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Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing
Nicholas A Bokulich1, Sathish Subramanian, Jeremiah J Faith
1Department of Viticulture and Enology, University of California, Davis, Davis, California, USA.
Nature Methods
|December 4, 2012
Summary
High-throughput sequencing data quality is crucial for microbial ecology studies. Read length and abundance are key to distinguishing accurate from erroneous reads, improving data interpretation.
Area of Science:
- Microbial Ecology
- Bioinformatics
- Genomics
Background:
- High-throughput sequencing significantly advanced microbial ecology research.
- Read quality is a major limitation for accurate microbial community analysis.
- Taxonomy assignment and alpha-diversity assessment are particularly affected by read quality issues.
Purpose of the Study:
- To identify key factors influencing read quality in Illumina sequencing.
- To develop guidelines for effective quality filtering of sequencing data.
- To improve the accuracy of microbial community assessments using Illumina data.
Main Methods:
- Analysis of read length and abundance to differentiate correct from erroneous sequencing reads.
- Evaluation of data from Illumina GAIIx, HiSeq, and MiSeq instruments.
- Development of user-defined quality-filtering strategies.
Main Results:
- High-quality read length and abundance were identified as primary discriminators of correct reads.
- Erroneous reads can be effectively filtered using these criteria.
- User-defined filtering strategies enhance the extraction of high-quality data.
Conclusions:
- Read length and abundance are critical metrics for assessing Illumina sequencing data quality.
- Implementing user-defined quality-filtering strategies optimizes microbial community analysis.
- Improved data quality leads to more reliable taxonomy assignment and diversity assessments.
