K-mer Counting: Memory-efficient strategy, parallel computing and field of application for Bioinformatics

Ming Xiao, Jiakun Li, Song Hong, Yongtao Yang, Junhua Li, Jianxin Wang, Jian Yang, Wenbiao Ding, Le Zhang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Currently, k-mer counting is an important algorithm for bioinformatics research. This review lists the major application fields of k-mer counting in Bioinformatics at the beginning. Next, we introduce the commonly used memory-efficient strategy for k-mer counting tools, because the large amount of memory request is a bottleneck of k-mer counting tools. Next we illustrate the current parallel computing technologies for k-mer counting tool. Finally, we discuss the future study for k-mer counting.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2561-2567
Number of pages7
ISBN (Electronic)9781538654880
DOIs
Publication statusPublished - 21 Jan 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/TerritorySpain
CityMadrid
Period3/12/186/12/18

Keywords

  • Bioinformatics
  • genome sequence analysis
  • k-mer counting

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