Proceedings of the second annual conference of the MidSouth Computational Biology and Bioinformatics Society. 7-9 October 2004, Little Rock, Arkansas, USA.
Explore the source record for details and available documents.
Computational Biology↗
SEARCH · Search PubMed
Results for “Computational proteomics”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Hierarchical modularity of nested bow-ties in metabolic networks.
BACKGROUND: The exploration of the structural topology and the organizing principles of genome-based large-scale metabolic networks is essential for studying possible relations between structure and functionality of metabolic networks. Topological analysis of graph models has often been applied to study the structural characteristics of complex metabolic networks. RESULTS: In this work, metabolic networks of 75 organisms were investigated from a topological point of view. Network decomposition of three microbes (Escherichia coli, Aeropyrum pernix and Saccharomyces cerevisiae) shows that almost all of the sub-networks exhibit a highly modularized bow-tie topological pattern similar to that of the global metabolic networks. Moreover, these small bow-ties are hierarchically nested into larger ones and collectively integrated into a large metabolic network, and important features of this modularity are not observed in the random shuffled network. In addition, such a bow-tie pattern appears to be present in certain chemically isolated functional modules and spatially separated modules including carbohydrate metabolism, cytosol and mitochondrion respectively. CONCLUSION: The highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident. Identification and analysis of such a pattern is helpful for understanding the design principles and facilitate the modelling of metabolic networks.
Algorithms↗
Pfarao: a web application for protein family analysis customized for cytoskeletal and motor proteins (CyMoBase).
BACKGROUND: Annotation of protein sequences of eukaryotic organisms is crucial for the understanding of their function in the cell. Manual annotation is still by far the most accurate way to correctly predict genes. The classification of protein sequences, their phylogenetic relation and the assignment of function involves information from various sources. This often leads to a collection of heterogeneous data, which is hard to track. Cytoskeletal and motor proteins consist of large and diverse superfamilies comprising up to several dozen members per organism. Up to date there is no integrated tool available to assist in the manual large-scale comparative genomic analysis of protein families. DESCRIPTION: Pfarao (Protein Family Application for Retrieval, Analysis and Organisation) is a database driven online working environment for the analysis of manually annotated protein sequences and their relationship. Currently, the system can store and interrelate a wide range of information about protein sequences, species, phylogenetic relations and sequencing projects as well as links to literature and domain predictions. Sequences can be imported from multiple sequence alignments that are generated during the annotation process. A web interface allows to conveniently browse the database and to compile tabular and graphical summaries of its content. CONCLUSION: We implemented a protein sequence-centric web application to store, organize, interrelate, and present heterogeneous data that is generated in manual genome annotation and comparative genomics. The application has been developed for the analysis of cytoskeletal and motor proteins (CyMoBase) but can easily be adapted for any protein.
Amino Acid Sequence↗
Mining microarray expression data by literature profiling.
BACKGROUND: The rapidly expanding fields of genomics and proteomics have prompted the development of computational methods for managing, analyzing and visualizing expression data derived from microarray screening. Nevertheless, the lack of efficient techniques for assessing the biological implications of gene-expression data remains an important obstacle in exploiting this information. RESULTS: To address this need, we have developed a mining technique based on the analysis of literature profiles generated by extracting the frequencies of certain terms from thousands of abstracts stored in the Medline literature database. Terms are then filtered on the basis of both repetitive occurrence and co-occurrence among multiple gene entries. Finally, clustering analysis is performed on the retained frequency values, shaping a coherent picture of the functional relationship among large and heterogeneous lists of genes. Such data treatment also provides information on the nature and pertinence of the associations that were formed. CONCLUSIONS: The analysis of patterns of term occurrence in abstracts constitutes a means of exploring the biological significance of large and heterogeneous lists of genes. This approach should contribute to optimizing the exploitation of microarray technologies by providing investigators with an interface between complex expression data and large literature resources.
Cluster Analysis↗
The human proteome organization (HUPO) and environmental health.
