De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation.

ABSTRACT:

Introduction

Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties.

Methods

We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequen

SUBMITTER: Rauer L 

PROVIDER: S-EPMC11792448 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature