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PubMed2006

EGassembler: online bioinformatics service for large-scale processing, clustering and assembling ESTs and genomic DNA fragments.

Expressed sequence tag (EST) sequencing has proven to be an economically feasible alternative for gene discovery in species lacking a draft genome sequence. Ongoing large-scale EST sequencing projects feel the need for bioinformatics tools to facilitate uniform EST handling. This brings about a renewed importance for a universal tool for processing and functional annotation of large sets of ESTs. EGassembler (http://egassembler.hgc.jp/) is a web server, which provides an automated as well as a user-customized analysis tool for cleaning, repeat masking, vector trimming, organelle masking, clustering and assembling of ESTs and genomic fragments. The web server is publicly available and provides the community a unique all-in-one online application web service for large-scale ESTs and genomic DNA clustering and assembling. Running on a Sun Fire 15K supercomputer, a significantly large volume of data can be processed in a short period of time. The results can be used to functionally annotate genes, to facilitate splice alignment analysis, to link the transcripts to genetic and physical maps, design microarray chips, to perform transcriptome analysis and to map to KEGG metabolic pathways. The service provides an excellent bioinformatics tool to research groups in wet-lab as well as an all-in-one-tool for sequence handling to bioinformatics researchers.

Computational Biology↗

PubMed2006

Bioinformatic challenges for the next decade(s).

The science of bioinformatics has developed in the wake of methods to determine the sequences of the informational macromolecules--DNAs, RNAs and proteins. But in a wider sense, the biological world depends in its every process on the transmission of information, and hence bioinformatics is the fundamental core of biology. We here give a consideration of some of the key problems of bioinformatics in the coming decade, and perhaps longer.

Computational Biology↗

PubMed2005

The study of G-protein coupled receptor oligomerization with computational modeling and bioinformatics.

To achieve a structural context for the analysis of G-protein coupled receptor (GPCR) oligomers, molecular modeling must be used to predict the corresponding interaction interfaces. The task is complicated by the paucity of detailed structural data at atomic resolution, and the large number of possible modes in which the bundles of seven transmembrane (TM) segments of the interacting GPCR monomers can be packed together into dimers and/or higher-order oligomers. Approaches and tools offered by bioinformatics can be used to reduce the complexity of this task and, combined with computational modeling, can serve to yield testable predictions for the structural properties of oligomers. Most of the bioinformatics methods take advantage of the evolutionary relation that exists among GPCRs, as expressed in their sequences and measurable in the common elements of their structural and functional features. These common elements are responsible for the presence of detectable patterns of motifs and correlated mutations evident from the alignment of the sequences of these complex biological systems. The decoding of these patterns in terms of structural and functional determinants can provide indications about the most likely interfaces of dimerization/oligomerization of GPCRs. We review here the main approaches from bioinformatics, enhanced by computational molecular modeling, that have been used to predict likely interfaces of dimerization/oligomerization of GPCRs, and compare results from their application to rhodopsin-like GPCRs. A compilation of the most frequently predicted GPCR oligomerization interfaces points to specific regions of TMs 4-6.

Computational Biology↗

PubMedDate not supplied

The interactions between clinical informatics and bioinformatics: a case study.

For the past decade, Stanford Medical Informatics has combined clinical informatics and bioinformatics research and training in an explicit way. The interest in applying informatics techniques to both clinical problems and problems in basic science can be traced to the Dendral project in the 1960s. Having bioinformatics and clinical informatics in the same academic unit is still somewhat unusual and can lead to clashes of clinical and basic science cultures. Nevertheless, the benefits of this organization have recently become clear, as the landscape of academic medicine in the next decades has begun to emerge. The author provides examples of technology transfer between clinical informatics and bioinformatics that illustrate how they complement each other.

Academic Medical Centers↗

PubMed2003

Evaluation of ontology merging tools in bioinformatics.

Ontologies are being used nowadays in many areas, including bioinformatics. One of the issues in ontology research is the aligning and merging of ontologies. Tools have been developed for ontology merging, but they have not been evaluated for their use in bioinformatics. In this paper we evaluate two of the most well-known ontology merging tools with a bioinformatics perspective. As test ontologies we have used Gene Ontology and Signal-Ontology.