The Human Proteome Organization, or HUPO, was formed to promote research and large-scale analysis of the human proteome. By consolidating national proteome organizations into an international body, HUPO will coordinate international initiatives, biological resources, protocols, standards and data for studying the human proteome. HUPO has identified five key areas to advance study of the human proteome, specifically in bioinformatics, new technologies, the plasma proteome, cell models, and a public antibody initiative. Consideration of three major issue areas may help develop HUPO's strategy for human proteome study. First is the need to distinguish the value of high throughput platforms from discovery platforms in proteomics. Second is the importance for international planning on integrating both transcriptome and proteome data and databases. Last is that effects of the environment from chemical, physical, and biological exposures alter the expression and structure of the proteome, which become manifest in long-term adverse health effects and disease. Environmental health research stands to greatly benefit from the shared resources, data, and vision of the HUPO organization as a valuable resource in exploiting knowledge of the human proteome toward improving public health.
Computational Biology↗
[World trends of the bioinformatics and the systems biology: rapid change towards the integration of the analyses of genome, protein and biological systems].
Explore the source record for details and available documents.
Computational Biology↗
Proteonomica and bioinformatic.
Explore the source record for details and available documents.
Computational Biology↗
FASSM: enhanced function association in whole genome analysis using sequence and structural motifs.
We present an algorithm to detect remote homology, which arises through circular permutation and discontinuous domains. It is also helpful in detecting small domain proteins that are characterized by few conserved residues. The input to the algorithm is a set of multiply aligned protein sequence profiles. This method, coded as FASSM, examines the sequence conservation and positions of protein family signatures or motifs for the annotation of protein sequences and to facilitate the analysis of their domains. The overall coverage of FASSM is 93% in comparison to other validation tools like HMM and IMPALA. The method is especially useful for difficult relationships such as discontinuous domains during whole-genome surveys and is demonstrated to perform accurate family associations at sequence identities as low as 15%.
Algorithms↗
BIOVERSE: enhancements to the framework for structural, functional and contextual modeling of proteins and proteomes.
We have made a number of enhancements to the previously described Bioverse web server and computational biology framework (http://bioverse.compbio.washington.edu). In this update, we provide an overview of the new features available that include: (i) expansion of the number of organisms represented in the Bioverse and addition of new data sources and novel prediction techniques not available elsewhere, including network-based annotation; (ii) reengineering the database backend and supporting code resulting in significant speed, search and ease-of use improvements; and (iii) creation of a stateful and dynamic web application frontend to improve interface speed and usability. Integrated Java-based applications also allow dynamic visualization of real and predicted protein interaction networks.
Computer Graphics↗
Proteomics research aids cancer diagnosis and treatment.
Explore the source record for details and available documents.
Computer Simulation↗
Evaluation and classification of RING-finger domains encoded by the Arabidopsis genome.
BACKGROUND: In computational analysis, the RING-finger domain is one of the most frequently detected domains in the Arabidopsis proteome. In fact, it is more abundant in Arabidopsis than in other eukaryotic genomes. However, computational analysis might classify ambiguous domains of the closely related PHD and LIM motifs as RING domains by mistake. Thus, we set out to define an ordered set of Arabidopsis RING domains by evaluating predicted domains on the basis of recent structural data. RESULTS: Inspection of the proteome with a current InterPro release predicts 446 RING domains. We evaluated each detected domain and as a result eliminated 59 false positives. The remaining 387 domains were grouped by cluster analysis and according to their metal-ligand arrangement. We further defined novel patterns for additional computational analyses of the proteome. They were based on recent structural data that enable discrimination between the related RING, PHD and LIM domains. These patterns allow us to predict with different degrees of certainty whether a particular domain is indeed likely to form a RING finger. CONCLUSIONS: In summary, 387 domains have a significant potential to form a RING-type cross-brace structure. Many of these RING domains overlap with predicted PHD domains; however, the RING domain signature mostly prevails. Thus, the abundance of PHD domains in Arabidopsis has been significantly overestimated. Cluster analysis of the RING domains defines groups of proteins, which frequently show significant similarity outside the RING domain. These groups document a common evolutionary origin of their members and potentially represent genes of overlapping functionality.
Amino Acid Motifs↗
MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications.
