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PubMed2003

The discovery net system for high throughput bioinformatics.

MOTIVATION: Bioinformatics requires Grid technologies and protocols to build high performance applications without focusing on the low level detail of how the individual Grid components operate. RESULTS: The Discovery Net system is a middleware that allows service developers to integrate tools based on existing and emerging Grid standards such as web services. Once integrated, these tools can be used to compose reusable workflows using these services that can later be deployed as new services for others to use. Using the Discovery Net system and a range of different bioinformatics tools, we built a Grid based application for Genome Annotation. This includes workflows for automatic nucleotide annotation, annotation of predicted proteins and text analysis based on metabolic profiles and text analysis.

Algorithms↗
PubMed2004

YETI: Yeast Exploration Tool Integrator.

UNLABELLED: Yeast Exploration Tool Integrator (YETI) is a novel bioinformatics tool for the integrated visualization and analysis of functional genomic data sets from the budding yeast Saccharomyces cerevisiae. AVAILABILITY: YETI is freely available for use over the WWW, or download under license, at http://www.bru.ed.ac.uk/~orton/yeti.html

Computer Graphics↗
PubMed2005

Comparative plant genomics resources at PlantGDB.

PlantGDB (http://www.plantgdb.org/) is a database of plant molecular sequences. Expressed sequence tag (EST) sequences are assembled into contigs that represent tentative unique genes. EST contigs are functionally annotated with information derived from known protein sequences that are highly similar to the putative translation products. Tentative Gene Ontology terms are assigned to match those of the similar sequences identified. Genome survey sequences are assembled similarly. The resulting genome survey sequence contigs are matched to ESTs and conserved protein homologs to identify putative full-length open reading frame-containing genes, which are subsequently provisionally classified according to established gene family designations. For Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), the exon-intron boundaries for gene structures are annotated by spliced alignment of ESTs and full-length cDNAs to their respective complete genome sequences. Unique genome browsers have been developed to present all available EST and cDNA evidence for current transcript models (for Arabidopsis, see the AtGDB site at http://www.plantgdb.org/AtGDB/; for rice, see the OsGDB site at http://www.plantgdb.org/OsGDB/). In addition, a number of bioinformatic tools have been integrated at PlantGDB that enable researchers to carry out sequence analyses on-site using both their own data and data residing within the database.

Computational Biology↗
PubMed2003

BIBI, a bioinformatics bacterial identification tool.

BIBI was designed to automate DNA sequence analysis for bacterial identification in the clinical field. BIBI relies on the use of BLAST and CLUSTAL W programs applied to different subsets of sequences extracted from GenBank. These sequences are filtered and stored in a new database, which is adapted to bacterial identification.

Bacteria↗
PubMed2001

A computer system to perform structure comparison using TOPS representations of protein structure.

We describe the design and implementation of a fast topology-based method for protein structure comparison. The approach uses the TOPS topological representation of protein structure, aligning two structures using a common discovered pattern and generating measure of distance derived from an insert score. Heavy use is made of a constraint-based pattern-matching algorithm for TOPS diagrams that we have designed and described elsewhere (Bioinformatics 15(4) (1999) 317). The comparison system is maintained at the European Bioinformatics Institute and is available over the Web at tops.ebi.ac.uk/tops. Users submit a structure description in Protein Data Bank (PDB) format and can compare it with structures in the entire PDB or a representative subset of protein domains, receiving the results by email.

Algorithms↗
PubMed2006

bioNMF: a versatile tool for non-negative matrix factorization in biology.

BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at http://www.dacya.ucm.es/apascual/bioNMF.

Algorithms↗
PubMed2001

Cambridge Healthtech Institute's Third Annual Conference on Lab-on-a-Chip and Microarrays. 22-24 January 2001, Zurich, Switzerland.

