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Biomedical subjects

Toshihisa Takagi

Publications and source records attributed to Toshihisa Takagi.

At least 19 recordsLinked to original sources
PubMed2006

MetaGene: prokaryotic gene finding from environmental genome shotgun sequences.

Exhaustive gene identification is a fundamental goal in all metagenomics projects. However, most metagenomic sequences are unassembled anonymous fragments, and conventional gene-finding methods cannot be applied. We have developed a prokaryotic gene-finding program, MetaGene, which utilizes di-codon frequencies estimated by the GC content of a given sequence with other various measures. MetaGene can predict a whole range of prokaryotic genes based on the anonymous genomic sequences of a few hundred bases, with a sensitivity of 95% and a specificity of 90% for artificial shotgun sequences (700 bp fragments from 12 species). MetaGene has two sets of codon frequency interpolations, one for bacteria and one for archaea, and automatically selects the proper set for a given sequence using the domain classification method we propose. The domain classification works properly, correctly assigning domain information to more than 90% of the artificial shotgun sequences. Applied to the Sargasso Sea dataset, MetaGene predicted almost all of the annotated genes and a notable number of novel genes. MetaGene can be applied to wide variety of metagenomic projects and expands the utility of metagenomics.

Computational Biology↗
PubMed2006

Biomedical knowledge navigation by literature clustering.

There is an urgent need for a system that facilitates surveys by biomedical researchers and the subsequent formulation of hypotheses based on the knowledge stored in literature. One approach is to cluster papers discussing a topic of interest and reveal its sub-topics that allow researchers to acquire an overview of the topic. We developed such a system called McSyBi. It accepts a set of citation data retrieved with PubMed and hierarchically and non-hierarchically clusters them based on the titles and the abstracts using statistical and natural language processing methods. A novel point is that McSyBi allows its users to change the clustering by entering a MeSH term or UMLS Semantic Type, and therefore they can see a set of citation data from multiple aspects. We evaluated McSyBi quantitatively and qualitatively: clustering of 27 sets of citation data (40643 different papers) and scrutiny of several resultant clusters. While non-hierarchical clustering provides us with an overview of the target topic, hierarchical clustering allows us to see more details and relationships among citation data. McSyBi is freely available at http://textlens.hgc.jp/McSyBi/.

Artificial Intelligence↗
PubMed2006

Event ontology: a pathway-centric ontology for biological processes.

Event ontology is a new biomedical ontology developed to annotate pathway components in a pathway database. It organizes the concepts and terms of sub-pathways, pathways, biological phenomena, experimental conditions, medications, and external stimuli appearing in biological pathways (e.g. signal transduction, disease-, metabolic-, molecular interaction-, genetic interaction pathways, etc.). Concepts in the Event ontology are extracted manually from scientific literature. Each term has links to external databases such as Gene Ontology, Reactome, KEGG, BioCyc, and PubMed.

Computational Biology↗
PubMed2005

ALICE: an algorithm to extract abbreviations from MEDLINE.

OBJECTIVE: To help biomedical researchers recognize dynamically introduced abbreviations in biomedical literature, such as gene and protein names, we have constructed a support system called ALICE (Abbreviation LIfter using Corpus-based Extraction). ALICE aims to extract all types of abbreviations with their expansions from a target paper on the fly. METHODS: ALICE extracts an abbreviation and its expansion from the literature by using heuristic pattern-matching rules. This system consists of three phases and potentially identifies valid 320 abbreviation-expansion patterns as combinations of the rules. RESULTS: It achieved 95% recall and 97% precision on randomly selected titles and abstracts from the MEDLINE database. CONCLUSION: ALICE extracted abbreviations and their expansions from the literature efficiently. The subtly compiled heuristics enabled it to extract abbreviations with high recall without significantly reducing precision. ALICE does not only facilitate recognition of an undefined abbreviation in a paper by constructing an abbreviation database or dictionary, but also makes biomedical literature retrieval more accurate. This system is freely available at http://uvdb3.hgc.jp/ALICE/ALICE_index.html.

Abbreviations as Topic↗
PubMed2005

Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues.

Gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new methods of diagnosis and therapy. Our collaborative efforts in Japan have been mainly focused on solid tumors such as breast, colorectal and hepatocellular cancers. The expression data are obtained by a high-throughput RT-PCR technique, and patients are recruited mainly from a single hospital. In the cancer gene expression database (CGED), the expression and clinical data are presented in a way useful for scientists interested in specific genes or biological functions. The data can be retrieved either by gene identifiers or by functional categories defined by Gene Ontology terms or the Swiss-Prot annotation. Expression patterns of multiple genes, selected by names or similarity search of the patterns, can be compared. Visual presentation of the data with sorting function enables users to easily recognize of relationships between gene expression and clinical parameters. Data for other cancers such as lung and thyroid cancers will be added in the near future. The URL of CGED is http://cged.hgc.jp.

Databases, Genetic↗
PubMed2005

PRIME: automatically extracted PRotein Interactions and Molecular Information databasE.

With the exponentially increasing amount of information in the biomedical field, the significance of advanced information retrieval and information extraction, as well as the role of databases, has been increasing. PRIME is an integrated gene/protein informatics database based on natural language processing. It provides automatically extracted protein/family/gene/compound interaction information including both physical and genetic interactions, gene ontology based functions, and graphic pathway viewers. Gene/protein/family names and functional terms are recognized based on dictionaries developed in our laboratory. The interaction and functional information are extracted by syntactic dependencies and various phrase patterns. We have included about 920,000 (non-redundant) protein interactions and 360,000 annotated gene-function relationships for major eukaryotes. By combining the sequence and text information, the pathway comparison between two organisms and simple pathway deduction based on other organism interaction data, and pathway filtering using tissue expression data, are also available. This database is accessible at http://prime.ontology.ims.u-tokyo.ac.jp:8081.

Abstracting and Indexing↗
PubMed2005

Reassembly and interfacing neural models registered on biological model databases.

The importance of modeling and simulation of biological process is growing for further understanding of living systems at all scales from molecular to cellular, organic, and individuals. In the field of neuroscience, there are so called platform simulators, the de-facto standard neural simulators. More than a hundred neural models are registered on the model database. These models are executable in corresponding simulation environments. But usability of the registered models is not sufficient. In order to make use of the model, the users have to identify the input, output and internal state variables and parameters of the models. The roles and units of each variable and parameter are not explicitly defined in the model files. These are suggested implicitly in the papers where the simulation results are demonstrated. In this study, we propose a novel method of reassembly and interfacing models registered on biological model database. The method was applied to the neural models registered on one of the typical biological model database, ModelDB. The results are described in detail with the hippocampal pyramidal neuron model. The model is executable in NEURON simulator environment, which demonstrates that somatic EPSP amplitude is independent of synapse location. Input and output parameters and variables were identified successfully, and the results of the simulation were recorded in the organized form with annotations.

Computational Biology↗
PubMed2004

Automatic extraction of gene/protein biological functions from biomedical text.

MOTIVATION: With the rapid advancement of biomedical science and the development of high-throughput analysis methods, the extraction of various types of information from biomedical text has become critical. Since automatic functional annotations of genes are quite useful for interpreting large amounts of high-throughput data efficiently, the demand for automatic extraction of information related to gene functions from text has been increasing. RESULTS: We have developed a method for automatically extracting the biological process functions of genes/protein/families based on Gene Ontology (GO) from text using a shallow parser and sentence structure analysis techniques. When the gene/protein/family names and their functions are described in ACTOR (doer of action) and OBJECT (receiver of action) relationships, the corresponding GO-IDs are assigned to the genes/proteins/families. The gene/protein/family names are recognized using the gene/protein/family name dictionaries developed by our group. To achieve wide recognition of the gene/protein/family functions, we semi-automatically gather functional terms based on GO using co-occurrence, collocation similarities and rule-based techniques. A preliminary experiment demonstrated that our method has an estimated recall of 54-64% with a precision of 91-94% for actually described functions in abstracts. When applied to the PUBMED, it extracted over 190 000 gene-GO relationships and 150 000 family-GO relationships for major eukaryotes.

