The University of Tokyo · Biochemistry, Genetics and Molecular Biology
Professor Kiyoshi Asai's research lab specializes in computational biology and bioinformatics, focusing on the development of advanced algorithms and models for analyzing biological sequences. The lab pioneers the application of statistical models, such as hidden Markov models (HMMs), to predict protein secondary structures and contributes to the improvement of long-read sequencing technologies through innovative simulation tools. Current research directions include enhancing read simulation with realistic error models and multi-pass sequencing emulation, supporting the advancement of next-generation sequencing data analysis. The lab plays a key role in bridging computational methods with high-throughput biological data to drive discoveries in genomics and structural biology.
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
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