Assembling full-length rRNA genes from short-read metagenomic sequence datasets using EMIRGE.

Area of Science:

• Microbial Ecology
• Bioinformatics
• Genomics

Background:

• High-throughput DNA sequencing has advanced microbial ecology.
• Short read lengths hinder the identification of small subunit ribosomal RNA (SSU rRNA) genes in metagenomic data.
• Interpreting microbial samples requires SSU rRNA gene sequences for comparison with existing databases.

Purpose of the Study:

• To present EMIRGE, specialized software for assembling SSU rRNA genes from various sequencing data types.
• To address the challenges of strain similarity and variable conservation within SSU rRNA genes during assembly.
• To provide practical guidance for optimizing EMIRGE's use in microbial community analysis.

Main Methods:

• Development of EMIRGE software for SSU rRNA gene assembly.
• Optimization of EMIRGE to handle strain similarity and sequence conservation variability.
• Application of EMIRGE to shotgun metagenomes, long PCR amplicons, and total-RNA transcriptomes.

Main Results:

• EMIRGE successfully assembles SSU rRNA genes from diverse sequencing datasets.
• The software is designed to manage the complexities of SSU rRNA gene assembly.
• Detailed discussion on EMIRGE's functionality and parameter optimization is provided.

Conclusions:

• EMIRGE is a valuable tool for microbial ecologists using high-throughput sequencing data.
• The software facilitates the interpretation of microbial community structure by enabling SSU rRNA gene identification.
• Effective use of EMIRGE requires understanding its parameters and data preprocessing techniques.