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The Los Alamos hepatitis C sequence database.

MOTIVATION: The hepatitis C virus (HCV) is a significant threat to public health worldwide. The virus is highly variable and evolves rapidly, making it an elusive target for the immune system and for vaccine and drug design. At present, some 30 000 HCV sequences have been published. A central website that provides annotated sequences and analysis tools will be helpful to HCV scientists worldwide. RESULTS: The HCV sequence database collects and annotates sequence data and provides them to the public via a website that contains a user-friendly search interface and a large number of sequence analysis tools, based on the model of the highly regarded Los Alamos HIV database. The HCV sequence database was officially launched in September 2003. Since then, its usage has steadily increased and is now at an average of approximately 280 visits per day from distinct IP addresses. AVAILABILITY: The HCV website can be accessed via http://hcv.lanl.gov and http://hcv-db.org.

Amino Acid Sequence↗

Widespread occurrence of antisense transcription in the human genome.

An increasing number of eukaryotic genes are being found to have naturally occurring antisense transcripts. Here we study the extent of antisense transcription in the human genome by analyzing the public databases of expressed sequences using a set of computational tools designed to identify sense-antisense transcriptional units on opposite DNA strands of the same genomic locus. The resulting data set of 2,667 sense-antisense pairs was evaluated by microarrays containing strand-specific oligonucleotide probes derived from the region of overlap. Verification of specific cases by northern blot analysis with strand-specific riboprobes proved transcription from both DNA strands. We conclude that > or =60% of this data set, or approximately 1,600 predicted sense-antisense transcriptional units, are transcribed from both DNA strands. This indicates that the occurrence of antisense transcription, usually regarded as infrequent, is a very common phenomenon in the human genome. Therefore, antisense modulation of gene expression in human cells may be a common regulatory mechanism.

Algorithms↗

Method for automatic management of the semantic network ambiguity in the UMLS: possible application for information retrieval on the Web.

The Unified Medical Language System (UMLS) is an extensive collection of terms and concepts. The UMLS includes biomedical terms from standard classifications. The semantic network (SN) links the concepts, sometimes ambiguously. In this paper we try, on one hand to describe the relationship between concepts more efficiently and on the other hand to find new relationships. Assuming that re-usability and automatic extraction of knowledge from existing thesaurus enables an improvement of the metatheasaurus, we cross the SN with linked concepts from the ADM (Assisted Medical Diagnosis). Results are presented and our discussion concerns firstly the use of the SN only; secondly the improvement that allows pre-selection of linked concepts, and thirdly the possibility to coincide with other developments that improve the metathesaurus.

Electronic Data Processing↗

ProRule: a new database containing functional and structural information on PROSITE profiles.

MOTIVATION: Increase the discriminatory power of PROSITE profiles to facilitate function determination and provide biologically relevant information about domains detected by profiles for the annotation of proteins. SUMMARY: We have created a new database, ProRule, which contains additional information about PROSITE profiles. ProRule contains notably the position of structurally and/or functionally critical amino acids, as well as the condition they must fulfill to play their biological role. These supplementary data should help function determination and annotation of the UniProt Swiss-Prot knowledgebase. ProRule also contains information about the domain detected by the profile in the Swiss-Prot line format. Hence, ProRule can be used to make Swiss-Prot annotation more homogeneous and consistent. The format of ProRule can be extended to provide information about combination of domains. AVAILABILITY: ProRule can be accessed through ScanProsite at http://www.expasy.org/tools/scanprosite. A file containing the rules will be made available under the PROSITE copyright conditions on our ftp site (ftp://www.expasy.org/databases/prosite/) by the next PROSITE release.

Amino Acid Sequence↗

ProtBuD: a database of biological unit structures of protein families and superfamilies.

MOTIVATION: Modeling of protein interactions is often possible from known structures of related complexes. It is often time-consuming to find the most appropriate template. Hypothesized biological units (BUs) often differ from the asymmetric units and it is usually preferable to model from the BUs. RESULTS: ProtBuD is a database of BUs for all structures in the Protein Data Bank (PDB). We use both the PDBs BUs and those from the Protein Quaternary Server. ProtBuD is searchable by PDB entry, the Structural Classification of Proteins (SCOP) designation or pairs of SCOP designations. The database provides the asymmetric and BU contents of related proteins in the PDB as identified in SCOP and Position-Specific Iterated BLAST (PSI-BLAST). The asymmetric unit is different from PDB and/or Protein Quaternary Server (PQS) BUs for 52% of X-ray structures, and the PDB and PQS BUs disagree on 18% of entries. AVAILABILITY: The database is provided as a standalone program and a web server from http://dunbrack.fccc.edu/ProtBuD.php.

