Evidence map›Paper›PMID 32753028›Full record

ArticleBMC bioinformatics2020

smsMap: mapping single molecule sequencing reads by locating the alignment starting positions.

Ze-Gang Wei, Shao-Wu Zhang, Fei Liu

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Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ze-Gang WeiKey Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
Shao-Wu ZhangKey Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China. zhangsw@nwpu.edu.cn.ORCID http://orcid.org/0000-0003-1305-7447
Fei LiuInstitute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China.

Funding

National Natural Science Foundation of China 61473232National Natural Science Foundation of China 61873202National Natural Science Foundation of China 91430111
6 · The paper itself

Abstract

backgroundSingle Molecule Sequencing (SMS) technology can produce longer reads with higher sequencing error rate. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. Most existing mapping tools generally adopt the traditional seed-and-extend strategy, and the candidate aligned regions for each query read are selected either by counting the number of matched seeds or chaining a group of seeds. However, for all the existing mapping tools, the coverage ratio of the alignment region to the query read is lower, and the read alignment quality and efficiency need to be improved. Here, we introduce smsMap, a novel mapping tool that is specifically designed to map the long reads of SMS to a reference genome.

resultssmsMap was evaluated with other existing seven SMS mapping tools (e.g., BLASR, minimap2, and BWA-MEM) on both simulated and real-life SMS datasets. The experimental results show that smsMap can efficiently achieve higher aligned read coverage ratio and has higher sensitivity that can align more sequences and bases to the reference genome. Additionally, smsMap is more robust to sequencing errors.

conclusionssmsMap is computationally efficient to align SMS reads, especially for the larger size of the reference genome (e.g., H. sapiens genome with over 3 billion base pairs). The source code of smsMap can be freely downloaded from https://github.com/NWPU-903PR/smsMap .

Indexed as

Sequence AlignmentSoftwareAlgorithmsComputer SimulationDatabases, GeneticEscherichia coliHumansSequence Analysis, DNATime Factors

Identifiers

PMID32753028
PMCPMC7430848

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.