MAG's demise signals trouble for bioinformatics firms.
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Computational Biology↗
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[Computing environments required for bioinformatics].
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Computational Biology↗
Bioinformatics and cancer: an essential alliance.
Modern research in cancer has been revolutionized by the introduction of new high-throughput methodologies such as DNA microarrays. Keeping the pace with these technologies, the bioinformatics offer new solutions for data analysis and, what is more important, it permits to formulate a new class of hypothesis inspired in systems biology, more oriented to blocks of functionally-related genes. Although software implementations for this new methodologies is new there are some options already available. Bioinformatic solutions for other high-throughput techniques such as array-CGH of large-scale genotyping is also revised.
Chromosome Aberrations↗
ProGenGrid: a grid-enabled platform for bioinformatics.
In this paper we describe the ProGenGrid (Proteomics and Genomics Grid) system, developed at the CACT/ISUFI of the University of Lecce which aims at providing a virtual laboratory where e-scientists can simulate biological experiments, composing existing analysis and visualization tools, monitoring their execution, storing the intermediate and final output and finally, if needed, saving the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web Services and composed through a workflow. Since bioinformatics applications need to use high performance machines or a high number of workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide-spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for drug design, providing them as services, through easy to use interfaces such as the Web and Web service interfaces built using the open source gSOAP Toolkit, whereas as Grid middleware we are using the Globus Toolkit 3.2, exploiting some protocols such as GSI and GridFTP.
Computational Biology↗
JADE: an approach for interconnecting bioinformatics databases.
To achieve the integration of biological data available on the World Wide Web and maintained in diverse sources such as GDB, Genbank or Acedb, we have developed a software called Jade. Jade allows programmers to create analytic tools and graphical user interfaces for one or more existing bioinformatics data sources. These tools can then be interchanged, compared and reused without making modifications in the data sources themselves. The system is implemented in the Java programming language and will run equally well on Macintosh, Windows or Unix workstations. Jade is free and can be used immediately by all interested parties.
Computational Biology↗
TreeScan: a bioinformatic application to search for genotype/phenotype associations using haplotype trees.
SUMMARY: We present the software implementation of the tree scanning method to detect associations between genetic haplotypes and quantitative traits, utilizing the evolutionary history of the haplotypes, in samples of unrelated individuals. AVAILABILITY: The program is available free of charge, under the GNU General Public License. A package including C source code, a Makefile, and Windows (DOS) and Macintosh binaries, can be downloaded from http://darwin.uvigo.es
Chromosome Mapping↗
EdiPy: a resource to simulate the evolution of plant mitochondrial genes under the RNA editing.
EdiPy is an online resource appropriately designed to simulate the evolution of plant mitochondrial genes in a biologically realistic fashion. EdiPy takes into account the presence of sites subjected to RNA editing and provides multiple artificial alignments corresponding to both genomic and cDNA sequences. Each artificial data set can successively be submitted to main and widespread evolutionary and phylogenetic software packages such as PAUP, Phyml, PAML and Phylip. As an online bioinformatic resource, EdiPy is available at the following web page: http://biologia.unical.it/py_script/index.html.
Computational Biology↗
JADE: An approach for interconnecting bioinformatics databases
To achieve the integration of biological data available on the World Wide Web and maintained in diverse sources such as GDB, Genbank or Acedb, we have developed a software called Jade. Jade allows programmers to create analytic tools and graphical user interfaces for one or more existing bioinformatics data sources. These tools can then be interchanged, compared and reused without making modifications in the data sources themselves. The system is implemented in the Java programming language and will run equally well on Macintosh, Windows or Unix workstations. Jade is free and can be used immediately by all interested parties.
Journal Article↗
Influence of nicotine on protein expression around hydrophilic osseointegrated implants: A proteomic study in male rats.
OBJECTIVE: To ensure the success of dental implant treatment, various factors must be considered, including osseointegration and systemic conditions. There is evidence in the literature that smokers may exhibit alterations in tissue healing, which can compromise the success of implant rehabilitation. Therefore, this study aimed to investigate the influence of nicotine on the protein profile of bone tissue around hydrophilic implants during the osseointegration process in rats. DESIGN: Bone tissue samples from the control and nicotine groups (n = 3 per group) were subjected to protein extraction, mass spectrometry, and bioinformatic analyses. Protein identification was performed using Proteome Discoverer 2.1 software and the SEQUEST algorithm, and the protein data were compared with those of a protein database of Rattus norvegicus obtained from UniProt. RESULTS: A total of 740 proteins were detected in both the control group and the nicotine-exposed group. Among them, the proteins biglycan, periostin and histone H4 were highlighted because of their higher abundance in the healthy implant group, while they were reduced in the nicotine-exposed group. CONCLUSIONS: Nicotine has the potential to alter the protein profile of bone tissue around hydrophilic implants during osseointegration, which may impair tissue remodeling and healing.
