  Microbial ecology serves as a foundation for a wide range of scientific and
biomedical studies. Rapidly-evolving high-throughput sequencing technology
enables the comprehensive search for microbial biomarkers using longitudinal
experiments. Such experiments consist of repeated biological observations from
each subject over time and are essential in accounting for the high
between-subject and within-subject variability.
  Unfortunately, many of the statistical tests based on parametric models rely
on correctly specifying temporal dependence structure which is unavailable in
most microbiome data.
  In this paper, we propose an extension of the nonparametric bootstrap method
that enables inference on these types longitudinal data. The proposed moving
block bootstrap (MBB) method accounts for within-subject dependency by using
overlapping blocks of repeated observations within each subject to draw valid
inferences based on approximately pivotal statistics. Our simulation studies
show an increase in power compared to merge-by-subject (MBS) strategies. We
also show that compared to tests that presume independent samples (PIS), our
proposed method reduces false microbial biomarker discovery rates.
  In this paper, we illustrated the MBB method using three different pregnancy
data and an oral microbiome data. We provide an open-source R package
https://github.com/PratheepaJ/bootLong to make our method accessible and the
study in this paper reproducible.
