Analytical Chemistry,
Journal Year:
2016,
Volume and Issue:
88(22), P. 11084 - 11091
Published: Oct. 21, 2016
The
rapid
development
of
metabolomics
has
significantly
advanced
health
and
disease
related
research.
However,
metabolite
identification
remains
a
major
analytical
challenge
for
untargeted
metabolomics.
While
the
use
collision
cross-section
(CCS)
values
obtained
in
ion
mobility-mass
spectrometry
(IM-MS)
effectively
increases
confidence
metabolites,
it
is
restricted
by
limited
number
available
CCS
metabolites.
Here,
we
demonstrated
machine-learning
algorithm
called
support
vector
regression
(SVR)
to
develop
prediction
method
that
utilized
14
common
molecular
descriptors
predict
In
this
work,
first
experimentally
measured
(ΩN2)
∼400
metabolites
nitrogen
buffer
gas
used
these
as
training
data
optimize
method.
high
precision
was
externally
validated
using
an
independent
set
with
median
relative
error
(MRE)
∼3%,
better
than
conventional
theoretical
calculation.
Using
SVR
based
method,
large-scale
predicted
database
generated
35
203
Human
Metabolome
Database
(HMDB).
For
each
metabolite,
five
different
adducts
positive
negative
modes
were
predicted,
accounting
176
015
total.
Finally,
improved
accuracy
real
biological
samples.
Conclusively,
our
results
proved
can
accurately
from
improve
efficiency
database,
namely,
MetCCS,
freely
on
Internet.
Journal of the American Society for Mass Spectrometry,
Journal Year:
2016,
Volume and Issue:
27(12), P. 1897 - 1905
Published: Sept. 13, 2016
Metabolites
are
building
blocks
of
cellular
function.
These
species
involved
in
enzyme-catalyzed
chemical
reactions
and
essential
for
Upstream
biological
disruptions
result
a
series
metabolomic
changes
and,
as
such,
the
metabolome
holds
wealth
information
that
is
thought
to
be
most
predictive
phenotype.
Uncovering
this
knowledge
work
progress.
The
field
metabolomics
still
maturing;
community
has
leveraged
proteomics
experience
when
applicable
developed
range
sample
preparation
instrument
methodology
along
with
myriad
data
processing
analysis
approaches.
Research
focuses
have
now
shifted
toward
fundamental
understanding
biology
responsible
changes.
There
several
types
experiments
including
both
targeted
untargeted
analyses.
While
untargeted,
hypothesis
generating
workflows
exhibit
many
valuable
attributes,
challenges
inherent
approach
remain.
This
Critical
Insight
comments
on
these
challenges,
focusing
identification
process
LC-MS-based
studies-specifically
mammalian
systems.
Biological
interpretation
hinges
ability
accurately
identify
metabolites.
confidence
associated
identifications
often
overlooked
reviewed,
opportunities
advancing
described.
Graphical
Abstract
ᅟ.
Metabolites,
Journal Year:
2018,
Volume and Issue:
8(2), P. 31 - 31
Published: May 10, 2018
The
annotation
of
small
molecules
remains
a
major
challenge
in
untargeted
mass
spectrometry-based
metabolomics.
We
here
critically
discuss
structured
elucidation
approaches
and
software
that
are
designed
to
help
during
the
unknown
compounds.
Only
by
elucidating
metabolites
first
is
it
possible
biologically
interpret
complex
systems,
map
compounds
pathways
create
reliable
predictive
metabolic
models
for
translational
clinical
research.
These
strategies
include
construction
quality
tandem
spectral
databases
such
as
coalition
MassBank
repositories
investigations
MS/MS
matching
confidence.
present
silico
fragmentation
tools
MS-FINDER,
CFM-ID,
MetFrag,
ChemDistiller
CSI:FingerID
can
annotate
from
existing
structure
have
been
used
CASMI
(critical
assessment
molecule
identification)
contests.
Furthermore,
use
retention
time
liquid
chromatography
utility
collision
cross-section
modelling
ion
mobility
experiments
covered.
Workflows
published
examples
successfully
annotated
included.
Mass Spectrometry Reviews,
Journal Year:
2017,
Volume and Issue:
37(4), P. 513 - 532
Published: April 24, 2017
Tandem
mass
spectral
library
search
(MS/MS)
is
the
fastest
way
to
correctly
annotate
MS/MS
spectra
from
screening
small
molecules
in
fields
such
as
environmental
analysis,
drug
screening,
lipid
and
metabolomics.