Amino Acid Sequence↗

DWARF--a data warehouse system for analyzing protein families.

BACKGROUND: The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. DESCRIPTION: The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of alpha/beta-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. CONCLUSION: DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering.

Amino Acid Sequence↗

Detection of signals in mRNAs that influence translation.

Genome sequencing efforts mean that we now have extensive data from a wide range of organisms to study. Understanding the differing natures of the biology of these organisms is an important aim of genome analysis. We are interested in signals that affect translation of mRNAs. Some signals in the mRNA influence how efficiently it is translated into protein. Previous studies have indicated that many important signals are located around the initiation and termination codons. We have developed tools described here to extract the relevant sequence regions from GenBank. To create databases organised by species, or higher taxonomic groupings (eg planta), a program was developed to dynamically view and edit the taxonomy database. Data from relevant species were then extracted using our Genbank feature table parser. We analysed all available sequences, particularly those from complete genomes. Patterns were then identified using information theory. The software is available from http://transterm.otago.ac.nz. Patterns around the initiation codons for most of the organisms fall into two groups, containing the previously known Shine-Dalgarno and Kozaks efficiency signals. However, we have identified several organisms that appear to utilise novel systems. Our analysis indicates that some organisms with extremely high GC% genomes do not have a strong dependence on base pairing ribosome binding sites, as the complementary sequence is absent from many genes.

Abstracting and Indexing↗

Breast tissue banking: collection, handling, storage, and release of tissue for breast cancer research.

A "tumor bank" provides a resource tool that can efficiently address the limitations of other tissue resources, such as clinical archives and research study collections. A tumor bank allows formal accreditation of ethics and security procedures, and through a dedicated management, processing, storage, retrieval, and release structure, can provide selected and preassembled material and data for research and discovery. This chapter describes the design, operating protocols, and methods for processing, storage, and release of the Manitoba Breast Tumor Bank.

Animals↗

Evaluation of annotation strategies using an entire genome sequence.

MOTIVATION: Genome-wide functional annotation either by manual or automatic means has raised considerable concerns regarding the accuracy of assignments and the reproducibility of methodologies. In addition, a performance evaluation of automated systems that attempt to tackle sequence analyses rapidly and reproducibly is generally missing. In order to quantify the accuracy and reproducibility of function assignments on a genome-wide scale, we have re-annotated the entire genome sequence of Chlamydia trachomatis (serovar D), in a collaborative manner. RESULTS: We have encoded all annotations in a structured format to allow further comparison and data exchange and have used a scale that records the different levels of potential annotation errors according to their propensity to propagate in the database due to transitive function assignments. We conclude that genome annotation may entail a considerable amount of errors, ranging from simple typographical errors to complex sequence analysis problems. The most surprising result of this comparative study is that automatic systems might perform as well as the teams of experts annotating genome sequences.

Amino Acid Sequence↗

[The Quick Return Service of electroencephalogram].

Because electroencephalography (EEG) examinations take time and numerous channels are used, they generate an enormous volume of data. Vast amounts of space would be required to store the data on shelves and in storage rooms, and a great deal of time and labor would be needed to retrieve the data and use it again. We developed an EEG optical disk filing system to solve such problems. With this system display terminals are set up in the various outpatient departments which often request EEG examinations, i.e., the neurology department, psychiatry department, and pediatric department, making it possible to search and display EEG waveforms, the content of EEG reports, etc., stored on the optical disks at any time. Using such functions patients can be examined without having to send for charts and EEG paper-output waveforms, which is troublesome and time-consuming, and contributes to shortening examination time.

Electroencephalography↗

Mining viral protease data to extract cleavage knowledge.

MOTIVATION: The motivation is to identify, through machine learning techniques, specific patterns in HIV and HCV viral polyprotein amino acid residues where viral protease cleaves the polyprotein as it leaves the ribosome. An understanding of viral protease specificity may help the development of future anti-viral drugs involving protease inhibitors by identifying specific features of protease activity for further experimental investigation. While viral sequence information is growing at a fast rate, there is still comparatively little understanding of how viral polyproteins are cut into their functional unit lengths. The aim of the work reported here is to investigate whether it is possible to generalise from known cleavage sites to unknown cleavage sites for two specific viruses-HIV and HCV. An understanding of proteolytic activity for specific viruses will contribute to our understanding of viral protease function in general, thereby leading to a greater understanding of protease families and their substrate characteristics. RESULTS: Our results show that artificial neural networks and symbolic learning techniques (See5) capture some fundamental and new substrate attributes, but neural networks outperform their symbolic counterpart.

