Development of a novel bioinformatics method to analyze big genome sequence data for efficient knowledge discovery

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330327
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Life / Health / Medical informatics
• Research Institution: Niigata University
• Principal Investigator: Abe Takashi 新潟大学, 自然科学系, 准教授 (30390628)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 一括学習型自己組織化マップ / 自己圧縮BLSOM / 連続塩基組成 / 連続アミノ酸組成 / メタゲノム / 系統推定

Outline of Final Research Achievements

As the result of extensive decoding of genome sequences, novel tools are needed for comprehensive analyses of available big sequence data. We previously developed a BLSOM, which can cluster genomic fragment sequences according to phylotype solely dependent on oligonucleotide composition. Howerver, a large-scale BLSOM needs a large computational resource.
We have developed Self-Compressing BLSOM (SC-BLSOM) for reduction of computation time, which allows us comprehensive analysis of big sequence data. The strategy of SC-BLSOM is to hierarchically construct BLSOMs according to data class such as phylotype. SC-BLSOM could be constructed faster than BLSOM and cluster the sequences according to phylotype with high accuracy. We have also developed a new method to predict protein function on the basis of similarity in oligonucleotide composition. The proteins could be related to function-known proteins. These methods are useful to analyze big sequence data for efficient knowledge discovery.

Free Research Field

生命・健康・医療情報学