# Assessing microbiome engraftment extent following fecal microbiota transplant with q2-fmt

## TL;DR

This paper introduces q2-fmt, a new software tool for analyzing how well donor microbes transfer to recipients during fecal microbiota transplants.

## Contribution

The novel contribution is a QIIME 2 plugin that integrates multiple methods for assessing microbiome engraftment after FMT.

## Key findings

- q2-fmt includes methods for assessing engraftment through community coalescence, indicator features, and resilience.
- The plugin is integrated into QIIME 2, enabling reproducible and accessible microbiome analysis workflows.
- The tool is supported by documentation and real-world data examples for practical use.

## Abstract

We present q2-fmt, a QIIME 2 plugin that provides diverse methods for assessing the extent of microbiome engraftment following fecal microbiota transplant. The methods implemented here were informed by a recent literature review on approaches for assessing FMT engraftment, and cover aspects of engraftment including Community Coalescence, Indicator Features, and Resilience. q2-fmt is free for all use, and detailed documentation illustrating worked examples on a real-world data set are provided in the project’s documentation.

Fecal Microbiota Transplants (FMTs) are a treatment for recurrent Clostridium difficile infections and are being investigated as treatments for many other conditions. To determine if an FMT was successful, we need to assess how the microbiome of a donor is transferred into a recipient following FMT. q2-fmt is software for quantifying this transfer with diverse methods incorporated in one of the most widely used microbiome analysis platforms, QIIME 2. This allows q2-fmt users to investigate their FMT data in the microbiome platform that they are already using for their microbiome analysis and get all the benefits of working in QIIME 2, including full analysis reproducibility through retrospective provenance tracking, access to the same tools through interfaces targeting users with differing computational backgrounds, and integration with other widely used microbiome analysis tools.