東京大学 · 生化学・遺伝学・分子生物学
阿部清志教授の研究室では、ゲノム情報の解析を柱に、長距離シークエンシング技術の発展に伴い生じる高次元なゲノムデータの解析に特化したアルゴリズム開発を行っています。特に、PacBioやOxford Nanoporeの長距離シークエンサーからのデータを的確にシミュレート・解析するためのシミュレータPBSIM3の開発を通じて、エラーモデルやマルチパスシークエンシングの再現を実現しています。また、タンパク質の二次構造予測においても、隠れマルコフモデル（HMM）を応用した新しい解析手法の構築を進め、構造予測の精度向上に貢献しています。
Figures are computed from collected data and may differ slightly.
The purpose of this paper is to introduce a new method for analyzing the amino acid sequences of proteins using the hidden Markov model (HMM), which is a type of stochastic model. Secondary structures such as helix, sheet and turn are learned by HMMs, and these HMMs are applied to new sequences whose structures are unknown. The output probabilities from the HMMs are used to predict the secondary structures of the sequences. The authors tested this prediction system on approximately 100 sequences
Long-read sequencers, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencers, have improved their read length and accuracy, thereby opening up unprecedented research. Many tools and algorithms have been developed to analyze long reads, and rapid progress in PacBio and ONT has further accelerated their development. Together with the development of high-throughput sequencing technologies and their analysis tools, many read simulators have been developed and effectiv
Open papers in the app to read, cite, and organize with AI.