Cambridge Healthtech Institute's Third Annual Conference on Lab-on-a-Chip and Microarray technology covered the latest advances in this technology and applications in life sciences. Highlights of the meetings are reported briefly with emphasis on applications in genomics, drug discovery and molecular diagnostics. There was an emphasis on microfluidics because of the wide applications in laboratory and drug discovery. The lab-on-a-chip provides the facilities of a complete laboratory in a hand-held miniature device. Several microarray systems have been used for hybridisation and detection techniques. Oligonucleotide scanning arrays provide a versatile tool for the analysis of nucleic acid interactions and provide a platform for improving the array-based methods for investigation of antisense therapeutics. A method for analysing combinatorial DNA arrays using oligonucleotide-modified gold nanoparticle probes and a conventional scanner has considerable potential in molecular diagnostics. Various applications of microarray technology for high-throughput screening in drug discovery and single nucleotide polymorphisms (SNP) analysis were discussed. Protein chips have important applications in proteomics. With the considerable amount of data generated by the different technologies using microarrays, it is obvious that the reading of the information and its interpretation and management through the use of bioinformatics is essential. Various techniques for data analysis were presented. Biochip and microarray technology has an essential role to play in the evolving trends in healthcare, which integrate diagnosis with prevention/treatment and emphasise personalised medicines.

Computational Biology↗
PubMed2006

Current bioinformatics tools in genomic biomedical research (Review).

On the advent of a completely assembled human genome, modern biology and molecular medicine stepped into an era of increasingly rich sequence database information and high-throughput genomic analysis. However, as sequence entries in the major genomic databases currently rise exponentially, the gap between available, deposited sequence data and analysis by means of conventional molecular biology is rapidly widening, making new approaches of high-throughput genomic analysis necessary. At present, the only effective way to keep abreast of the dramatic increase in sequence and related information is to apply biocomputational approaches. Thus, over recent years, the field of bioinformatics has rapidly developed into an essential aid for genomic data analysis and powerful bioinformatics tools have been developed, many of them publicly available through the World Wide Web. In this review, we summarize and describe the basic bioinformatics tools for genomic research such as: genomic databases, genome browsers, tools for sequence alignment, single nucleotide polymorphism (SNP) databases, tools for ab initio gene prediction, expression databases, and algorithms for promoter prediction.

Computational Biology↗
PubMed2005

Combined proteomics and pathways analysis of collecting duct reveals a protein regulatory network activated in vasopressin escape.

Low sensitivity is characteristic of many proteomics methods. Presented here is an approach that combines proteomics based on difference gel electrophoresis (DIGE) with bioinformatic pathways analysis to identify both abundant and relatively nonabundant proteins in inner medullary collecting duct (IMCD) altered in abundance during escape from vasopressin-induced antidiuresis. Rats received the vasopressin analog dDAVP by osmotic minipump plus either a daily water load (vasopressin escape) or only enough water to replace losses (control). Immunoblotting confirmed the hallmark of vasopressin escape, a decrease in aquaporin-2, and demonstrated a decrease in the abundance of the urea transporter UT-A3. DIGE identified 22 mostly high-abundance proteins regulated during vasopressin escape. These proteins were analyzed using pathways analysis software to reveal protein clusters incorporating the proteins identified by DIGE. A single dominant cluster emerged that included many relatively low-abundance proteins (abundances too low for DIGE identification), including several transcription factors. Immunoblotting confirmed a decrease in total and phosphorylated c-myc, a decrease in c-fos, and increases in c-jun and p53. Furthermore, immunoblotting confirmed hypothesized changes in other proteins in the proposed network: Increases in c-src, receptor for activated C kinase 1, calreticulin, and caspase 3 and decreases in steroid receptor co-activator 1, Grp78/BiP, and annexin A4. This combined approach proved capable of uncovering regulatory proteins that are altered in response to a specific physiologic perturbation without being detected directly by DIGE. The results demonstrate a dominant protein regulatory network in IMCD cells that is altered in association with vasopressin escape, providing a new framework for further studies of signaling in IMCD.

Animals↗
PubMed2006

Bioconductor: an open source framework for bioinformatics and computational biology.

This chapter describes the Bioconductor project and details of its open source facilities for analysis of microarray and other high-throughput biological experiments. Particular attention is paid to concepts of container and workflow design, connections of biological metadata to statistical analysis products, support for statistical quality assessment, and calibration of inference uncertainty measures when tens of thousands of simultaneous statistical tests are performed.

Animals↗
PubMed2006

Development of bioinformatic tools to support EST-sequencing, in silico- and microarray-based transcriptome profiling in mycorrhizal symbioses.

