A read-filtering algorithm for high-throughput marker-gene studies that greatly improves OTU accuracy

TL;DR

This paper introduces a novel read-filtering algorithm that uses error-probability calculations to improve OTU accuracy in high-throughput marker-gene studies, leading to more faithful microbial diversity estimates.

Contribution

The paper presents a new, sensitive filtering method that outperforms existing approaches by retaining more reads and producing more accurate OTUs.

Findings

Retained more reads than previous methods

Produced fewer, more accurate OTUs

Enhanced representation of microbial diversity

Abstract

Adequate read filtering is critical when processing high-throughput data in marker-gene-based studies. Sequencing errors can cause the mis-clustering of otherwise similar reads, artificially increasing the number of retrieved Operational Taxonomic Units (OTUs) and therefore leading to the overestimation of microbial diversity. Sequencing errors will also result in OTUs that are not accurate reconstructions of the original biological sequences. Herein we present a novel and sensitive sequence filtering algorithm that minimizes both problems by calculating the exact error-probability distribution of a sequence from its quality scores. In order to validate our method, we quality-filtered thirty-seven publicly available datasets obtained by sequencing mock and environmental microbial communities with the Roche 454, Illumina MiSeq and IonTorrent PGM platforms, and compared our results to those obtained with previous approaches such as the ones included in mothur, QIIME and UPARSE. Our algorithm retained substantially more reads than its predecessors, while resulting in fewer and more accurate OTUs. This improved sensitiveness produced more faithful representations, both quantitatively and qualitatively, of the true microbial diversity present in the studied samples. Furthermore, the method introduced in this work is computationally inexpensive and can be readily applied in conjunction with any existent analysis pipeline.