Species
delimitation
is
the
process
of
distinguishing
between
populations
same
species
and
distinct
a
particular
group
organisms.
Various
methods
exist
for
inferring
limits,
with
most
them
being
rooted
in
Coalescent
Theory.
Their
primary
goal
to
identify
independently
evolving
lineages
that
should
represent
separate
species.
models
have
improved
by
enabling
explicit
testing
hypotheses
regarding
evolutionary
independence
among
lineages.
However,
they
some
limitations,
especially
complex
scenarios,
large
datasets,
varying
genetic
data
types.
In
this
context,
machine
learning
(ML)
can
be
considered
as
promising
analytical
tool,
clearly
provides
an
effective
way
explore
dataset
structures
when
species-level
divergences
are
hypothesised.
review,
we
examine
use
ML
provide
overview
critical
appraisal
existing
workflows.
We
also
simple
explanations
on
how
main
types
approaches
operate,
which
help
researchers
students
interested
field.
While
current
designed
infer
limits
analytically
powerful,
present
specific
limitations
not
definitive
alternatives
traditional
coalescent
delimitation.
For
instance,
there
clear
utilisation
simulated
data,
supervised
deep
approaches,
type
representation
used
each
approach.
then
discuss
strengths
weaknesses
pipelines,
propose
best
practices
delimitation,
offer
insights
into
potential
future
applications.
Generative
adversarial
networks
domain
adaptation
techniques,
could
partially
address
misspecification
issue
related
simulating
data.
Besides,
integrating
hypothesis
process,
alongside
available
coalescent-based
methods,
enable
more
comprehensive
exploration
parameters,
improving
accuracy
biological
interpretability
analyses.
Additionally,
suggest
guidelines
enhancing
accessibility,
effectiveness,
objectivity
processes,
aiming
transformative
perspective
subject.
The Plant Journal,
Год журнала:
2025,
Номер
122(4)
Опубликована: Май 1, 2025
SUMMARY
Licorice
is
a
popular
herb
around
the
world,
with
Glycyrrhiza
uralensis
,
inflata
and
glabra
being
three
most
common
medicinal
species.
Glycyrrhizin,
important
bioactive
compound,
determines
quality
of
licorices.
Besides,
some
characteristic
flavonoids,
such
as
licochalcone
A
(LCA)
from
G.
glabridin
are
emerging
expensive
raw
materials
in
fields
medicine
cosmetics.
We
obtained
high‐quality
genomic
sequence
data
these
licorices
sizes
425,
447,
423
Mb,
respectively.
By
genome
assembly‐assisted
comparison,
collinear
relationships
structural
variations
(SVs)
among
species
were
identified.
These
presence/absence
(PAV)
genes
mainly
enriched
secondary
metabolism
pathways.
With
assembled
genomes
transcriptomes,
we
constructed
regulatory
network
glycyrrhizin
identified
GibHLH9,
GibHLH53,
GibHLH174
key
transcription
factors
that
promote
by
transactivating
expression
GiCSyGT
GiUGT73P12
In
addition,
proposed
LCA
biosynthesis
pathways
analyzed
all
genomes.
Then
function
GiOMT17
was
confirmed
vivo
vitro
.
As
consequence,
appearance
unique
differential
commonly
existed
explains
why
licorice
synthesize
flavonoids
but
only
specific
accumulate
them
to
certain
amount.
Our
findings
provide
for
future
research
supply
valuable
gene
resources
synthetic
biology
molecular
breeding
high‐yield
active
ingredients.
International Journal of Molecular Sciences,
Год журнала:
2022,
Номер
24(1), С. 121 - 121
Опубликована: Дек. 21, 2022
Sucrose
non-fermenting-1-related
protein
kinase-1
(SnRK1)
and
its
scaffolding
proteins,
FCS-like
zinc
finger
proteins
(FLZs),
are
well
conserved
in
land
plants
involved
various
processes
of
plant
growth
stress
responses.
Glycyrrhiza
inflata
Bat.
is
a
widely
used
licorice
species
with
strong
abiotic
resistance,
which
terpenoids
flavonoids
the
major
bioactive
components.
