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Archaeal-type rhodopsins in Chlamydomonas: model structure and intracellular localization.

Phototaxis in the unicellular green alga Chlamydomonas reinhardtii is mediated by rhodopsin-type photoreceptor(s). Recent expressed sequence tag database from the Kazusa DNA Research Institute has provided the basis for unequivocal identification of two archaeal-type rhodopsins in it. Here we demonstrate that one is located near the eyespot, wherein the photoreceptor(s) has long been thought to be enriched, along with the results of bioinformatic analyses. Secondary structure prediction showed that the second putative transmembrane helices (helix B) of these rhodopsins are rich in glutamate residues, and homology modeling suggested that some additional intra- or intermolecular interactions are necessary for opsin-like folding of the N-terminal ca. 300-aa membrane spanning domains of 712 and 737-aa polypeptides. These results complement physiological and electrophysiological experiments combined with the manipulation of their expression [O.A. Sineshchekov, K.H. Jung, J.H. Spudich, Proc. Natl. Sci. USA 99 (2002) 8689; G. Nagel, D. Olig, M. Fuhrmann, S. Kateriya, A.M. Musti, E. Bamberg, P. Hegemann, Science 296 (2002) 2395].

Algal Proteins↗

Making sense of score statistics for sequence alignments.

The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores between a query and a database. The effects of the similarity matrix and gap penalty values on the score distribution are then examined, and it is shown that the alignment statistics can undergo an abrupt phase transition. A few types of random sequence databases used in the estimation of statistical significance are presented, and the statistics employed by the BLAST, FASTA and PRSS programs are compared. Finally the different strategies used to assess the statistical significance of the matches produced by profiles and hidden Markov models are presented.

Amino Acid Sequence↗

Prediction and visualization of structural switches in RNA.

There are various cases where the biological function of an RNA molecule involves a reversible change of conformation. paRNAss is a software approach to the prediction of such structural switching in RNA. It is based on three hypotheses about the secondary structure space of a switching RNA molecule, which can be evaluated by RNA folding and structure comparison. In the positive case, the predicted structures must be verified experimentally. Additionally, we give an animated visualization of an energetically favourable transition between the predicted structures. paRNAss is available via the Bielefeld Bioinformatics Server. This paper explains the underlying model and shows that the approach performs well in a variety of applications.

Base Sequence↗

An amino acid substitution matrix for protein conformation identification.

Amino acid substitution matrices play an essential role in protein sequence alignment, a fundamental task in bioinformatics. Most widely used matrices, such as PAM matrices derived from homologous sequences and BLOSUM matrices derived from aligned segments of PROSITE, did not integrate conformation information in their construction. There are a few structure-based matrices, which are derived from limited data of structure alignment. Using databases PDB_SELECT and DSSP, we create a database of sequence-conformation blocks which explicitly represent sequence-structure relationship. Members in a block are identical in conformation and are highly similar in sequence. From this block database, we derive a conformation-specific amino acid substitution matrix CBSM60. The matrix shows an improved performance in conformational segment search and homolog detection.

Amino Acid Sequence↗

Structural characterization of pollen allergens.

The molecular characterization of allergens has accelerated significantly since the widespread implementation of modern analytical methods. The combination of gene cloning and heterologous protein expression has generated an extensive array of allergens that is available for comparative analysis, as well as clinical applications. Several internet-accessible allergen databases integrate the accumulated information from biomedical research and clinical practice. Innovations in classical biophysical methods, such as mass spectrometry, X-ray crystallography, and nuclear magnetic resonance spectroscopy, have rendered complex biological macromolecules amenable to detailed structural analysis. The modern scientific era has realized the synthesis of bioinformatics, molecular biology, biochemistry, biophysics, and immunology, and given us the means needed to decipher the remaining mysteries of allergies. This article addresses how the synergism of modern scientific techniques has hastened our understanding of allergies, how these techniques are applied in the identification and characterization of allergens, and how these methods assist the rational development of clinical tools for allergy diagnosis and treatment.

Allergens↗

Medical libraries, bioinformatics, and networked information: a coming convergence?