Algorithms↗

Mining frequent patterns in protein structures: a study of protease families.

MOTIVATION: Analysis of protein sequence and structure databases usually reveal frequent patterns (FP) associated with biological function. Data mining techniques generally consider the physicochemical and structural properties of amino acids and their microenvironment in the folded structures. Dynamics is not usually considered, although proteins are not static, and their function relates to conformational mobility in many cases. RESULTS: This work describes a novel unsupervised learning approach to discover FPs in the protein families, based on biochemical, geometric and dynamic features. Without any prior knowledge of functional motifs, the method discovers the FPs for each type of amino acid and identifies the conserved residues in three protease subfamilies; chymotrypsin and subtilisin subfamilies of serine proteases and papain subfamily of cysteine proteases. The catalytic triad residues are distinguished by their strong spatial coupling (high interconnectivity) to other conserved residues. Although the spatial arrangements of the catalytic residues in the two subfamilies of serine proteases are similar, their FPs are found to be quite different. The present approach appears to be a promising tool for detecting functional patterns in rapidly growing structure databases and providing insights in to the relationship among protein structure, dynamics and function. AVAILABILITY: Available upon request from the authors.

Algorithms↗

Identification of putative domain linkers by a neural network - application to a large sequence database.

BACKGROUND: The reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database (101602 sequences). RESULTS: Our search detected 3009 putative domain linkers adjacent to or overlapping with domains, as defined by sequence similarity to either Protein Data Bank (PDB) or Conserved Domain Database (CDD) sequences. Among these putative linkers, 75% were "correctly" located within 20 residues of a domain terminus, and the remaining 25% were found in the middle of a domain, and probably represented failed predictions. Moreover, our neural network predicted 5124 putative domain linkers in structurally un-annotated regions without sequence similarity to PDB or CDD sequences, which suggest to the possible existence of novel structural domains. As a comparison, we performed the same analysis by identifying low-complexity regions (LCR), which are known to encode unstructured polypeptide segments, and observed that the fraction of LCRs that correlate with domain termini is similar to that of domain linkers. However, domain linkers and LCRs appeared to identify different types of domain boundary regions, as only 32% of the putative domain linkers overlapped with LCRs. CONCLUSION: Overall, our study indicates that the two methods detect independent and complementary regions, and that the combination of these methods can substantially improve the sensitivity of the domain boundary prediction. This finding should enable the identification of novel structural domains, yielding new targets for large scale protein analyses.

Algorithms↗

The LBI-method for automated indexing of diagnoses by using SNOMED. Part 1. Design and realization.

We present a simple, formal, lexicon-based method for automated indexing of diagnoses based on the Systematized Nomenclature of Medicine (SNOMED II), called the LBI-method. Part 1 gives an introduction to the LBI-method and presents its realization as application system SALBIDH. The underlying model states that a diagnosis is represented by a set of indices of any nomenclature. The LBI-method is defined as a composition of functions, which in turn define the 3 steps of the LBI-method: preprocessing, morphological analysis, and semantic analysis. Part 2 will focus on the design and the results of an evaluation study to judge the quality of the LBI-method. In this evaluation study the quality of automated indexing was examined as well as the quality of the retrieval of patient data by using automated indexed diagnoses.

Abstracting and Indexing↗

The homeodomain resource: a prototype database for a large protein family.

The Homeodomain Resource is an annotated collection of non-redundant protein sequences, three-dimensional structures and genomic information for the homeodomain protein family. Release 2.0 contains 765 full-length homeodomain-containing sequences, 29 experimentally derived structures and 116 homeobox loci implicated in human genetic disorders. Entries are fully hyperlinked to facilitate easy retrieval of the original records from source databases. A simple search engine with a graphical user interface is provided to query the component databases and assemble customized data sets. A new feature for this release is the addition of more automated methods for database searching, maintenance and implementation of efficient data management. The Homeodomain Resource is freely available through the WWW at http://genome.nhgri.nih.gov/homeodomain

Amino Acid Sequence↗

Genepi: a blackboard framework for genome annotation.

