Yasubumi Sakakibara

Summary

Affiliation: Keio University
Country: Japan

Publications

  1. ncbi Grammatical inference in bioinformatics
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, 223 8522, Japan
    IEEE Trans Pattern Anal Mach Intell 27:1051-62. 2005
  2. ncbi COPICAT: a software system for predicting interactions between proteins and chemical compounds
    Yasubumi Sakakibara
    Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Yokohama 223 8522, Japan
    Bioinformatics 28:745-6. 2012
  3. ncbi Whole genome assembly of a natto production strain Bacillus subtilis natto from very short read data
    Yukari Nishito
    Department of Biosciences and Informatics, Keio University, Hiyoshi, Kohoku Ku, Yokohama, Japan
    BMC Genomics 11:243. 2010
  4. ncbi Directed acyclic graph kernels for structural RNA analysis
    Kengo Sato
    Japan Biological Informatics Consortium JBIC, 2 45 Aomi, Koto ku, Tokyo 135 8073, Japan
    BMC Bioinformatics 9:318. 2008
  5. ncbi Pair hidden Markov models on tree structures
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, 223 8522, Japan
    Bioinformatics 19:i232-40. 2003
  6. ncbi Stem kernels for RNA sequence analyses
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, Kanagawa 223 8522, Japan
    J Bioinform Comput Biol 5:1103-22. 2007
  7. ncbi [Development of a large-scale comparative genome system and its application to the analysis of mycobacteria genomes]
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, Japan
    Nihon Hansenbyo Gakkai Zasshi 76:251-6. 2007
  8. ncbi Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening
    Nobuyoshi Nagamine
    Department of Biosciences and Informatics, Keio University, Yokohama, Japan
    PLoS Comput Biol 5:e1000397. 2009
  9. ncbi Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures
    Hiroshi Matsui
    Keio University, Department of Biosciences and Informatics, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
    Bioinformatics 21:2611-7. 2005
  10. ncbi RNA secondary structural alignment with conditional random fields
    Kengo Sato
    Department of Biosciences and Informatics, Keio University, Hiyoshi Kohoku ku, Yokohama, Japan
    Bioinformatics 21:ii237-42. 2005