Libraries will be changed by technological and social developments that are fueled by information technology, bioinformatics, and networked information. Libraries in highly focused settings such as the health sciences are at a pivotal point in their development as the synthesis of historically diverse and independent information sources transforms health care institutions. Boundaries are breaking down between published literature and research data, between research databases and clinical patient data, and between consumer health information and professional literature. This paper focuses on the dynamics that are occurring with networked information sources and the roles that libraries will need to play in the world of medical informatics in the early twenty-first century.

Databases, Factual↗

Development of a prostate cDNA microarray and statistical gene expression analysis package.

A cDNA microarray comprising 5184 different cDNAs spotted onto nylon membrane filters was developed for prostate gene expression studies. The clones used for arraying were identified by cluster analysis of > 35 000 prostate cDNA library-derived expressed sequence tags (ESTs) present in the dbEST database maintained by the National Center for Biotechnology Information. Total RNA from two cell lines, prostate line 8.4 and melanoma line UACC903, was used to make radiolabeled probe for filter hybridizations. The absolute intensity of each individual cDNA spot was determined by phosphorimager scanning and evaluated by a bioinformatics package developed specifically for analysis of cDNA microarray experimentation. Results indicated 89% of the genes showed intensity levels above background in prostate cells compared with only 28% in melanoma cells. Replicate probe preparations yielded results with correlation values ranging from r = 0.90 to 0.93 and coefficient of variation ranging from 16 to 28%. Findings indicate that among others, the keratin 5 and vimentin genes were differentially expressed between these two divergent cell lines. Follow-up northern blot analysis verified these two expression changes, thereby demonstrating the reliability of this system. We report the development of a cDNA microarray system that is sensitive and reliable, demonstrates a low degree of variability, and is capable of determining verifiable gene expression differences between two distinct human cell lines. This system will prove useful for differential gene expression analysis in prostate-derived cells and tissue.

DNA, Complementary↗

Strategies for the physiome project.

The physiome is the quantitative description of the functioning organism in normal and pathophysiological states. The human physiome can be regarded as the virtual human. It is built upon the morphome, the quantitative description of anatomical structure, chemical and biochemical composition, and material properties of an intact organism, including its genome, proteome, cell, tissue, and organ structures up to those of the whole intact being. The Physiome Project is a multicentric integrated program to design, develop, implement, test and document, archive and disseminate quantitative information, and integrative models of the functional behavior of molecules, organelles, cells, tissues, organs, and intact organisms from bacteria to man. A fundamental and major feature of the project is the databasing of experimental observations for retrieval and evaluation. Technologies allowing many groups to work together are being rapidly developed. Internet II will facilitate this immensely. When problems are huge and complex, a particular working group can be expert in only a small part of the overall project. The strategies to be worked out must therefore include how to pull models composed of many submodules together even when the expertise in each is scattered amongst diverse institutions. The technologies of bioinformatics will contribute greatly to this effort. Developing and implementing code for large-scale systems has many problems. Most of the submodules are complex, requiring consideration of spatial and temporal events and processes. Submodules have to be linked to one another in a way that preserves mass balance and gives an accurate representation of variables in nonlinear complex biochemical networks with many signaling and controlling pathways. Microcompartmentalization vitiates the use of simplified model structures. The stiffness of the systems of equations is computationally costly. Faster computation is needed when using models as thinking tools and for iterative data analysis. Perhaps the most serious problem is the current lack of definitive information on kinetics and dynamics of systems, due in part to the almost total lack of databased observations, but also because, though we are nearly drowning in new information being published each day, either the information required for the modeling cannot be found or has never been obtained. "Simple" things like tissue composition, material properties, and mechanical behavior of cells and tissues are not generally available. The development of comprehensive models of biological systems is a key to pharmaceutics and drug design, for the models will become gradually better predictors of the results of interventions, both genomic and pharmaceutic. Good models will be useful in predicting the side effects and long term effects of drugs and toxins, and when the models are really good, to predict where genomic intervention will be effective and where the multiple redundancies in our biological systems will render a proposed intervention useless. The Physiome Project will provide the integrating scientific basis for the Genes to Health initiative, and make physiological genomics a reality applicable to whole organisms, from bacteria to man.

Computer Simulation↗

Construction, database integration, and application of an Oenothera EST library.