Abstracting and Indexing↗
PubMed2004

FREX: a query interface for biological processes with hierarchical and recursive structures.

An intelligent system for signal transduction pathways and other higher order functional knowledge is presented. Molecular mechanisms of biological processes are typically represented as diagrams ("pathways") that have a graph-analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and range from metal ion to protein to biological processes in general. In addition, the kinds of interactions that connect biological entities are likewise diverse. Consequently, current knowledge about pathways is highly heterogeneous both in the sense of the types of constituents and the granularity of descriptions. To cope with this problem, the proposed system adopts a recursive and hierarchical representation model that enables the annotation and query of pathways or sub-pathways of arbitral granularity. By combining the use of this hierarchical structure and biological ontologies, literature-based information regarding biological mechanisms becomes accessible by computer.

Computational Biology↗
PubMed2004

Predicting rules on organization of cis-regulatory elements, taking the order of elements into account.

MOTIVATION: In eukaryotes, rules regarding organization of cis-regulatory elements are complex. They sometimes govern multiple kinds of elements and positional restrictions on elements. RESULTS: We propose a method for detecting rules, by which the order of elements is restricted. The order restriction is expressed as element patterns. We extract all the element patterns that occur in promoter regions of at least the specified number of genes. Then, we find significant patterns based on the expression similarity of genes with promoter regions containing each of the extracted patterns. When we applied our method to Saccharomyces cerevisiae, we detected significant patterns overlooked by previous methods, thus demonstrating the utility of our method for analyses of eukaryotic gene regulation. We also suggest that several types of element organization exist: (i) those in which only the order of elements is important, (ii) order and distance both are important and (iii) only the combination of elements is important. AVAILABILITY: The program for extracting element patterns is available upon request.

Algorithms↗
PubMed2004

Prediction of protein-protein interaction sites using support vector machines.

The identification of protein-protein interaction sites is essential for the mutant design and prediction of protein-protein networks. The interaction sites of residue units were predicted using support vector machines (SVM) and the profiles of sequentially/spatially neighboring residues, plus additional information. When only sequence information was used, prediction performance was highest using the feature vectors, sequentially neighboring profiles and predicted interaction site ratios, which were calculated by SVM regression using amino acid compositions. When structural information was also used, prediction performance was highest using the feature vectors, spatially neighboring residue profiles, accessible surface areas, and the with/without protein interaction sites ratios predicted by SVM regression and amino acid compositions. In the latter case, the precision at recall = 50% was 54-56% for a homo-hetero mixed test set and >20% higher than for random prediction. Approximately 30% of the residues wrongly predicted as interaction sites were the closest sequentially/spatially neighboring on the interaction site residues. The predicted residues covered 86-87% of the actual interfaces (96-97% of interfaces with over 20 residues). This prediction performance appeared to be slightly higher than a previously reported study. Comparing the prediction accuracy of each molecule, it seems to be easier to predict interaction sites for stable complexes.

Algorithms↗
PubMed2004

Large-scale collection and characterization of promoters of human and mouse genes.

We report the generation and initial characterization of a large-scale collection of sequences of putative promoter regions (PPRs) of human and mouse genes. Based on our unique collection of 400,225 and 580,209 human and mouse full-length cDNAs, we determined exact transcriptional start sites (TSSs). Using positional information of the TSSs, we could retrieve adjacent sequences as PPRs for 8,793 and 6,875 human and mouse genes, respectively. The positions of the PPRs were 4 kb upstream to previously reported 5'-ends of cDNAs on average, demonstrating that full-length cDNA information is indispensable for this purpose. Among those PPRs supported by experimentally validated TSSs, 3,324 could be paired as mutually homologous genes between human and mouse and were used for the comprehensive comparative studies. The sequence identities in the proximal regions of the TSSs were 45% on average, and 22,794 putative transcription factor binding sites that are conserved between human and mouse were identified. The data resource created in the present work and the results of the sequences' initial characterization should lay the firm foundation for deciphering the transcriptional modulations of human genes. All the data were deposited and made available through a database for comparative studies, DBTSS.