Computational Biology↗

PubMed2003

Applying database technology to clinical and basic research bioinformatics projects.

This paper describes the application of database technology to medical information with the goal of providing medical and clinical researchers with the tools necessary to plan bioinformatics projects. Commercial database management systems were utilized, standard database design practices were applied, a user interface was created, data entered, and the development of analysis tools, including data mining technologies is underway. Databases were constructed based on animal and cell culture models of diabetes and clinical data. Bioinformatics is a useful tool in both basic research and clinical settings. The advantages of relational databases and an approach to managing bioinformatics projects are discussed.

Animals↗

PubMed2003

Bioinformatics meets proteomics--bridging the gap between mass spectrometry data analysis and cell biology.

Proteomics research programs typically comprise the identification of protein content of any given cell, their isoforms, splice variants, post-translational modifications, interacting partners and higher-order complexes under different conditions. These studies present significant analytical challenges owing to the high proteome complexity and the low abundance of the corresponding proteins, which often requires highly sensitive and resolving techniques. Mass spectrometry plays an important role in proteomics and has become an indispensable tool for molecular and cellular biology. However, the analysis of mass spectrometry data can be a daunting task in view of the complexity of the information to decipher, the accuracy and dynamic range of quantitative analysis, the availability of appropriate bioinformatics software and the overwhelming size of data files. The past ten years have witnessed significant technological advances in mass spectrometry-based proteomics and synergy with bioinformatics is vital to fulfill the expectations of biological discovery programs. We present here the technological capabilities of mass spectrometry and bioinformatics for mining the cellular proteome in the context of discovery programs aimed at trace-level protein identification and expression from microgram amounts of protein extracts from human tissues.

Computational Biology↗

PubMed2006

FastGroupII: a web-based bioinformatics platform for analyses of large 16S rDNA libraries.

BACKGROUND: High-throughput sequencing makes it possible to rapidly obtain thousands of 16S rDNA sequences from environmental samples. Bioinformatic tools for the analyses of large 16S rDNA sequence databases are needed to comprehensively describe and compare these datasets. RESULTS: FastGroupII is a web-based bioinformatics platform to dereplicate large 16S rDNA libraries. FastGroupII provides users with the option of four different dereplication methods, performs rarefaction analysis, and automatically calculates the Shannon-Wiener Index and Chao1. FastGroupII was tested on a set of 16S rDNA sequences from coral-associated Bacteria. The different grouping algorithms produced similar, but not identical, results. This suggests that 16S rDNA datasets need to be analyzed in multiple ways when being used for community ecology studies. CONCLUSION: FastGroupII is an effective bioinformatics tool for the trimming and dereplication of 16S rDNA sequences. Several standard diversity indices are calculated, and the raw sequences are prepared for downstream analyses.

Algorithms↗

PubMed2006

HotSwap for bioinformatics: a STRAP tutorial.

BACKGROUND: Bioinformatics applications are now routinely used to analyze large amounts of data. Application development often requires many cycles of optimization, compiling, and testing. Repeatedly loading large datasets can significantly slow down the development process. We have incorporated HotSwap functionality into the protein workbench STRAP, allowing developers to create plugins using the Java HotSwap technique. RESULTS: Users can load multiple protein sequences or structures into the main STRAP user interface, and simultaneously develop plugins using an editor of their choice such as Emacs. Saving changes to the Java file causes STRAP to recompile the plugin and automatically update its user interface without requiring recompilation of STRAP or reloading of protein data. This article presents a tutorial on how to develop HotSwap plugins. STRAP is available at http://strapjava.de and http://www.charite.de/bioinf/strap. CONCLUSION: HotSwap is a useful and time-saving technique for bioinformatics developers. HotSwap can be used to efficiently develop bioinformatics applications that require loading large amounts of data into memory.

Algorithms↗

PubMed2006

Predicting candidate genes for human deafness disorders: a bioinformatics approach.