Coevolution of cellular genetic compartments is a fundamental aspect in eukaryotic genome evolution that becomes apparent in serious developmental disturbances after interspecific organelle exchanges. The genus Oenothera represents a unique, at present the only available, resource to study the role of the compartmentalized plant genome in diversification of populations and speciation processes. An integrated approach involving cDNA cloning, EST sequencing, and bioinformatic data mining was chosen using Oenothera elata with the genetic constitution nuclear genome AA with plastome type I. The Gene Ontology system grouped 1621 unique gene products into 17 different functional categories. Application of arrays generated from a selected fraction of ESTs revealed significantly differing expression profiles among closely related Oenothera species possessing the potential to generate fertile and incompatible plastid/nuclear hybrids (hybrid bleaching). Furthermore, the EST library provides a valuable source of PCR-based polymorphic molecular markers that are instrumental for genotyping and molecular mapping approaches.

Cell Nucleus↗

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Angiotensin II↗

Protein folding and unfolding simulations: a new challenge for data mining.

One of the unsolved paradigms in molecular biology is the protein folding problem. In recent years, with the identification of several diseases as protein folding disorders and with the explosion of genome information and the need for efficient ways to predict protein structure, protein folding became a central issue in molecular sciences research. Using molecular dynamics unfolding simulations of an amyloidogenic protein--transthyretin--as an example, we put forward a series of ideas on how simulations of this type may be used to infer rules and unfolding behavior in amyloidogenic proteins, and to extrapolate rules for protein folding in different structural classes of proteins. These, in turn, could help in the development of protein structure prediction methods. The need to analyse different proteins and to run multiple simulations creates a huge amount of data which has to be stored, managed, analyzed and shared (database and Grid technology; data mining). Once the data is captured, the next challenge is to find meaningful patterns (associations, correlations, clusters, rules, relationships) among molecular properties, or their relative importance at different stages of the folding or unfolding processes. This clearly puts new and interesting challenges to the bioinformatics community.

Computational Biology↗

Molecular biologist's guide to proteomics.

The emergence of proteomics, the large-scale analysis of proteins, has been inspired by the realization that the final product of a gene is inherently more complex and closer to function than the gene itself. Shortfalls in the ability of bioinformatics to predict both the existence and function of genes have also illustrated the need for protein analysis. Moreover, only through the study of proteins can posttranslational modifications be determined, which can profoundly affect protein function. Proteomics has been enabled by the accumulation of both DNA and protein sequence databases, improvements in mass spectrometry, and the development of computer algorithms for database searching. In this review, we describe why proteomics is important, how it is conducted, and how it can be applied to complement other existing technologies. We conclude that currently, the most practical application of proteomics is the analysis of target proteins as opposed to entire proteomes. This type of proteomics, referred to as functional proteomics, is always driven by a specific biological question. In this way, protein identification and characterization has a meaningful outcome. We discuss some of the advantages of a functional proteomics approach and provide examples of how different methodologies can be utilized to address a wide variety of biological problems.

Amino Acid Sequence↗

Spoligologos: a bioinformatic approach to displaying and analyzing Mycobacterium tuberculosis data.

Spacer oligonucleotide (spoligotyping) analysis is a rapid polymerase chain reaction-based method of DNA fingerprinting the Mycobacterium tuberculosis complex. We examined spoligotype data using a bioinformatic tool (sequence logo analysis) to elucidate undisclosed phylogenetic relationships and gain insights into the global dissemination of strains of tuberculosis. Logo analysis of spoligotyping data provides a simple way to describe a fingerprint signature and may be useful in categorizing unique spoligotypes patterns as they are discovered. Large databases of DNA fingerprint information, such as those from the U.S. National Tuberculosis Genotyping and Surveillance Network and the European Concerted Action on Tuberculosis, contain information on thousands of strains from diverse regions. The description of related spoligotypes has depended on exhaustive listings of the individual spoligotyping patterns. Logo analysis may become another useful graphic method of visualizing and presenting spoligotyping clusters from these databases.

Bacterial Typing Techniques↗

[Cloning and expression analysis of lung carcinoma related gene HLCDG1].