Here,
we
identified
2
SnRK1
catalytic
α
subunit
encoding
genes
(GiSnRK1α1
GiSnRK1α2)
21
FLZ
G.
inflata.
Polygenetic
analysis
showed
that
GiFLZs
could
be
divided
into
three
groups.
A
total
10
representative
GiFLZ
interact
GiSnRK1α1,
they
display
overlapped
subcellular
localization
(mainly
nucleus
cytoplasm)
when
transiently
expressed
Nicotiana
benthamiana
leaf
cells.
Coinciding
existence
phytohormone-responsive
stress-responsive
cis-regulatory
elements
GiSnRK1α
gene
promoters,
actively
responsive
to
methyl
jasmonic
acid
(MeJA)
abscisic
(ABA)
treatments,
several
GiSnRK1α1
regulated
by
drought
saline-alkaline
stresses.
Interestingly,
20
(except
for
GiFLZ2)
show
higher
expression
roots
than
leaves.
These
data
provide
comprehensive
information
on
future
functional
characterization.
Mitochondrial DNA Part B,
Год журнала:
2024,
Номер
9(3), С. 385 - 389
Опубликована: Март 3, 2024
Alismataceae
is
one
of
the
early
diverged
families
monocotyledonous
plants.
We
report
complete
chloroplast
genomes
three
Alisma
species,
including
orientale
(Sam.)
Juzep.
1934,
A.
subcordatum
Raf.
1908,
and
triviale
Pursh
1813,
which
a
traditional
Chinese
medical
plant
used
widely
to
treat
diuretics,
diabetes,
hepatitis,
inflammation.
sequenced
with
Illumina
Nova-Seq
6000
platform
using
herbarium
collections.
The
orientale,
are
159,861
bp,
160,180
159,727
bp
in
length,
respectively.
each
contain
113
genes,
four
rRNAs,
30
tRNAs
79
protein-coding
average
GC
content
36.0%.
Based
on
whole
19
species
close
allies,
medicinally
important
was
found
be
closely
related
another
medicinal
plantago-aquatica
L.
1753
phylogenetic
analysis.
genus
supported
monophyletic.
Species
delimitation
is
the
process
of
distinguishing
between
populations
same
species
and
distinct
a
particular
group
organisms.
Various
methods
exist
for
inferring
limits,
with
most
them
being
rooted
in
Coalescent
Theory.
Their
primary
goal
to
identify
independently
evolving
lineages
that
should
represent
separate
species.
models
have
improved
by
enabling
explicit
testing
hypotheses
regarding
evolutionary
independence
among
lineages.
However,
they
some
limitations,
especially
complex
scenarios,
large
datasets,
varying
genetic
data
types.
In
this
context,
machine
learning
(ML)
can
be
considered
as
promising
analytical
tool,
clearly
provides
an
effective
way
explore
dataset
structures
when
species-level
divergences
are
hypothesised.
review,
we
examine
use
ML
provide
overview
critical
appraisal
existing
workflows.
We
also
simple
explanations
on
how
main
types
approaches
operate,
which
help
researchers
students
interested
field.
While
current
designed
infer
limits
analytically
powerful,
present
specific
limitations
not
definitive
alternatives
traditional
coalescent
delimitation.
For
instance,
there
clear
utilisation
simulated
data,
supervised
deep
approaches,
type
representation
used
each
approach.
then
discuss
strengths
weaknesses
pipelines,
propose
best
practices
delimitation,
offer
insights
into
potential
future
applications.
Generative
adversarial
networks
domain
adaptation
techniques,
could
partially
address
misspecification
issue
related
simulating
data.
Besides,
integrating
hypothesis
process,
alongside
available
coalescent-based
methods,
enable
more
comprehensive
exploration
parameters,
improving
accuracy
biological
interpretability
analyses.
Additionally,
suggest
guidelines
enhancing
accessibility,
effectiveness,
objectivity
processes,
aiming
transformative
perspective
subject.