Animals↗
Gene prioritization through genomic data fusion.
The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.
Algorithms↗
Predicting subcellular localization of proteins in a hybridization space.
MOTIVATION: The localization of a protein in a cell is closely correlated with its biological function. With the number of sequences entering into databanks rapidly increasing, the importance of developing a powerful high-throughput tool to determine protein subcellular location has become self-evident. In view of this, the Nearest Neighbour Algorithm was developed for predicting the protein subcellular location using the strategy of hybridizing the information derived from the recent development in gene ontology with that from the functional domain composition as well as the pseudo amino acid composition. RESULTS: As a showcase, the same plant and non-plant protein datasets as investigated by the previous investigators were used for demonstration. The overall success rate of the jackknife test for the plant protein dataset was 86%, and that for the non-plant protein dataset 91.2%. These are the highest success rates achieved so far for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach (particularly by incorporating the knowledge of gene ontology) may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. AVAILABILITY: The software would be made available on sending a request to the authors.
Algorithms↗
AGenDA: gene prediction by cross-species sequence comparison.
Automatic gene prediction is one of the major challenges in computational sequence analysis. Traditional approaches to gene finding rely on statistical models derived from previously known genes. By contrast, a new class of comparative methods relies on comparing genomic sequences from evolutionary related organisms to each other. These methods are based on the concept of phylogenetic footprinting: they exploit the fact that functionally important regions in genomic sequences are usually more conserved than non-functional regions. We created a WWW-based software program for homology-based gene prediction at BiBiServ (Bielefeld Bioinformatics Server). Our tool takes pairs of evolutionary related genomic sequences as input data, e.g. from human and mouse. The server runs CHAOS and DIALIGN to create an alignment of the input sequences and subsequently searches for conserved splicing signals and start/stop codons near regions of local sequence conservation. Genes are predicted based on local homology information and splice signals. The server returns predicted genes together with a graphical representation of the underlying alignment. The program is available at http://bibiserv.TechFak.Uni-Bielefeld.DE/agenda/.
Animals↗
The Web as an educational tool for/in learning/teaching bioinformatics statistics.
Statistics provides essential tool in Bioinformatics to interpret the results of a database search or for the management of enormous amounts of information provided from genomics, proteomics and metabolomics. The goal of this project was the development of a software tool that would be as simple as possible to demonstrate the use of the Bioinformatics statistics. Computer Simulation Methods (CSMs) developed using Microsoft Excel were chosen for their broad range of applications, immediate and easy formula calculation, immediate testing and easy graphics representation, and of general use and acceptance by the scientific community. The result of these endeavours is a set of utilities which can be accessed from the following URL: http://gmein.uib.es/bioinformatica/statistics. When tested on students with previous coursework with traditional statistical teaching methods, the general opinion/overall consensus was that Web-based instruction had numerous advantages, but traditional methods with manual calculations were also needed for their theory and practice. Once having mastered the basic statistical formulas, Excel spreadsheets and graphics were shown to be very useful for trying many parameters in a rapid fashion without having to perform tedious calculations. CSMs will be of great importance for the formation of the students and professionals in the field of bioinformatics, and for upcoming applications of self-learning and continuous formation.
Computational Biology↗
Prediction and Evaluation of Protein Aggregation with Computational Methods.
Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have been developed to predict aggregation of proteins and peptides to better understand fundamental mechanisms of various aggregation diseases. Here, we attempt to provide a brief overview of bioinformatic methods and tools to better understand molecular mechanisms of aggregation disorders. Furthermore, through a better understanding of protein aggregation mechanisms, it might be possible to design novel therapeutic agents to treat and hopefully prevent protein aggregation diseases.
Computational Biology↗
Computing chemistry on the web.
The development of on-line software tools is changing the way we traditionally perform our analysis in drug design, but will chemoinformatics be forever behind bioinformatics in this development?
Computer Simulation↗
REPuter: fast computation of maximal repeats in complete genomes.
SUMMARY: A software tool was implemented that computes exact repeats and palindromes in entire genomes very efficiently. AVAILABILITY: Via the Bielefeld Bioinformatics Server (http://bibiserv.techfak.uni-bielefeld.de/rep uter/).
DNA↗
Signal transduction-related bioinformatics services.
Signal transduction pathways are crucial for the regulation of a very wide variety of cellular functions ranging, for example, from translation to intercellular communication, and from metabolism to apoptosis. Protein kinases and phosphatases, together with their binding partners, are key players in these cascades. They also form a substantial part of the genes in genomes and proteins in proteomes in all animals. Signalling can be studied in many different levels and ways. This has resulted in large body of publications and Internet services. This paper describes open-access databases and software aiming at presenting the kind of data available and how to perform bioinformatics analyses.
Computational Biology↗