BACKGROUND & OBJECTIVE: High frequent loss of hetero- zygosity (LOH) of 3p, 5q, 6q, 9p, 10q, 11p, 13q, 17p, and 19p in lung carcinoma was detected by comparative genomic hybridization (CGH) and genomic-wide scan for analysis of genetic alteration with microsatellite allelotying. It was indicated that there might be some other unknown tumor susceptible genes or suppressor genes likely to be involved in lung carcinoma development and progression. The aim of this study was to clone the full-length cDNA of LXDD1,an expressed sequence tag(EST) isolated by mRNA differential display, which is significantly down-regulated in lung carcinoma and represents a novel gene. METHODS: Differential expression of LXDD1 in lung carcinoma was confirmed by Northern blot analysis, the expression of the LXDD1 in human normal tissues and the size of the transcription of the LXDD1-representative gene were also determined using MTN (Multiple Tissues Northern Blots). The putative full-length cDNA of the EST-representative gene was cloned and analyzed by bioinformatics. In addition, differential reverse transcription polymerase chain reaction (RT-PCR) was used to detect the expression of the novel gene in various cancer cell lines, primary lung carcinomas, and matched normal lung tissues. RESULTS: The full length cDNA with no homology to any reported genes in the database of GenBank was successfully cloned and named HLCDG1 (Human lung carcinoma deleted gene 1, GenBank accession number AF447582). A transmembrane protein with 166 amino acids was deduced to come from the open reading frame of the 3113 bp full-length cDNA, HLCDG1 gene was confirmed to be located at chromosome band 5q33 by alignment of electric polymerase chain reaction (e-PCR). CONCLUSION: HLCDG1 is a novel gene down-regulated in lung carcinoma, which may be involved in the development of lung carcinoma.

Base Sequence↗

Computational tools for the study of allergens.

Allergy is a major cause of morbidity worldwide. The number of characterized allergens and related information is increasing rapidly creating demands for advanced information storage, retrieval and analysis. Bioinformatics provides useful tools for analysing allergens and these are complementary to traditional laboratory techniques for the study of allergens. Specific applications include structural analysis of allergens, identification of B- and T-cell epitopes, assessment of allergenicity and cross-reactivity, and genome analysis. In this paper, the most important bioinformatic tools and methods with relevance to the study of allergy have been reviewed.

Allergens↗

eL-DASionator: an LDAS upload file generator.

BACKGROUND: The Distributed Annotation System (DAS) allows merging of DNA sequence annotations from multiple sources and provides a single annotation view. A straightforward way to establish a DAS annotation server is to use the "Lightweight DAS" server (LDAS). Onto this type of server, annotations can be uploaded as flat text files in a defined format. The popular Ensembl ContigView uses the same format for the transient upload and display of user data. RESULTS: In order to easily generate LDAS upload files we developed a software tool that is accessible via a web-interface http://atgc.lirmm.fr/eldasionator.html. Users can submit their DNA sequences of interest. Our program (i) aligns these sequences to the reference sequences of Ensembl, (ii) determines start and end positions of each sequence on the reference sequence, and (iii) generates a formatted annotation file. This file can be used to load any LDAS annotation server or it can be uploaded to the Ensembl ContigView. CONCLUSION: The eL-DASionator is an on-line tool that is intended for life-science researchers with little bioinformatics background. It conveniently generates LDAS upload files, and makes it possible to generate annotations in a standard format that permits comfortable sharing of this data.

Base Sequence↗

XML schemas for common bioinformatic data types and their application in workflow systems.

BACKGROUND: Today, there is a growing need in bioinformatics to combine available software tools into chains, thus building complex applications from existing single-task tools. To create such workflows, the tools involved have to be able to work with each other's data--therefore, a common set of well-defined data formats is needed. Unfortunately, current bioinformatic tools use a great variety of heterogeneous formats. RESULTS: Acknowledging the need for common formats, the Helmholtz Open BioInformatics Technology network (HOBIT) identified several basic data types used in bioinformatics and developed appropriate format descriptions, formally defined by XML schemas, and incorporated them in a Java library (BioDOM). These schemas currently cover sequence, sequence alignment, RNA secondary structure and RNA secondary structure alignment formats in a form that is independent of any specific program, thus enabling seamless interoperation of different tools. All XML formats are available at http://bioschemas.sourceforge.net, the BioDOM library can be obtained at http://biodom.sourceforge.net. CONCLUSION: The HOBIT XML schemas and the BioDOM library simplify adding XML support to newly created and existing bioinformatic tools, enabling these tools to interoperate seamlessly in workflow scenarios.

Algorithms↗

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system.

BACKGROUND: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. RESULTS: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70-85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders. CONCLUSIONS: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

Animals↗