Efficient Mining Multi-Mers in a Variety of Biological Sequences

Jingsong Zhang; Jianmei Guo; Ming Zhang; Xiangtian Yu; Xiaoqing Yu; Weifeng Guo; Tao Zeng; Luonan Chen

doi:10.1109/TCBB.2018.2828313

Efficient Mining Multi-Mers in a Variety of Biological Sequences

Jingsong Zhang
, Jianmei Guo
, Ming Zhang
, Xiangtian Yu
, Xiaoqing Yu
, Weifeng Guo
, Tao Zeng^*
, Luonan Chen^*

^*Corresponding author for this work

CAS - Center for Excellence in Molecular Cell Science
Alibaba Group Holding Ltd.
Naval Medical University
Shanghai Institute of Technology
Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Counting the occurrence frequency of each kk-mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most kk-mer counting algorithms rely on a given kk to produce single-length kk-mers, which is inefficient for sequence analysis for different kk. Moreover, existing kk-mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of kk-mers in protein sequences can provide substantial biological insights in structure, function, and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine kk-mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNA, RNA, and protein sequences.

Original language	English
Article number	8341507
Pages (from-to)	949-958
Number of pages	10
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume	17
Issue number	3
DOIs	https://doi.org/10.1109/TCBB.2018.2828313
State	Published - 1 May 2020
Externally published	Yes

Keywords

Sequential pattern mining
and biological sequence analysis
inverted index
κ-mer counting
κ-mers of various lengths

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10.1109/TCBB.2018.2828313

Cite this

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title = "Efficient Mining Multi-Mers in a Variety of Biological Sequences",

abstract = "Counting the occurrence frequency of each kk-mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most kk-mer counting algorithms rely on a given kk to produce single-length kk-mers, which is inefficient for sequence analysis for different kk. Moreover, existing kk-mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of kk-mers in protein sequences can provide substantial biological insights in structure, function, and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine kk-mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNA, RNA, and protein sequences.",

keywords = "Sequential pattern mining, and biological sequence analysis, inverted index, κ-mer counting, κ-mers of various lengths",

author = "Jingsong Zhang and Jianmei Guo and Ming Zhang and Xiangtian Yu and Xiaoqing Yu and Weifeng Guo and Tao Zeng and Luonan Chen",

note = "Publisher Copyright: {\textcopyright} 2004-2012 IEEE.",

year = "2020",

month = may,

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doi = "10.1109/TCBB.2018.2828313",

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AU - Zhang, Jingsong

AU - Guo, Jianmei

AU - Zhang, Ming

AU - Yu, Xiangtian

AU - Yu, Xiaoqing

AU - Guo, Weifeng

AU - Zeng, Tao

AU - Chen, Luonan

PY - 2020/5/1

Y1 - 2020/5/1

N2 - Counting the occurrence frequency of each kk-mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most kk-mer counting algorithms rely on a given kk to produce single-length kk-mers, which is inefficient for sequence analysis for different kk. Moreover, existing kk-mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of kk-mers in protein sequences can provide substantial biological insights in structure, function, and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine kk-mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNA, RNA, and protein sequences.

AB - Counting the occurrence frequency of each kk-mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most kk-mer counting algorithms rely on a given kk to produce single-length kk-mers, which is inefficient for sequence analysis for different kk. Moreover, existing kk-mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of kk-mers in protein sequences can provide substantial biological insights in structure, function, and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine kk-mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNA, RNA, and protein sequences.

KW - Sequential pattern mining

KW - and biological sequence analysis

KW - inverted index

KW - κ-mer counting

KW - κ-mers of various lengths

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DO - 10.1109/TCBB.2018.2828313

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SN - 1545-5963

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JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics

JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics

IS - 3

M1 - 8341507

ER -

Efficient Mining Multi-Mers in a Variety of Biological Sequences

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Keywords

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