BACKGROUND: There are more than 50 genes for autosomal dominant and autosomal recessive nonsyndromic hereditary deafness that are yet to be cloned. The human genome sequence and expression profiles of transcripts in the inner ear have aided positional cloning approaches. The knowledge of protein interactions offers additional advantages in selecting candidate genes within a mapped region. RESULTS: We have employed a bioinformatic approach to assemble the genes encoded by genomic regions that harbor various deafness loci. The genes were then in silico analyzed for their candidacy by expression pattern and ability to interact with other proteins. Such analyses have narrowed a list of 2400 genes from suspected regions of the genome to a manageable number of about 140 for further analysis. CONCLUSION: We have established a list of strong candidate genes encoded by the regions linked to various nonsyndromic hereditary hearing loss phenotypes by using a novel bioinformatic approach. The candidates presented here provide a starting point for mutational analysis in well-characterized families along with genetic linkage to refine the loci. The advantages and shortcomings of this bioinformatic approach are discussed.

Chromosome Mapping↗

PubMed2006

Incorporating a new bioinformatics component into genetics at a historically black college: outcomes and lessons.

Many students at minority-serving institutions are underexposed to Internet resources such as the human genome project, PubMed, NCBI databases, and other Web-based technologies because of a lack of financial resources. To change this, we designed and implemented a new bioinformatics component to supplement the undergraduate Genetics course at Clark Atlanta University. The outcomes of the Bioinformatics course were assessed. During the first week of the semester, students were assigned the Felder-Soloman's Index of Learning Styles Inventory. The overwhelming majority of students were visual (82.1%) and sequential (75.0%) learners. Furthermore, pre- and postcourse surveys were administered during the first and the last week of the course to assess learning, confidence level, and mental activity. These indicated students increased the number of hours spent using computers and doing homework. Students reported confidence in using computers to study genetics increased, enabling them to better visualize and understand genetics. Furthermore, students were more mentally engaged in a more social learning environment. Although the students appreciated the value of the bioinformatics component, they reported the additional work load was substantial enough to receive additional course credit.

Adolescent↗

PubMed2004

Overview of commonly used bioinformatics methods and their applications.

Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These include a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and even hyphenated techniques as neuro-fuzzy networks. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities.

Computational Biology↗

PubMed2004

Building a bioinformatics community of practice through library education programs.

This paper addresses the following questions:What makes the community of practice concept an intriguing framework for developing library services for bioinformatics? What is the campus context and setting? What has been the Health Sciences Library's role in bioinformatics at the University of North Carolina (UNC) Chapel Hill? What are the Health Sciences Library's goals? What services are currently offered? How will these services be evaluated and developed? How can libraries demonstrate their value? Providing library services for an emerging community such as bioinformatics and computational biology presents special challenges for libraries including understanding needs, defining and communicating the library's role, building relationships within the community, preparing staff, and securing funding. Like many academic health sciences libraries, the University of North Carolina (UNC) at Chapel Hill Health Sciences Library is addressing these challenges in the context of its overall mission and goals.

Academic Medical Centers↗

PubMed2004

Bioinformatics in mass spectrometry data analysis for proteomics studies.

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.

Computational Biology↗

PubMed2006

Bioinformatics and data mining in proteomics.

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

Computational Biology↗

PubMed2005

Experimental and bioinformatic approaches for interrogating protein-protein interactions to determine protein function.

An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein-protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein-protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein-protein interactions.

Animals↗

PubMed2005

Using bioinformatics for drug target identification from the genome.

Genomics and proteomics technologies have created a paradigm shift in the drug discovery process, with bioinformatics having a key role in the exploitation of genomic, transcriptomic, and proteomic data to gain insights into the molecular mechanisms that underlie disease and to identify potential drug targets. We discuss the current state of the art for some of the bioinformatic approaches to identifying drug targets, including identifying new members of successful target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks. In addition, we introduce drug target discovery using the strategy of systems biology, and discuss some of the data resources for the identification of drug targets. Although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires an understanding of the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work.

Alzheimer Disease↗

PubMed2006

MetaBasis: a web-based database containing metadata on software tools and databases in the field of bioinformatics.

UNLABELLED: We have developed an integrated web-based relational database information system, which offers an extensive search functionality of validated entries containing available bioinformatics computing resources. This system, called MetaBasis, aims to provide the bioinformatics community, and especially newcomers to the field, with easy access to reliable bioinformatics databases and tools. MetaBasis is focused on non-commercial and open-source software tools. AVAILABILITY: http://metabasis.bioacademy.gr/

Computational Biology↗