Collaborators

Detail Information

Publications23

  1. ncbi Grammatical inference in bioinformatics
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, 223 8522, Japan
    IEEE Trans Pattern Anal Mach Intell 27:1051-62. 2005
    ....
  2. ncbi COPICAT: a software system for predicting interactions between proteins and chemical compounds
    Yasubumi Sakakibara
    Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Yokohama 223 8522, Japan
    Bioinformatics 28:745-6. 2012
    ..dna.bio.keio.ac.jp. All functions, including the prediction function are freely available via anonymous login without registration. Registered users, however, can use the system more intensively...
  3. ncbi Whole genome assembly of a natto production strain Bacillus subtilis natto from very short read data
    Yukari Nishito
    Department of Biosciences and Informatics, Keio University, Hiyoshi, Kohoku Ku, Yokohama, Japan
    BMC Genomics 11:243. 2010
    ..subtilis natto, from very short read data is more challenging, particularly with our aim to assemble one fully connected scaffold from short reads around 35 bp in length...
  4. ncbi Directed acyclic graph kernels for structural RNA analysis
    Kengo Sato
    Japan Biological Informatics Consortium JBIC, 2 45 Aomi, Koto ku, Tokyo 135 8073, Japan
    BMC Bioinformatics 9:318. 2008
    ..However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity...
  5. ncbi Pair hidden Markov models on tree structures
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, 223 8522, Japan
    Bioinformatics 19:i232-40. 2003
    ..We demonstrate some computational experiments to show the effectiveness of our method for structural alignments, and discuss a complexity issue of PHMMTSs...
  6. ncbi Stem kernels for RNA sequence analyses
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, Kanagawa 223 8522, Japan
    J Bioinform Comput Biol 5:1103-22. 2007
    ..This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences...
  7. ncbi [Development of a large-scale comparative genome system and its application to the analysis of mycobacteria genomes]
    Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, Japan
    Nihon Hansenbyo Gakkai Zasshi 76:251-6. 2007
    ..g. three mammal chromosomes) in a matter of minutes. We also demonstrate an application of Murasaki to the comparative analysis of multiple mycobacteria genomes...
  8. ncbi Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening
    Nobuyoshi Nagamine
    Department of Biosciences and Informatics, Keio University, Yokohama, Japan
    PLoS Comput Biol 5:e1000397. 2009
    ....
  9. ncbi Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures
    Hiroshi Matsui
    Keio University, Department of Biosciences and Informatics, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
    Bioinformatics 21:2611-7. 2005
    ..We believe that our implemented program based on PSTAGs is the first grammar-based and practically executable software for comparative analyses of RNA pseudoknot structures, and, further, non-coding RNAs...
  10. ncbi RNA secondary structural alignment with conditional random fields
    Kengo Sato
    Department of Biosciences and Informatics, Keio University, Hiyoshi Kohoku ku, Yokohama, Japan
    Bioinformatics 21:ii237-42. 2005
    ..Several methods have been proposed to accomplish the structural alignment tasks for RNA sequences, and we found that one of the most important points is to estimate an accurate score matrix for calculating structural alignments...
  11. ncbi Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures
    Yutaka Saito
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, Kanagawa 223 8522, Japan
    BMC Bioinformatics 12:S48. 2011
    ..Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level...
  12. ncbi Genome-wide searching with base-pairing kernel functions for noncoding RNAs: computational and expression analysis of snoRNA families in Caenorhabditis elegans
    Kensuke Morita
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, Kanagawa 223 8522, Japan
    Nucleic Acids Res 37:999-1009. 2009
    ..Finally, highly expressed six candidates were identified as the original target regions by DNA sequencing...
  13. ncbi Accurate identification of orthologous segments among multiple genomes
    Tsuyoshi Hachiya
    Department of Biosciences and Informatics, Keio University, Seikei University, Japan
    Bioinformatics 25:853-60. 2009
    ..Although a number of algorithms for detecting orthologous segments have been proposed, none of them contain a framework for optimizing their parameter values...
  14. ncbi Robust and accurate prediction of noncoding RNAs from aligned sequences
    Yutaka Saito
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, Kanagawa 223 8522, Japan
    BMC Bioinformatics 11:S3. 2010
    ..Therefore, the evaluation of the robustness against alignment errors is necessary as well as the development of accurate prediction methods...
  15. ncbi Murasaki: a fast, parallelizable algorithm to find anchors from multiple genomes
    Kris Popendorf
    Department of Biosciences and Informatics, Keio University, Yokohama, Japan
    PLoS ONE 5:e12651. 2010
    ....
  16. ncbi Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data
    Nobuyoshi Nagamine
    Department of Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Kohoku Ku, Yokohama, Japan
    Bioinformatics 23:2004-12. 2007
    ..In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability...
  17. ncbi Identifying cooperative transcriptional regulations using protein-protein interactions
    Nobuyoshi Nagamine
    Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
    Nucleic Acids Res 33:4828-37. 2005
    ..However, since a typical problem using protein-protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy...
  18. ncbi Construction of a genetic AND gate under a new standard for assembly of genetic parts
    Shotaro Ayukawa
    Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Midori ku, Yokohama, Kanagawa, Japan
    BMC Genomics 11:S16. 2010
    ..Thus, a standardized method for integrating operator sequences to the regulatory region of a plasmid is required...
  19. ncbi Improved measurements of RNA structure conservation with generalized centroid estimators
    Yohei Okada
    Department of Biosciences and Informatics, Keio University Yokohama, Japan
    Front Genet 2:54. 2011
    ..We conclude that our methods are more suitable for genome-wide alignments which are of low quality from the point of view on secondary structures than the original SCI and BPD...
  20. ncbi SAMSCOPE: an OpenGL-based real-time interactive scale-free SAM viewer
    Kris Popendorf
    Biosciences and Informatics, Keio University, 3 14 1 Hiyoshi, Yokohama, 223 8522, Japan
    Bioinformatics 28:1276-7. 2012
    ..Availability and implementation: The SAMSCOPE software, implemented in C++ for Linux, with source code, binary packages and documentation are freely available from http://samscope.dna.bio.keio.ac.jp. CONTACT: yasu@bio.keio.ac.jp...
  21. ncbi The Dugesia ryukyuensis database as a molecular resource for studying switching of the reproductive system
    Hideyuki Ishizuka
    Department of Biological Sciences and Informatics, Keio University, Hiyoshi, Yokohama, Japan
    Zoolog Sci 24:31-7. 2007
    ..bio.keio.ac.jp/planaria/) is an open-access, online resource providing access to sequence, classification, clustering, and annotation data. This database should constitute a powerful tool for analyzing sexualization in planarians...
  22. ncbi Software.ncrna.org: web servers for analyses of RNA sequences
    Kiyoshi Asai
    Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, 5 1 5 Kashiwa no ha, Chiba 277 8561, Japan
    Nucleic Acids Res 36:W75-8. 2008
    ..The servers are located at http://software.ncrna.org, along with the information for the evaluation and downloading. This website is freely available to all users and there is no login requirement...
  23. ncbi PSSMTS: position specific scoring matrices on tree structures
    Kengo Sato
    Japan Biological Informatics Consortium, 2 45 Aomi, Koto ku, Tokyo 135 8073, Japan
    J Math Biol 56:201-14. 2008
    ..Experimental results show that PSSMs enable us to find individual RNA families efficiently, especially if we have biological knowledge such as sequence motifs...