HashSeq: a Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Data Sets.

ABSTRACT: 16S rRNA gene sequencing is a common and cost-effective technique for characterization of microbial communities. Recent bioinformatics methods enable high-resolution detection of sequence variants of only one nucleotide difference. In this study, we utilized a very fast HashMap-based approach to detect sequence variants in six publicly available 16S rRNA gene data sets. We then use the normal distribution combined with locally estimated scatterplot smoothing (LOESS) regression to estimate background error rates as a function of sequencing depth for individual clusters of sequences. This method is computationally efficient and produces inference that yields sets of variants that are conservative and well supported by reference databases. We argue that this approach to inference is fast, sim

SUBMITTER: Fouladi F 

PROVIDER: S-EPMC8577285 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature