PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-11 (11)
 

Clipboard (0)
None
Journals
Year of Publication
Document Types
1.  Towards the integrated analysis, visualization and reconstruction of microbial gene regulatory networks 
Briefings in Bioinformatics  2008;10(1):75-83.
To handle changing environmental surroundings and to manage unfavorable conditions, microbial organisms have evolved complex transcriptional regulatory networks. To comprehensively analyze these gene regulatory networks, several online available databases and analysis platforms have been implemented and established. In this article, we address the typical cycle of scientific knowledge exploration and integration in the area of procaryotic transcriptional gene regulation. We briefly review five popular, publicly available systems that support (i) the integration of existing knowledge, (ii) visualization capabilities and (iii) computer analysis to predict promising wet lab targets. We exemplify the benefits of such integrated data analysis platforms by means of four application cases exemplarily performed with the corynebacterial reference database CoryneRegNet.
doi:10.1093/bib/bbn055
PMCID: PMC2638629  PMID: 19074493
data integration; systems biology; gene regulation; transcription factor, database
2.  Bringing Web 2.0 to bioinformatics 
Briefings in Bioinformatics  2008;10(1):1-10.
Enabling deft data integration from numerous, voluminous and heterogeneous data sources is a major bioinformatic challenge. Several approaches have been proposed to address this challenge, including data warehousing and federated databasing. Yet despite the rise of these approaches, integration of data from multiple sources remains problematic and toilsome. These two approaches follow a user-to-computer communication model for data exchange, and do not facilitate a broader concept of data sharing or collaboration among users. In this report, we discuss the potential of Web 2.0 technologies to transcend this model and enhance bioinformatics research. We propose a Web 2.0-based Scientific Social Community (SSC) model for the implementation of these technologies. By establishing a social, collective and collaborative platform for data creation, sharing and integration, we promote a web services-based pipeline featuring web services for computer-to-computer data exchange as users add value. This pipeline aims to simplify data integration and creation, to realize automatic analysis, and to facilitate reuse and sharing of data. SSC can foster collaboration and harness collective intelligence to create and discover new knowledge. In addition to its research potential, we also describe its potential role as an e-learning platform in education. We discuss lessons from information technology, predict the next generation of Web (Web 3.0), and describe its potential impact on the future of bioinformatics studies.
doi:10.1093/bib/bbn041
PMCID: PMC2638627  PMID: 18842678
Web 2.0; bioinformatics; scientific social community; web service; pipelines
3.  Simulation of DNA sequence evolution under models of recent directional selection 
Briefings in Bioinformatics  2008;10(1):84-96.
Computer simulation is an essential tool in the analysis of DNA sequence variation for mapping events of recent adaptive evolution in the genome. Various simulation methods are employed to predict the signature of selection in sequence variation. The most informative and efficient method currently in use is coalescent simulation. However, this method is limited to simple models of directional selection. Whole-population forward-in-time simulations are the alternative to coalescent simulations for more complex models. The notorious problem of excessive computational cost in forward-in-time simulations can be overcome by various simplifying amendments. Overall, the success of simulations depends on the creative application of some population genetic theory to the simulation algorithm.
doi:10.1093/bib/bbn048
PMCID: PMC2638626  PMID: 19109303
selective sweep; polymorphism; coalescent simulation; forward-in-time simulation; adaptive evolution; Wright–Fisher model
4.  Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis 
Briefings in Bioinformatics  2008;10(1):11-23.
The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.
doi:10.1093/bib/bbn045
PMCID: PMC2638625  PMID: 18971242
expert system; knowledge-based system; data integration; UIMA; AlexSys; multiple sequence alignment
5.  Models of coding sequence evolution 
Briefings in Bioinformatics  2008;10(1):97-109.
Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. Models of codon evolution have been particularly useful, because, in addition to providing a significant improvement in model realism for protein-coding sequences, codon models can also be designed to test hypotheses about the selective pressures that shape the evolution of the sequences. Such models typically assume a phylogeny and can be used to identify sites or lineages that have evolved adaptively. Recently some of the key assumptions that underlie phylogenetic tests of selection have been questioned, such as the assumption that the rate of synonymous changes is constant across sites or that a single phylogenetic tree can be assumed at all sites for recombining sequences. While some of these issues have been addressed through the development of novel methods, others remain as caveats that need to be considered on a case-by-case basis. Here, we outline the theory of codon models and their application to the detection of positive selection. We review some of the more recent developments that have improved their power and utility, laying a foundation for further advances in the modeling of coding sequence evolution.
doi:10.1093/bib/bbn049
PMCID: PMC2638624  PMID: 18971241
maximum likelihood; phylogenetics; evolutionary models; selection
6.  Web-based applications for building, managing and analysing kinetic models of biological systems 
Briefings in Bioinformatics  2008;10(1):65-74.
Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.
doi:10.1093/bib/bbn039
PMCID: PMC2638623  PMID: 18805901
systems biology; web-based applications; kinetic modelling; dynamic simulation
7.  Gene-set analysis and reduction 
Briefings in Bioinformatics  2008;10(1):24-34.
Gene-set analysis aims to identify differentially expressed gene sets (pathways) by a phenotype in DNA microarray studies. We review here important methodological aspects of gene-set analysis and illustrate them with varying performance of several methods proposed in the literature. We emphasize the importance of distinguishing between ‘self-contained’ versus ‘competitive’ methods, following Goeman and Bühlmann. We also discuss reducing a gene set to its subset, consisting of ‘core members’ that chiefly contribute to the statistical significance of the differential expression of the initial gene set by phenotype. Significance analysis of microarray for gene-set reduction (SAM-GSR) can be used for an analytical reduction of gene sets to their core subsets. We apply SAM-GSR on a microarray dataset for identifying biological gene sets (pathways) whose gene expressions are associated with p53 mutation in cancer cell lines. Codes to implement SAM-GSR in the statistical package R can be downloaded from http://www.ualberta.ca/~yyasui/homepage.html.
doi:10.1093/bib/bbn042
PMCID: PMC2638622  PMID: 18836208
DNA microarray; gene sets; gene set n; multivariate means; pathways; significance analysis of microarray; two-sample test
8.  Experience using web services for biological sequence analysis 
Briefings in Bioinformatics  2008;9(6):493-505.
Programmatic access to data and tools through the web using so-called web services has an important role to play in bioinformatics. In this article, we discuss the most popular approaches based on SOAP/WS-I and REST and describe our, a cross section of the community, experiences with providing and using web services in the context of biological sequence analysis. We briefly review main technological approaches as well as best practice hints that are useful for both users and developers. Finally, syntactic and semantic data integration issues with multiple web services are discussed.
doi:10.1093/bib/bbn029
PMCID: PMC2989672  PMID: 18621748
web services; SOAP; REST; internet technologies; sequence analysis
9.  VisANT: an integrative framework for networks in systems biology 
Briefings in bioinformatics  2008;9(4):317-325.
The essence of a living cell is adaptation to a changing environment, and a central goal of modern cell biology is to understand adaptive change under normal and pathological conditions. Because the number of components is large, and processes and conditions are many, visual tools are useful in providing an overview of relations that would otherwise be far more difficult to assimilate. Historically, representations were static pictures, with genes and 10 proteins represented as nodes, and known or inferred correlations between them (links) represented by various kinds of lines. The modern challenge is to capture functional hierarchies and adaptation to environmental change, and to discover pathways and processes embedded in known data, but not currently recognizable. Among the tools being developed to meet this challenge is VisANT (freely available at http://visant.bu.edu) which integrates, mines and displays hierarchical information. Challenges to integrating modeling (discrete or continuous) and simulation capabilities into such visual mining software are briefly discussed.
doi:10.1093/bib/bbn020
PMCID: PMC2743399  PMID: 18463131
network; integration; systems biology; metagraph; visualization
10.  Penalized feature selection and classification in bioinformatics 
Briefings in Bioinformatics  2008;9(5):392-403.
In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classification techniques—which belong to the family of embedded feature selection methods—for bioinformatics studies with high-dimensional input. Classification objective functions, penalty functions and computational algorithms are discussed. Our goal is to make interested researchers aware of these feature selection and classification methods that are applicable to high-dimensional bioinformatics data.
doi:10.1093/bib/bbn027
PMCID: PMC2733190  PMID: 18562478
bioinformatics application; feature selection; penalization
11.  MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences 
Briefings in bioinformatics  2008;9(4):299-306.
The Molecular Evolutionary Genetics Analysis (MEGA) software is a desktop application designed for comparative analysis of homologous gene sequences either from multigene families or from different species with a special emphasis on inferring evolutionary relationships and patterns of DNA and protein evolution. In addition to the tools for statistical analysis of data, MEGA provides many convenient facilities for the assembly of sequence data sets from files or web-based repositories, and it includes tools for visual presentation of the results obtained in the form of interactive phylogenetic trees and evolutionary distance matrices. Here we discuss the motivation, design principles, and priorities that have shaped the development of MEGA. We also discuss how MEGA might evolve in the future to assist researchers in their growing need to analyze large dataset using new computational methods.
doi:10.1093/bib/bbn017
PMCID: PMC2562624  PMID: 18417537
phylogenetics; genome; evolution; software

Results 1-11 (11)