BACKGROUND: Genome annotation can be viewed as an incremental, cooperative, data-driven, knowledge-based process that involves multiple methods to predict gene locations and structures. This process might have to be executed more than once and might be subjected to several revisions as the biological (new data) or methodological (new methods) knowledge evolves. In this context, although a lot of annotation platforms already exist, there is still a strong need for computer systems which take in charge, not only the primary annotation, but also the update and advance of the associated knowledge. In this paper, we propose to adopt a blackboard architecture for designing such a system RESULTS: We have implemented a blackboard framework (called Genepi) for developing automatic annotation systems. The system is not bound to any specific annotation strategy. Instead, the user will specify a blackboard structure in a configuration file and the system will instantiate and run this particular annotation strategy. The characteristics of this framework are presented and discussed. Specific adaptations to the classical blackboard architecture have been required, such as the description of the activation patterns of the knowledge sources by using an extended set of Allen's temporal relations. Although the system is robust enough to be used on real-size applications, it is of primary use to bioinformatics researchers who want to experiment with blackboard architectures. CONCLUSION: In the context of genome annotation, blackboards have several interesting features related to the way methodological and biological knowledge can be updated. They can readily handle the cooperative (several methods are implied) and opportunistic (the flow of execution depends on the state of our knowledge) aspects of the annotation process.

Algorithms↗

Methods for semi-automated indexing for high precision information retrieval.

OBJECTIVE: To evaluate a new system, ISAID (Internet-based Semi-automated Indexing of Documents), and to generate textbook indexes that are more detailed and more useful to readers. DESIGN: Pilot evaluation: simple, nonrandomized trial comparing ISAID with manual indexing methods. Methods evaluation: randomized, cross-over trial comparing three versions of ISAID and usability survey. PARTICIPANTS: Pilot evaluation: two physicians. Methods evaluation: twelve physicians, each of whom used three different versions of the system for a total of 36 indexing sessions. MEASUREMENTS: Total index term tuples generated per document per minute (TPM), with and without adjustment for concordance with other subjects; inter-indexer consistency; ratings of the usability of the ISAID indexing system. RESULTS: Compared with manual methods, ISAID decreased indexing times greatly. Using three versions of ISAID, inter-indexer consistency ranged from 15% to 65% with a mean of 41%, 31%, and 40% for each of three documents. Subjects using the full version of ISAID were faster (average TPM: 5.6) and had higher rates of concordant index generation. There were substantial learning effects, despite our use of a training/run-in phase. Subjects using the full version of ISAID were much faster by the third indexing session (average TPM: 9.1). There was a statistically significant increase in three-subject concordant indexing rate using the full version of ISAID during the second indexing session (p < 0.05). SUMMARY: Users of the ISAID indexing system create complex, precise, and accurate indexing for full-text documents much faster than users of manual methods. Furthermore, the natural language processing methods that ISAID uses to suggest indexes contributes substantially to increased indexing speed and accuracy.

Abstracting and Indexing↗

V-MitoSNP: visualization of human mitochondrial SNPs.

BACKGROUND: Mitochondrial single nucleotide polymorphisms (mtSNPs) constitute important data when trying to shed some light on human diseases and cancers. Unfortunately, providing relevant mtSNP genotyping information in mtDNA databases in a neatly organized and transparent visual manner still remains a challenge. Amongst the many methods reported for SNP genotyping, determining the restriction fragment length polymorphisms (RFLPs) is still one of the most convenient and cost-saving methods. In this study, we prepared the visualization of the mtDNA genome in a way, which integrates the RFLP genotyping information with mitochondria related cancers and diseases in a user-friendly, intuitive and interactive manner. The inherent problem associated with mtDNA sequences in BLAST of the NCBI database was also solved. DESCRIPTION: V-MitoSNP provides complete mtSNP information for four different kinds of inputs: (1) color-coded visual input by selecting genes of interest on the genome graph, (2) keyword search by locus, disease and mtSNP rs# ID, (3) visualized input of nucleotide range by clicking the selected region of the mtDNA sequence, and (4) sequences mtBLAST. The V-MitoSNP output provides 500 bp (base pairs) flanking sequences for each SNP coupled with the RFLP enzyme and the corresponding natural or mismatched primer sets. The output format enables users to see the SNP genotype pattern of the RFLP by virtual electrophoresis of each mtSNP. The rate of successful design of enzymes and primers for RFLPs in all mtSNPs was 99.1%. The RFLP information was validated by actual agarose electrophoresis and showed successful results for all mtSNPs tested. The mtBLAST function in V-MitoSNP provides the gene information within the input sequence rather than providing the complete mitochondrial chromosome as in the NCBI BLAST database. All mtSNPs with rs number entries in NCBI are integrated in the corresponding SNP in V-MitoSNP. CONCLUSION: V-MitoSNP is a web-based software platform that provides a user-friendly and interactive interface for mtSNP information, especially with regard to RFLP genotyping. Visual input and output coupled with integrated mtSNP information from MITOMAP and NCBI make V-MitoSNP an ideal and complete visualization interface for human mtSNPs association studies.

Base Sequence↗