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PubMed2006

KemaDom: a web server for domain prediction using kernel machine with local context.

Predicting domains of proteins is an important and challenging problem in computational biology because of its significant role in understanding the complexity of proteomes. Although many template-based prediction servers have been developed, ab initio methods should be designed and further improved to be the complementarity of the template-based methods. In this paper, we present a novel domain prediction system KemaDom by ensembling three kernel machines with the local context information among neighboring amino acids. KemaDom, an alternative ab initio predictor, can achieve high performance in predicting the number of domains in proteins. It is freely accessible at http://www.iipl.fudan.edu.cn/lschen/kemadom.htm and http://www.iipl.fudan.edu.cn/~lschen/kemadom.htm.

Artificial Intelligence↗
PubMed2006

MAPU: Max-Planck Unified database of organellar, cellular, tissue and body fluid proteomes.

Mass spectrometry (MS)-based proteomics has become a powerful technology to map the protein composition of organelles, cell types and tissues. In our department, a large-scale effort to map these proteomes is complemented by the Max-Planck Unified (MAPU) proteome database. MAPU contains several body fluid proteomes; including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities and is freely available at http://www.mapuproteome.com using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

Animals↗
PubMed2006

SUBA: the Arabidopsis Subcellular Database.

Knowledge of protein localisation contributes towards our understanding of protein function and of biological inter-relationships. A variety of experimental methods are currently being used to produce localisation data that need to be made accessible in an integrated manner. Chimeric fluorescent fusion proteins have been used to define subcellular localisations with at least 1100 related experiments completed in Arabidopsis. More recently, many studies have employed mass spectrometry to undertake proteomic surveys of subcellular components in Arabidopsis yielding localisation information for approximately 2600 proteins. Further protein localisation information may be obtained from other literature references to analysis of locations (AmiGO: approximately 900 proteins), location information from Swiss-Prot annotations (approximately 2000 proteins); and location inferred from gene descriptions (approximately 2700 proteins). Additionally, an increasing volume of available software provides location prediction information for proteins based on amino acid sequence. We have undertaken to bring these various data sources together to build SUBA, a SUBcellular location database for Arabidopsis proteins. The localisation data in SUBA encompasses 10 distinct subcellular locations, >6743 non-redundant proteins and represents the proteins encoded in the transcripts responsible for 51% of Arabidopsis expressed sequence tags. The SUBA database provides a powerful means by which to assess protein subcellular localisation in Arabidopsis (http://www.suba.bcs.uwa.edu.au).

Arabidopsis Proteins↗
PubMed2004

Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition.

Membrane proteins are generally classified into the following five types: (1) type I membrane proteins, (2) type II membrane proteins, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins and (5) GPI-anchored membrane proteins. Prediction of membrane protein types has become one of the growing hot topics in bioinformatics. Currently, we are facing two critical challenges in this area: first, how to take into account the extremely complicated sequence-order effects, and second, how to deal with the highly uneven sizes of the subsets in a training dataset. In this paper, stimulated by the concept of using the pseudo-amino acid composition to incorporate the sequence-order effects, the spectral analysis technique is introduced to represent the statistical sample of a protein. Based on such a framework, the weighted support vector machine (SVM) algorithm is applied. The new approach has remarkable power in dealing with the bias caused by the situation when one subset in the training dataset contains many more samples than the other. The new method is particularly useful when our focus is aimed at proteins belonging to small subsets. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, indicating that the current approach may serve as a powerful complementary tool to other existing methods for predicting the types of membrane proteins.

Algorithms↗
PubMed2006

Integrated state evaluation for the images of crystallization droplets utilizing linear and nonlinear classifiers.

In a usual crystallization process, the researchers evaluate the protein crystallization growth states based on visual impressions and repeatedly assign scores throughout the growth process. Although the development of crystallization robotic systems has generally realised the automation of the setup and storage of crystallization samples, evaluation of crystallization states has not yet been completely automated. The method presented here attempts to categorize individual crystallization droplet images into five classes using multiple classifiers. In particular, linear and nonlinear classifiers are utilized. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis (LDA) and support vector machine (SVM). The performance of this method has been evaluated by comparing the results obtained using the method with the results obtained by a human expert and the concordance rate was 84.4%.

Algorithms↗
PubMed2006

Quantitative proteomics and its applications for systems biology.

Here we discuss the current state of research in the rapidly growing field of quantitative proteomics and its applications to systems biology. Quantitative proteomics can be successfully used for characterizing alterations in protein abundance, finding novel protein-protein and protein-peptide interactions, investigating formation of large macromolecular complexes, and elucidating temporal changes in organellar protein composition and phosphorylation in signal transduction cascades. Further, quantitative proteomics can directly compare activation of entire signaling networks in response to individual stimuli and discover critical differences in their circuits that account for alterations of cell response. Maturation of proteomic bioinformatics applications and continuous improvements in proteomics and related genomics and transcriptomics technologies now allows us to investigate cellular mechanisms at the integrative system level.