The
confidence
MS/MS‐based
annotation
of
chemical
structures
impacted
by
instrumental
settings
requirements,
data
acquisition
modes
including
data‐dependent
data‐independent
methods,
scoring
algorithms,
well
post‐curation
steps.
We
critically
discuss
parameters
that
influence
results,
accuracy,
precursor
ion
isolation
width,
intensity
thresholds,
centroiding
speed.
A
range
publicly
commercially
available
databases
NIST,
MassBank,
MoNA,
LipidBlast,
Wiley
MSforID,
METLIN
are
surveyed.
In
addition,
software
tools
NIST
MS
Search,
MS‐DIAL,
Mass
Frontier,
SmileMS,
Mass++,
XCMS
2
perform
fast
discussed.
algorithms
challenges
during
compound
reviewed.
Advanced
methods
silico
generation
tandem
using
quantum
chemistry
machine
learning
covered.
Community
efforts
for
curation
sharing
will
allow
faster
distribution
scientific
discoveries
Journal of Lipid Research,
Journal Year:
2017,
Volume and Issue:
58(12), P. 2275 - 2288
Published: Oct. 7, 2017
As
the
lipidomics
field
continues
to
advance,
self-evaluation
within
community
is
critical.
Here,
we
performed
an
interlaboratory
comparison
exercise
for
using
Standard
Reference
Material
(SRM)
1950-Metabolites
in
Frozen
Human
Plasma,
a
commercially
available
reference
material.
The
study
comprised
31
diverse
laboratories,
with
each
laboratory
different
workflow.
A
total
of
1,527
unique
lipids
were
measured
across
all
laboratories
and
consensus
location
estimates
associated
uncertainties
determined
339
these
at
sum
composition
level
by
five
or
more
participating
laboratories.
These
evaluated
detected
SRM
1950
serve
as
community-wide
benchmarks
intra-
quality
control
method
validation.
analyses
nonstandardized
laboratory-independent
workflows.
locations
also
compared
previous
examination
LIPID
MAPS
consortium.
While
central
theme
was
provide
values
help
harmonize
lipids,
lipid
mediators,
precursor
measurements
community,
it
initiated
stimulate
discussion
regarding
areas
need
improvement.
PROTEOMICS,
Journal Year:
2020,
Volume and Issue:
20(17-18)
Published: April 10, 2020
This
review
provides
a
brief
overview
of
the
development
data-independent
acquisition
(DIA)
mass
spectrometry-based
proteomics
and
selected
DIA
data
analysis
tools.
Various
schemes
for
are
summarized
first
including
Shotgun-CID,
DIA,
MSE
,
PAcIFIC,
AIF,
SWATH,
MSX,
SONAR,
WiSIM,
BoxCar,
Scanning
diaPASEF,
PulseDIA,
as
well
spectrometers
enabling
these
methods.
Next,
software
tools
classified
into
three
groups:
library-based
tools,
library-free
statistical
validation
The
approaches
reviewed
generating
spectral
libraries
six
which
tested
by
authors,
OpenSWATH,
Spectronaut,
Skyline,
PeakView,
DIA-NN,
EncyclopeDIA.
An
increasing
number
developed
DIA-Umpire,
Group-DIA,
PECAN,
PEAKS,
facilitate
identification
novel
proteoforms.
authors
share
their
user
experience
when
to
use
DIA-MS,
several
Finally,
state
art
spectrometry
authors'
views
future
directions
summarized.
Scientific Reports,
Journal Year:
2017,
Volume and Issue:
7(1)
Published: Oct. 31, 2017
Abstract
Metabolomics
answers
a
fundamental
question
in
biology:
How
does
metabolism
respond
to
genetic,
environmental
or
phenotypic
perturbations?
Combining
several
metabolomics
assays
can
yield
datasets
for
more
than
800
structurally
identified
metabolites.
However,
biological
interpretations
of
metabolic
regulation
these
are
hindered
by
inherent
limits
pathway
enrichment
statistics.
We
have
developed
ChemRICH,
statistical
approach
that
is
based
on
chemical
similarity
rather
sparse
biochemical
knowledge
annotations.
ChemRICH
utilizes
structure
and
ontologies
map
all
known
metabolites
name
modules.
Unlike
mapping,
this
strategy
yields
study-specific,
non-overlapping
sets
Subsequent
statistics
superior
enrichments
because
self-contained
size
where
p
-values
do
not
rely
the
background
database.
demonstrate
ChemRICH’s
efficiency
public
data
set
discerning
development
type
1
diabetes
non-obese
diabetic
mouse
model.
available
at
www.chemrich.fiehnlab.ucdavis.edu
