance in terms of identification sensitivity with comparable precision.

Algorithms↗
PubMed2005

SpecAlign--processing and alignment of mass spectra datasets.

SUMMARY: Pre-processing of chromatographic profile or mass spectral data is an important aspect of many types of proteomics and biomarker discovery experiments. Here we present a graphical computational tool, SpecAlign, that enables simultaneous visualization and manipulation of multiple datasets. SpecAlign not only provides all common processing functions, but also uniquely implements an algorithm that enables the complete alignment of each mass spectrum within a loaded dataset. We demonstrate its utility by aligning two datasets each containing six spectra; one set was acquired prior to instrument calibration and the other following calibration. AVAILABILITY: The software is free of charge and available for download from http://ptcl.chem.ox.ac.uk/~jwong/specalign. Supports Windows operating systems including Windows 9X/NT/2000/XP.

Algorithms↗
PubMed2002

Protein-interaction networks: from experiments to analysis.

Functional proteomics approaches aim to characterize comprehensively the function of gene products, and provide a first-level understanding of cellular mechanisms. Here, we review recent techniques for the construction and prediction of large-scale protein-interaction networks, with a particular emphasis on computational processing steps and comparative assessment of the reliability and completeness of the various approaches. We also discuss the use of protein-interaction network information in functional annotation and in the generation of higher-level biological hypotheses on pathways.

Computational Biology↗
PubMed2005

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization.

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.

Computer Graphics↗
PubMed2004

Fluorescent two-dimensional difference gel electrophoresis unveils the potential of gel-based proteomics.

Comparing different proteomes by classical two-dimensional electrophoresis is challenging and often complicated by substantial gel-to-gel variation. Separating two or more protein samples labelled with different fluorescent dyes in one single gel, as in two-dimensional difference gel electrophoresis, reduces this variability considerably. Recent technological innovations, specifically the introduction of a pooled internal standard, even further improve the quantification accuracy and statistical confidence of this method. In addition, decreasing the sample complexity by one of several protein or organelle fractionation procedures increases the number of spots investigated by this protein differential display methodology.

Animals↗
PubMed2007

Prediction of error associated with false-positive rate determination for peptide identification in large-scale proteomics experiments using a combined reverse and forward peptide sequence database strategy.

In recent years, a variety of approaches have been developed using decoy databases to empirically assess the error associated with peptide identifications from large-scale proteomics experiments. We have developed an approach for calculating the expected uncertainty associated with false-positive rate determination using concatenated reverse and forward protein sequence databases. After explaining the theoretical basis of our model, we compare predicted error with the results of experiments characterizing a series of mixtures containing known proteins. In general, results from characterization of known proteins show good agreement with our predictions. Finally, we consider how these approaches may be applied to more complicated data sets, as when peptides are separated by charge state prior to false-positive determination.

Algorithms↗
PubMed2000

Parasite proteomics.

Proteomics offers a new set of tools for investigating parasites and parasite-associated disease. In this article, John Barrett, Jim Jefferies and Peter Brophy describe the key technologies involved, including two-dimensional gel electrophoresis, image analysis, biological mass spectroscopy and database searching. The potential applications of proteomics in drug and vaccine discovery are reviewed, as are possible future developments.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2004

Differential proteomics: an overview of gel and non-gel based approaches.

The comprehensive analysis of gene expression in complex biological systems has demanded the development of new technologies to study the cell transcriptome and the cell proteome. Each approach has advantages and disadvantages from both the conceptual and the methodological viewpoints. Differential proteomics, the comparison of distinct proteomes (eg normal versus diseased cells, diseased versus treated cells etc) is of paramount importance. Several approaches can be used and these typically involve electrophoresis and/or chromatography combined with chemical or metabolic labelling and mass spectrometry. These approaches aim to identify molecular targets, namely proteins, involved in different physiopathological states. Incorporating this knowledge with knowledge from other technologies lays the foundations of active principles at the molecular level. Here, the various gel- and non-gel-based approaches that are used in a wide range of biological systems for the study of differentially expressed proteins will be reviewed.

Algorithms↗
PubMed2004

Improving large-scale proteomics by clustering of mass spectrometry data.

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.

Algorithms↗
PubMed2003

Mining the biomedical literature in the genomic era: an overview.

The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of genomics and proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years, there has been a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature and find the nuggets of information most relevant and useful for specific analysis tasks. This paper provides a road map to the various literature-mining methods, both in general and within bioinformatics. It surveys the disciplines involved in unstructured-text analysis, categorizes current work in biomedical literature mining with respect to these disciplines, and provides examples of text analysis methods applied towards meeting some of the current challenges in bioinformatics.

Biomedical Research↗
PubMed2005

PROTEIOS: an open source proteomics initiative.

SUMMARY: PROTEIOS is an initiative for the development of a comprehensive open source system for storage, organization, analysis and annotation of proteomics experiments. The PROTEIOS platform is based on commonly acknowledged principles for proteomics data publishing. AVAILABILITY: http://www.proteios.org

Algorithms↗
PubMed2006

springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an 'information landscape'.

The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on 'spring embedding' and an 'information landscape' allowing several related data sources to be dynamically combined while highlighting one particular feature. Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The 'information landscape' highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.

Algorithms↗