The great majority of terrestrial plants enters a beneficial arbuscular mycorrhiza (AM) or ectomycorrhiza (ECM) symbiosis with soil fungi. In the SPP 1084 "MolMyk: Molecular Basics of Mycorrhizal Symbioses", high-throughput EST-sequencing was performed to obtain snapshots of the plant and fungal transcriptome in mycorrhizal roots and in extraradical hyphae. To focus activities, the interactions between Medicago truncatula and Glomus intraradices as well as Populus tremula and Amanita muscaria were selected as models for AM and ECM symbioses, respectively. Together, almost, 20.000 expressed sequence tags (ESTs) were generated from different random and suppressive subtractive hybridization (SSH) cDNA libraries, providing a comprehensive overview of the mycorrhizal transcriptome. To automatically cluster and annotate EST-sequences, the BioMake and SAMS software tools were developed. In connection with the eNorthern software SteN, plant genes with a predicted mycorrhiza-induced expression were identified. To support experimental transcriptome profiling, macro- and microarray tools have been constructed for the two model mycorrhizae, based either on PCR-amplified cDNAs or 70mer oligonucleotides. These arrays were used to profile the transcriptome of AM and ECM roots under different conditions, and the data obtained were uploaded to the ArrayLIMS and EMMA databases that are designed to store and evaluate expression profiles from DNA arrays. Together, the EST- and transcriptome databases can be mined to identify candidate genes for targeted functional studies.

Computational Biology↗
PubMed2004

Tutorial section: domains and motifs - proteins in bite-sized chunks.

Possibly the ultimate goal of bioinformatics is to be able to predict protein tertiary structure and chemical functionality from the initial amino acid sequence. Despite the best efforts of many researchers over the past two decades, a reliable modelling method has yet to be found and the folding problem continues to be a hurdle for scientists.

Amino Acid Motifs↗
PubMed2005

BALLView: a tool for research and education in molecular modeling.

We present BALLView, a molecular viewer and modeling tool. It combines state-of-the-art visualization capabilities with powerful modeling functionality including implementations of force field methods and continuum electrostatics models. BALLView is a versatile and extensible tool for research in structural bioinformatics and molecular modeling. Furthermore, the convenient and intuitive graphical user interface offers novice users direct access to the full functionality, rendering it ideal for teaching. Through an interface to the object-oriented scripting language Python it is easily extensible.

Computer Graphics↗
PubMed2004

Deciphering regulatory patterns of inflammatory gene expression from interleukin-1-stimulated human endothelial cells.

OBJECTIVE: Endothelial cells comprise a key component of the inflammatory response. We set out to obtain a comprehensive overview of the immediate-early to early gene expression program of interleukin-1 (IL-1)-stimulated endothelial cells and to identify novel transcription factors and regulatory elements. METHODS AND RESULTS: Human umbilical vein endothelial cells (HUVECs) were stimulated with IL-1 for 0, 0.5, 1, 2.5, and 6 hours and analyzed using Affymetrix U133 microarrays. A total of 137 genes were found to be regulated >4-fold, including 18 transcription factors. The expression of selected genes was confirmed by real-time polymerase chain reaction. Cluster analysis was performed in order to group genes according to their expression profiles. To identify novel transcription factor-binding sites, the corresponding promoters were extracted from databases and analyzed for regulatory elements that were over-represented in specific clusters. Several potentially novel DNA binding sites were identified, and one was shown to specifically bind an IL-1-inducible protein from HUVEC. CONCLUSIONS: These results demonstrate that in the early phase after stimulation, IL-1 evokes a complex gene expression program that includes positive but also negative (feedback) regulators of diverse endothelial cell functions. Furthermore, the identification of a new promoter regulatory element demonstrates the feasibility of the bioinformatics-driven approach to discover novel regulatory mechanisms.

Binding Sites↗
PubMed2003

Development under extreme conditions: forensic bioinformatics in the wake of the World Trade Center disaster.

