Frontiers in Microbiology,
Год журнала:
2020,
Номер
11
Опубликована: Окт. 23, 2020
One
of
the
major
methods
to
identify
microbial
community
composition,
unravel
population
dynamics,
and
explore
diversity
in
environmental
samples
is
high-throughput
DNA-
or
RNA-based
16S
rRNA
(gene)
amplicon
sequencing
combination
with
bioinformatics
analyses.
However,
focusing
on
from
contrasting
habitats,
it
was
not
systematically
evaluated
i.)
which
analysis
provide
results
that
reflect
reality
most
accurately,
ii.)
how
interpretations
studies
are
biased
by
different
iii.)
if
optimal
workflow
can
be
implemented
an
easy-to-use
pipeline.
Here,
we
compared
performance
tools
(i.e.
Mothur,
QIIME1,
QIIME2,
MEGAN)
using
three
mock
datasets
known
composition
differed
quality,
species
number
abundance
distribution
even
uneven),
phylogenetic
closely
related
well-separated
sequences).
Our
showed
QIIME2
outcompeted
all
other
investigated
sequence
recovery
(>10
times
fewer
false
positives),
taxonomic
assignments
(>22%
better
F-score)
estimates
(>5%
assessment),
suggesting
this
approach
able
situ
accurately.
Further
24
obtained
four
terrestrial
freshwater
sites
revealed
dramatic
differences
resulting
for
pipelines
at
genus
level.
For
instance,
river
water
Sphaerotilus
only
reported
when
QIIME1
(8%
abundance)
Agitococcus
(2%
3%
abundance,
respectively),
but
both
genera
remained
undetected
analyzed
Mothur
MEGAN.
Since
these
abundant
taxa
probably
have
implications
important
biogeochemical
cycles
(e.g.
nitrate
sulfate
reduction)
sites,
their
detection
semi-quantitative
enumeration
crucial
valid
interpretations.
A
high-performance
computing
conformant
constructed
allow
FAIR
(Findable,
Accessible,
Interoperable,
Re-usable)
starting
raw
files,
identified
our
study.
presented
should
considered
future
studies,
thereby
facilitating
data
substantially,
while
maximizing
reliability
confidence
analysis.
The
field
of
microbiome
research
has
evolved
rapidly
over
the
past
few
decades
and
become
a
topic
great
scientific
public
interest.
As
result
this
rapid
growth
in
interest
covering
different
fields,
we
are
lacking
clear
commonly
agreed
definition
term
"microbiome."
Moreover,
consensus
on
best
practices
is
missing.
Recently,
panel
international
experts
discussed
current
gaps
frame
European-funded
MicrobiomeSupport
project.
meeting
brought
together
about
40
leaders
from
diverse
areas,
while
more
than
hundred
all
world
took
part
an
online
survey
accompanying
workshop.
This
article
excerpts
outcomes
workshop
corresponding
embedded
short
historical
introduction
future
outlook.
We
propose
based
compact,
clear,
comprehensive
description
provided
by
Whipps
et
al.
1988,
amended
with
set
novel
recommendations
considering
latest
technological
developments
findings.
clearly
separate
terms
microbiota
provide
discussion
composition
microbiota,
heterogeneity
dynamics
microbiomes
time
space,
stability
resilience
microbial
networks,
core
microbiomes,
functionally
relevant
keystone
species
as
well
co-evolutionary
principles
microbe-host
inter-species
interactions
within
microbiome.
These
broad
definitions
suggested
unifying
concepts
will
help
to
improve
standardization
studies
future,
could
be
starting
point
for
integrated
assessment
data
resulting
transfer
knowledge
basic
science
into
practice.
Furthermore,
standards
important
solving
new
challenges
associated
anthropogenic-driven
changes
planetary
health,
which
understanding
might
play
key
role.
Video
Abstract.
Journal of Advanced Research,
Год журнала:
2019,
Номер
19, С. 29 - 37
Опубликована: Март 20, 2019
Plants
have
evolved
with
a
plethora
of
microorganisms
having
important
roles
for
plant
growth
and
health.
A
considerable
amount
information
is
now
available
on
the
structure
dynamics
microbiota
as
well
functional
capacities
isolated
community
members.
Due
to
interesting
potential
due
current
challenges
in
crop
production
there
an
urgent
need
bring
microbial
innovations
into
practice.
Different
approaches
microbiome
improvement
exist.
On
one
hand
strains
or
strain
combinations
can
be
applied,
however,
field
success
often
variable
urgently
required.
Smart,
knowledge-driven
selection
needed
use
suitable
delivery
formulations.
other
hand,
farming
practices
genotype
influence
thus
functioning.
Therefore,
appropriate
breeding
leading
improved
plant-microbiome
interactions
are
avenues
increase
benefit
microbiota.
In
conclusion,
different
making
new
generation
inoculants
application
microbiome-based
agro-management
lines
could
lead
better
microbiome.
This
paper
reviews
importance
functionalities
bacterial
discusses
concepts
regard
plant-associated
bacteria.
The ISME Journal,
Год журнала:
2018,
Номер
12(9), С. 2263 - 2277
Опубликована: Июнь 13, 2018
Abstract
Plankton
communities
normally
consist
of
few
abundant
and
many
rare
species,
yet
little
is
known
about
the
ecological
role
planktonic
eukaryotes.
Here
we
used
a
18S
ribosomal
DNA
sequencing
approach
to
investigate
dynamics
eukaryotes,
explore
co-occurrence
patterns
eukaryotic
plankton
in
subtropical
reservoir
following
cyanobacterial
bloom
event.
Our
results
showed
that
event
significantly
altered
community
composition
diversity
without
affecting
plankton.
The
similarities
both
subcommunities
declined
with
increase
time-lag,
but
stronger
temporal
turnover
was
observed
taxa.
Further,
species
explained
higher
percentage
variation
than
richness.
Both
deterministic
stochastic
processes
influenced
assembly,
pattern
(e.g.,
drift)
particularly
pronounced
for
Co-occurrence
network
analysis
revealed
keystone
taxa
mainly
belonged
which
may
play
fundamental
roles
persistence.
Importantly,
covariations
between
non-rare
were
predominantly
positive,
implying
multispecies
cooperation
might
contribute
stability
resilience
microbial
community.
Overall,
these
findings
expand
current
understanding
mechanisms
interactions
underlying
changing
aquatic
ecosystems.
FEMS Microbiology Reviews,
Год журнала:
2018,
Номер
42(6), С. 761 - 780
Опубликована: Июль 25, 2018
Microbial
networks
are
an
increasingly
popular
tool
to
investigate
microbial
community
structure,
as
they
integrate
multiple
types
of
information
and
may
represent
systems-level
behaviour.
Interpreting
these
is
not
straightforward,
the
biological
implications
network
properties
unclear.
Analysis
allows
researchers
predict
hub
species
interactions.
Additionally,
such
analyses
can
help
identify
alternative
states
niches.
Here,
we
review
factors
that
result
in
spurious
predictions
address
emergent
be
meaningful
context
microbiome.
We
also
give
overview
studies
analyse
new
hypotheses.
Moreover,
show
a
simulation
how
affected
by
choice
environmental
factors.
For
example,
consistent
across
tools,
heterogeneity
induces
modularity.
highlight
need
for
robust
inference
suggest
strategies
infer
more
reliably.
