论文库首页  论文库
 
论文编号:
论文题目: MIPE: A metagenome-based community structure explorer and SSU primer evaluation tool
英文论文题目: MIPE: A metagenome-based community structure explorer and SSU primer evaluation tool
第一作者: Zou, B; Li, JF; Zhou, Q; Quan, ZX
英文第一作者: Zou, B; Li, JF; Zhou, Q; Quan, ZX
联系作者: Quan, ZX (reprint author), Fudan Univ, Sch Life Sci, Dept Microbiol & Microbial Engn, Shanghai, Peoples R China.
英文联系作者: Quan, ZX (reprint author), Fudan Univ, Sch Life Sci, Dept Microbiol & Microbial Engn, Shanghai, Peoples R China.
外单位作者单位:
英文外单位作者单位:
发表年度: 2017
卷: 12
期: 3
页码: e0174609
摘要: An understanding of microbial community structure is an important issue in the field of molecular ecology. The traditional molecular method involves amplification of small subunit ribosomal RNA (SSU rRNA) genes by polymerase chain reaction (PCR). However, PCR-based amplicon approaches are affected by primer bias and chimeras. With the development of high-throughput sequencing technology, unbiased SSU rRNA gene sequences can be mined from shotgun sequencing-based metagenomic or metatranscriptomic datasets to obtain a reflection of the microbial community structure in specific types of environment and to evaluate SSU primers. However, the use of short reads obtained through next-generation sequencing for primer evaluation has not been well resolved. The software MIPE (MIcrobiota metagenome Primer Explorer) was developed to adapt numerous short reads from metagenomes and metatranscriptomes. Using metagenomic or metatranscriptomic datasets as input, MIPE extracts and aligns rRNA to reveal detailed information on microbial composition and evaluate SSU rRNA primers. A mock dataset, a real Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) test dataset, two PrimerProspector test datasets and a real metatranscriptomic dataset were used to validate MIPE. The software calls Mothur (v1.33.3) and the SILVA database (v119) for the alignment and classification of rRNA genes from a metagenome or metatranscriptome. MIPE can effectively extract shotgun rRNA reads from a metagenome or metatranscriptome and is capable of classifying these sequences and exhibiting sensitivity to different SSU rRNA PCR primers. Therefore, MIPE can be used to guide primer design for specific environmental samples.
英文摘要: An understanding of microbial community structure is an important issue in the field of molecular ecology. The traditional molecular method involves amplification of small subunit ribosomal RNA (SSU rRNA) genes by polymerase chain reaction (PCR). However, PCR-based amplicon approaches are affected by primer bias and chimeras. With the development of high-throughput sequencing technology, unbiased SSU rRNA gene sequences can be mined from shotgun sequencing-based metagenomic or metatranscriptomic datasets to obtain a reflection of the microbial community structure in specific types of environment and to evaluate SSU primers. However, the use of short reads obtained through next-generation sequencing for primer evaluation has not been well resolved. The software MIPE (MIcrobiota metagenome Primer Explorer) was developed to adapt numerous short reads from metagenomes and metatranscriptomes. Using metagenomic or metatranscriptomic datasets as input, MIPE extracts and aligns rRNA to reveal detailed information on microbial composition and evaluate SSU rRNA primers. A mock dataset, a real Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) test dataset, two PrimerProspector test datasets and a real metatranscriptomic dataset were used to validate MIPE. The software calls Mothur (v1.33.3) and the SILVA database (v119) for the alignment and classification of rRNA genes from a metagenome or metatranscriptome. MIPE can effectively extract shotgun rRNA reads from a metagenome or metatranscriptome and is capable of classifying these sequences and exhibiting sensitivity to different SSU rRNA PCR primers. Therefore, MIPE can be used to guide primer design for specific environmental samples.
刊物名称: PLOS ONE
英文刊物名称: PLOS ONE
论文全文:
英文论文全文:
全文链接:
其它备注:
英文其它备注:
学科: Multidisciplinary Sciences
英文学科: Multidisciplinary Sciences
影响因子: 2.806
第一作者所在部门:
英文第一作者所在部门:
论文出处:
英文论文出处:
论文类别: Article
英文论文类别: Article
参与作者:
英文参与作者:
 
2014 中国科学院上海生命科学研究院 版权所有