Animals↗
PubMed2003

Melina: motif extraction from promoter regions of potentially co-regulated genes.

UNLABELLED: 'Melina' assists users to compare the results of four public softwares for DNA motif extraction in order to both confirm the reliability of each finding and avoid missing important motifs. It is also useful to optimize the sensitivity of software with a series of different parameter settings. AVAILABILITY: Melina is available at http://www.hgc.ims.u-tokyo.ac.jp/Melina/.

Amino Acid Motifs↗
PubMed2003

Kinase pathway database: an integrated protein-kinase and NLP-based protein-interaction resource.

Protein kinases play a crucial role in the regulation of cellular functions. Various kinds of information about these molecules are important for understanding signaling pathways and organism characteristics. We have developed the Kinase Pathway Database, an integrated database involving major completely sequenced eukaryotes. It contains the classification of protein kinases and their functional conservation, ortholog tables among species, protein-protein, protein-gene, and protein-compound interaction data, domain information, and structural information. It also provides an automatic pathway graphic image interface. The protein, gene, and compound interactions are automatically extracted from abstracts for all genes and proteins by natural-language processing (NLP). The method of automatic extraction uses phrase patterns and the GENA protein, gene, and compound name dictionary, which was developed by our group. With this database, pathways are easily compared among species using data with more than 47,000 protein interactions and protein kinase ortholog tables. The database is available for querying and browsing at http://kinasedb.ontology.ims.u-tokyo.ac.jp/.

Animals↗
PubMed2002

JSNP: a database of common gene variations in the Japanese population.

JSNP is a repository of Japanese Single Nucleotide Polymorphism (SNP) data, begun in 2000 and developed through the Prime Minister's Millennium Project. The aim of this undertaking is to identify and collate up to 150 000 SNPs from the Japanese population, located in genes or in adjacent regions that might influence the coding sequence of the genes. The project has been carried out by a collaboration between the Human Genome Center (HGC) in the Institute of Medical Science (IMS) at the University of Tokyo and the Japan Science and Technology Corporation (JST). JSNP serves as both a storage site for the Japanese SNPs obtained from the ongoing project and as a facility for public dissemination to allow researchers access to high quality SNP data. A primary motivation of the project is the construction of a basic data set to identify relationships between polymorphisms and common diseases or the reaction to drugs. As such, emphasis has been placed on the identification of SNPs that lie in candidate regions which may affect phenotype but which would not necessarily directly cause disease. Unrestricted access to JSNP and any associated files is available at http://snp.ims.u-tokyo.ac.jp/.

Chromosomes, Human↗
PubMedDate not supplied

A < 1.7 cM interval is responsible for Dmo1 obesity phenotypes in OLETF rats.

1. Dmo1 (Diabetes Mellitus OLETF type I) is a major quantitative trait locus for dyslipidaemia, obesity and diabetes phenotypes of male Otsuka Long Evans Tokushima Fatty (OLETF) rats. 2. Our congenic lines, produced by transferring Dmo1 chromosomal segments from the non-diabetic Brown Norway (BN) rat into the OLETF strain, have confirmed the strong, wide-range therapeutic effects of Dmo1 on dyslipidaemia, obesity and diabetes in the fourth (BC4) and fifth (BC5) generations of congenic animals. Analysis of a relatively small number of BC5 rats (n = 71) suggested that the critical Dmo1 interval lies within a < 4.9 cM region between D1Rat461 and D1Rat459. 3. To confirm the assignment of the Dmo1 critical interval, we intercrossed BC5 animals to produce a larger study population (BC5:F1 males; n = 406). For the present study, we used bodyweight at 18 weeks of age as an index of obesity; this phenotype is representative of the closely associated dyslipidaemia and hyperglycaemia phenotypes. 4. Interval mapping assigned logarithm of odds (LOD) peaks at the D1Rat90 marker (LOD = 9.11). One LOD support interval lies within the < 1.7 cM region between D1Rat461 and D1Rat459. 5. This large intercross study confirms that Dmo1 is likely localized within the interval.

Animals↗