Parallel implementation of motif-based clustering for HT-SELEX dataset

Takayoshi Ono, Shintaro Kato, Koichi Ito, Hirotaka Minagawa, Katsunori Horii, Ikuo Shiratori, Iwao Waga, Takafumi Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A clustering method for high-throughput sequencing with SELEX pools (HT-SELEX) is crucial for selecting different types of aptamer candidates. The fast and accurate clustering method is indispensable for an enormous sequence data produced by HT-SELSEX. We have already developed a fast motif-based clustering (FMBC) method for HT-SELEX data implemented by R language. FMBC exhibited high accuracy of sequence clustering compared with conventional methods, while the processing time of FMBC is longer than AptaCluster. This paper proposes the parallel implementation of FMBC using Python with multi-threading to improve the performance of FMBC. Experimental evaluation using the NCBI SRA data of SRR3279661 from BioProject PRJNA315881 demonstrated that parallel FMBC exhibited higher accuracy of clustering and shorter processing time than conventional methods.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages50-55
Number of pages6
ISBN (Electronic)9781728146171
DOIs
Publication statusPublished - 2019 Oct
Event19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 - Athens, Greece
Duration: 2019 Oct 282019 Oct 30

Publication series

NameProceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019

Conference

Conference19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
CountryGreece
CityAthens
Period19/10/2819/10/30

Keywords

  • Aptamer
  • Clustering
  • HT-SELEX
  • Parallel implementation
  • SELEX

ASJC Scopus subject areas

  • Information Systems
  • Biomedical Engineering
  • Health Informatics

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