The terrorist attacks of September 11, 2001 resulted in death and devastation in three locations, and extraordinary efforts have been exerted to identify the remains of all victims. As mass fatalities go, this one has been unusual at a policy level because the goal has been not merely to identify remains for every decedent, but to identify every bit of remains found so that even small pieces of tissue can be returned to families for burial. While the human impact at the Pentagon and Shanksville, PA was horrific, the World Trade Center site presented a particularly complex challenge for forensic DNA matching and data handling. A complete and definitive list of all those killed is still elusive, and human remains were crushed and co-mingled by the falling towers. Software tools had never been considered for a problem of this scale and scope. New data handling systems had to be created under extreme software development conditions characterized by incomplete requirements specifications, chaotically changing priorities, truly impossible deadlines and rapidly rolling production releases. Partly because of the company's experience with mtDNA tools built for the Armed Forces DNA Identification Lab starting in 1997, the New York City Office of Chief Medical Examiner [OCME] contacted Gene Codes Corporation in late September as existing data-handling tools began to fail. We began work on the project in mid-October, 2001. Our approach to the problem included: Extreme Programming [XP] methodology for functional software development, On-site time and motion analysis at the OCME for user interface design, Evidentiary references between STR, SNP and mtDNA analysis results, and Separate data Quality Control [QC] and software Quality Assurance [QA] initiatives. A substantial software suite was developed called M-FISys, an acronym for Mass-Fatality Identification System.

Computational Biology↗
PubMed2026

PyEvoMotion: a Python tool for population-based time-course analysis of genome evolution.

SUMMARY: We present PyEvoMotion, an open-source Python tool for inferring molecular clock models with time-dependent Gaussian noise from high-throughput genomic datasets. PyEvoMotion features a command-line interface and a modular architecture, allowing seamless integration into larger bioinformatic pipelines. The tool supports customizable filtering, temporal discretization definition, and mutation classification, making it adaptable to diverse research needs. While traditional phylogenetic methods may encounter computational challenges with large datasets, PyEvoMotion can process thousands to millions of sequences to compute statistical parameters associated with a stochastic differential equation model, thereby weighting the genetic variation within the population. Using viral genomic data, we demonstrate its capability to infer evolutionary rates and detect non-Brownian evolutionary motions with subdiffusive behavior. PyEvoMotion shows potential to provide overlooked insights into genome evolution in different contexts. AVAILABILITY AND IMPLEMENTATION: The open source software is available on GitHub at https://github.com/luksgrin/PyEvoMotion and on SourceForge at https://sourceforge.net/projects/pyevomotion.

Software↗
PubMed2004

FLOSYS--a web-accessible workflow system for protocol-driven biomolecular sequence analysis.

FLOSYS is an interactive web-accessible bioinformatics workflow system designed to assist biologists in multi-step data analyses. FLOSYS allows the user to create complex analysis pathways (protocols) graphically, similar to drawing a flowchart: icons representing particular bioinformatics tools are dragged and dropped onto a canvas and lines connecting those icons are drawn to specify the relationships between the tools. In addition, FLOSYS permits to select input-data, execute the protocol and store the results in a personal workspace. The three-tier architecture of FLOSYS has been implemented in Java and uses a relational database system together with new technologies for distributed and web computing such as CORBA, RMI, JSP and JDBC. The prototype of FLOSYS, which is part of the bioinformatics workbench AnaBench, is accessible on-line at http://malawimonas.bcm.umontreal.ca: 8091/anabench. The entire package is available on request to academic groups who wish to have a customized local analysis environment for research or teaching.

Computational Biology↗
PubMed2006

The pitfalls of proteomics experiments without the correct use of bioinformatics tools.

The elucidation of the entire genomic sequence of various organisms, from viruses to complex metazoans, most recently man, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. Over the past two decades, the focus of molecular biology has gradually moved from genomes to proteomes, the intention being to discover the functions of the genes themselves. The postgenomic era stimulated the development of new techniques (e.g. 2-DE and MS) and bioinformatics tools to identify the functions, reactions, interactions and location of the gene products in tissues and/or cells of living organisms. Both 2-DE and MS have been very successfully employed to identify proteins involved in biological phenomena (e.g. immunity, cancer, host-parasite interactions, etc.), although recently, several papers have emphasised the pitfalls of 2-DE experiments, especially in relation to experimental design, poor statistical treatment and the high rate of 'false positive' results with regard to protein identification. In the light of these perceived problems, we review the advantages and misuses of bioinformatics tools - from realisation of 2-DE gels to the identification of candidate protein spots - and suggest some useful avenues to improve the quality of 2-DE experiments. In addition, we present key steps which, in our view, need to be to taken into consideration during such analyses. Lastly, we present novel biological entities named 'interactomes', and the bioinformatics tools developed to analyse the large protein-protein interaction networks they form, along with several new perspectives of the field.

Amino Acid Sequence↗