As the speed with which proteomic labs generate data increases along with the scale of projects they are undertaking, the resulting data storage and data processing problems will continue to challenge computational resources. This is especially true for shotgun proteomic techniques that can generate tens of thousands of spectra per instrument each day. One design factor leading to many of these problems is caused by storing spectra and the database identifications for a given spectrum as individual files. While these problems can be addressed by storing all of the spectra and search results in large relational databases, the infrastructure to implement such a strategy can be beyond the means of academic labs. We report here a series of unified text file formats for storing spectral data (MS1 and MS2) and search results (SQT) that are compact, easily parsed by both machine and humans, and yet flexible enough to be coupled with new algorithms and data-mining strategies.
Database Management Systems↗
Profiler: an open web platform for multi-omics analysis.
MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).
Software↗
The Pathway Tools cellular overview diagram and Omics Viewer.
The Pathway Tools cellular overview diagram is a visual representation of the biochemical network of an organism. The overview is automatically created from a Pathway/Genome Database describing that organism. The cellular overview includes metabolic, transport and signaling pathways, and other membrane and periplasmic proteins. Pathway Tools supports interrogation and exploration of cellular biochemical networks through the overview diagram. Furthermore, a software component called the Omics Viewer provides visual analysis of whole-organism datasets using the overview diagram as an organizing framework. For example, gene expression and metabolomics measurements, alone or in combination, can be painted onto the overview, as can computed whole-organism datasets, such as predicted reaction-flux values. The cellular overview and Omics Viewer provide a mechanism whereby biologists can apply the pattern-recognition capabilities of the human visual system to analyze large-scale datasets in a biologically meaningful context. SRI's BioCyc.org website provides overview diagrams for more than 200 organisms. This article describes enhancements to the overview made since a 1999 publication, including the automatic layout capability, expansion of the cellular machinery that it includes, new semantic zooming and poster-generating capabilities, and extension of the Omics Viewer to support painting of metabolites, animations and zooming to individual pathway diagrams.
Computer Graphics↗
Predicting protein subcellular location using digital signal processing.
The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.
Algorithms↗
Bioinformatic insights from metagenomics through visualization.
Cutting-edge biological and bioinformatics research seeks a systems perspective through the analysis of multiple types of high-throughput and other experimental data for the same sample. Systems-level analysis requires the integration and fusion of such data, typically through advanced statistics and mathematics. Visualization is a complementary computational approach that supports integration and analysis of complex data or its derivatives. We present a bioinformatics visualization prototype, Juxter, which depicts categorical information derived from or assigned to these diverse data for the purpose of comparing patterns across categorizations. The visualization allows users to easily discern correlated and anomalous patterns in the data. These patterns, which might not be detected automatically by algorithms, may reveal valuable information leading to insight and discovery. We describe the visualization and interaction capabilities and demonstrate its utility in a new field, metagenomics, which combines molecular biology and genetics to identify and characterize genetic material from multi-species microbial samples.
Algorithms↗
Multidimensional proteomic analysis of proteolytic pathways involved in cell cycle control.
Explore the source record for details and available documents.
Acetone↗
Making blind robots see: the synergy between fluorescent dyes and imaging devices in automated proteomics.
Proteomics investigations endeavor to provide a global understanding of gene product synthesis rate, degradation rate, functional competence, posttranslational modification, subcellular distribution and physical interactions with other cell components. Protein expression encompasses an enormous dynamic range. Since rare proteins cannot be amplified by any type of PCR method, sensitive detection is critical to proteome projects. Fluorescence methods deliver streamlined detection protocols, superior detection sensitivity, broad linear dynamic range and excellent compatibility with modern microchemical identification methods such as mass spectrometry. Two general approaches to fluorescence detection of proteins are currently practiced: the covalent derivatization of proteins with fluorophores or noncovalent interaction of fluorophores either via the SDS micelle or through direct electrostatic interaction with proteins. One approach for quantifying fluorescence is to use a photomultiplier tube detector combined with a laser light scanner. In addition, fluorescence imaging is performed using a charge-coupled device camera combined with a UV light or xenon arc source. Fluorescent dyes with bimodal excitation spectra may be broadly implemented on a wide range of analytical imaging devices, permitting their widespread application to proteomics studies and incorporation into semiautomated analysis environments.
Animals↗