Animals↗
PubMed2004

Kernel-based data fusion and its application to protein function prediction in yeast.

Kernel methods provide a principled framework in which to represent many types of data, including vectors, strings, trees and graphs. As such, these methods are useful for drawing inferences about biological phenomena. We describe a method for combining multiple kernel representations in an optimal fashion, by formulating the problem as a convex optimization problem that can be solved using semidefinite programming techniques. The method is applied to the problem of predicting yeast protein functional classifications using a support vector machine (SVM) trained on five types of data. For this problem, the new method performs better than a previously-described Markov random field method, and better than the SVM trained on any single type of data.

Algorithms↗
PubMed2003

Semantic similarity measures as tools for exploring the gene ontology.

Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annotation. In many cases this data has been hard to model, and has been represented as scientific natural language, which is not readily computationally amenable. The development of the Gene Ontology provides us with a more accessible representation of some of this data. However it is not clear how this data can best be searched, or queried. Recently we have adapted information content based measures for use with the Gene Ontology (GO). In this paper we present detailed investigation of the properties of these measures, and examine various properties of GO, which may have implications for its future design.

Classification↗
PubMed2004

A functional hierarchical organization of the protein sequence space.

BACKGROUND: It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity. RESULTS: In this paper we show that ProtoNet correctly captures functional and structural aspects of the protein world. Furthermore, a novel feature is an automatic procedure that reduces the tree to 12% its original size. This procedure utilizes only parameters intrinsic to the clustering process. Despite the substantial reduction in size, the system's predictive power concerning biological functions is hardly affected. We then carry out an automatic comparison with existing functional protein annotations. Consequently, 78% of the clusters in the compressed tree (5,300 clusters) get assigned a biological function with a high confidence. The clustering and compression processes are unsupervised, and robust. CONCLUSIONS: We present an automatically generated unbiased method that provides a hierarchical classification of all currently known proteins.

Algorithms↗
PubMed2006

Effects of storage time and exogenous protease inhibitors on plasma protein levels.

Plasma biomarker analysis requires intact unbiased starting material. We analyzed the effects on plasma protein profiles of protease inhibitor cocktails and preprocessing storage. Plasma from 12 healthy subjects collected with and without protease inhibitors was prepared immediately and after 2 hours of room temperature storage. The samples were analyzed by a multiplexed enzyme-linked immunosorbent assay that captured 99 chemokines and cytokines. Unsupervised hierarchical analysis clustered the samples into 4 groups; one composed predominantly of samples processed immediately, another of samples processed after 2 hours, and the remaining two were a mix of immediate and 2-hour samples. The mixed and immediate group protein profiles were similar. However, among the immediate and 2-hour samples, the levels of 37 factors differed significantly: all were greater after 2 hours. The dramatic changes in protein levels during storage were independent of protease inhibitors and are likely due to cytokine production and/or release by leukocytes and platelets.

Blood Preservation↗
PubMed2006

Information management for the study of allergies.

Microarrays and other large-scale screening technologies produce quantities of increasingly complex allergy data. These data link molecular and clinical measurements and observations and provide fertile ground for improving our understanding of the processes involved in allergic reactions. Information technology is employed in gathering, storage, retrieval and analysis of these data. The increasing proportion of allergy data are generated from genomics and proteomics approaches. The major activity focuses on characterization of allergens including IgE reactivity, structural properties, and mapping of IgE and T-cell epitopes. Because of the complexity of allergy data, their utilization requires bioinformatics approaches. Allergen data are stored in the general and specialist databases. At least a dozen of important allergen databases and data repositories have been developed to date. These data are analysed using general and specialist bioinformatics tools. The major applications of bioinformatics include support for allergen characterization, assessment of allergenicity, and identification of allergic cross-reactivity. These applications in turn support the development of vaccines and therapies for allergic disease. In this article we review allergen databases and tools for the analysis of allergens, and discuss the new directions in the field supported by large scale screening involving genomics, proteomics, and bioinformatics support.

Allergens↗
PubMed2002

Pathway logic: symbolic analysis of biological signaling.

The genomic sequencing of hundreds of organisms including homo sapiens, and the exponential growth in gene expression and proteomic data for many species has revolutionized research in biology. However, the computational analysis of these burgeoning datasets has been hampered by the sparse successes in combinations of data sources, representations, and algorithms. Here we propose the application of symbolic toolsets from the formal methods community to problems of biological interest, particularly signaling pathways, and more specifically mammalian mitogenic and stress responsive pathways. The results of formal symbolic analysis with extremely efficient representations of biological networks provide insights with potential biological impact. In particular, novel hypotheses may be generated which could lead to wet lab validation of new signaling possibilities. We demonstrate the graphic representation of the results of formal analysis of pathways, including navigational abilities, and describe the logical underpinnings of the approach. In summary, we propose and provide an initial description of an algebra and logic of signaling pathways and biologically plausible abstractions that provide the foundation for the application of high-powered tools such as model checkers to problems of biological interest.

Animals↗
PubMed2004

[Bioinformatics].

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Computational Biology↗