Share the ppb level accuracy in common LC-MS analysis

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Share the ppb level accuracy in
common LC
-
MS analysis

Zhang Jiyang


School of Mechanical Engineering and
Automatization, National University of Defense
Technology


November
14, 2012

Outline


Background


Proteomics goes
to the high
-
high age


Instrument calibration and data re
-
calibration


FTDR 2.0: implement ppb level re
-
calibration


Workflow


Algorithms


Results


Discussions


How to utilize the high accuracy?




Background




Dreams
and
stories

Technology
needs

Advances on
instrument
and
experiment
protocol

Data
analysis
algorithms
and tools

Insights on
the
technologies
and biological
stories

Proteomics

Proteomics: the discovery loop

Low
-
Low

High
-
Low

High
-
High

High sensitivity LTQ


Low
-
Low

MS

and MS/MS

Very fast scan speed

MET: 3Da

No obvious isotopic profiles

Few isotopic profiles only for very high signals

MET: 0.6Da

LTQ/FT


High
-
Low

MS/MS scan

MS scan

Scan speed vs.
spectrum
quality

Isotopic profile

Isotopic profile

(
not so good
)

LTQ/Orbitrap

Store and CID fragment

Ion storage

MS and MS/MS scan

Isotopic profile

Isotopic profile

Scan speed vs. spectrum quality

Some
literatures


Cox J, Mann M
. Quantitative,
high
-
resolution
proteomics for data
-
driven systems biology
.
Annu

Rev
Biochem
. 2011 Jun 7;80:273
-
99.



Mann M, Kelleher NL.
Precision proteomics
: the case for high resolution and high mass
accuracy.
Proc
Natl

Acad

Sci

U S A. 2008 Nov 25;105(47): 18132
-
8.




Nilsson T, Mann M,
Aebersold

R, Yates JR 3rd,
Bairoch

A, Bergeron JJ.
Mass spectrometry in
high
-
throughput proteomics: ready for the big time.
Nat Methods. 2010 Sep;7(9):681
-
5.



Altelaar

AF et al.
Database independent proteomics analysis of the ostrich and
human proteome.
Proc
Natl

Acad

Sci

U S A. 2012 Jan 10;109(2):407
-
12.
Epub

2011 Dec 22.




Lamond

AI,
Uhlen

M, Horning S, et al.
Advancing cell biology through proteomics in space and
time (PROSPECTS
).
Mol Cell Proteomics. 2012 Mar;11(3):O112.017731. Epub 2012 Feb 6.

More dreams with high accuracy and high resolution
instruments: Top
-
down, cross
-
link based PPI, PTM
identification and discovery, real time state monitor
of cells…

What can be benefited from high
accuracy?


Database search: less candidates


De Novo: less possible


XIC based quantification: less noise?


PTM: less false positives

Yu L,
Xiong

YM,
Polfer

NC
. Periodicity of
monoisotopic

mass isomers
and isobars in proteomic
s. Anal Chem. 2011 Oct 15;83(20):8019
-
23.

Mitra I, Nefedov AV, Brasier AR, Sadygov RG.

Improved mass defect model for theoretical
tryptic

peptides.

Anal Chem. 2012 Mar 20;84(6):3026
-
32.

blank

Accuracy in control and common
experiments

Olsen JV, de Godoy LM, Li G,
Macek

B,
Mortensen P,
Pesch

R,
Makarov

A, Lange
O, Horning S, Mann M
.
Parts per million
mass accuracy on an
Orbitrap

mass
spectrometer via lock mass injection into
a C
-
trap
. Mol Cell Proteomics.
2005;4(12):2010
-
21.

<2ppm in well controlled experiments

Haas W,
Faherty

BK, Gerber SA, Elias JE,
Beausoleil

SA,
Bakalarski

CE, Li X,
Villén

J,
Gygi

SP
. Optimization and use of peptide mass
measurement accuracy in shotgun proteomics
. Mol Cell Proteomics.
2006 Jul;5(7):1326
-
37.

Instrument calibration


Internal calibration and external calibration

Muddiman

DC, Oberg AL. Statistical evaluation of internal and external mass
calibration laws utilized in
fourier

transform ion cyclotron resonance mass
spectrometry.
Anal Chem. 2005 Apr 15;77(8):2406
-
14.

Regress a formula from frequency and charge space effect to m/z.

Instrument calibration


Automatically performed on
Orbitrap

and FT

Parameters can be viewed in raw files

Data re
-
calibration


Question: why the m/z measurement errors vary


with time?



Question: Can we calibrate the m/z values after
the data collections?



Which (parameters) are relative to the m/z
measurement errors?



If possible

Data re
-
calibration

Nat
Biotechnol
. 2008 Dec;26(12):1367
-
72.

MaxQuant enables high peptide identification rates,
individualized
p.p.b.
-
range mass accuracies and
proteome
-
wide protein quantification.

Cox J, Mann M.


J Am Soc Mass
Spectrom
. 2009 Aug;20(8):1477
-
85.

Computational principles of determining and
improving mass precision and accuracy for
proteome measurements in an Orbitrap.

Cox J, Mann M.


J Proteome Res. 2011 Apr 1;10(4):1794
-
1805.
Epub

2011 Feb 22.

Andromeda: A Peptide Search Engine
Integrated into the
MaxQuant

Environment.

Cox J,
Neuhauser

N,
Michalski

A,
Scheltema

RA,
Olsen JV, Mann M.



Data re
-
calibration


Our work:


J Proteome Res. 2009 Feb;8(2):849
-
59.


Mass measurement errors of Fourier
-
transform mass spectrometry (FTMS): distribution, recalibration, and


application.


Zhang J, Ma J, Dou L, Wu S,
Qian

X, Xie H, Zhu Y, He F.



Simple Calibration


J Proteome Res. 2010 Jan;9(1):393
-
403.


MSQuant
, an open source platform for mass spectrometry
-
based quantitative proteomics.


Mortensen P,
Gouw

JW, Olsen JV,
Ong

SE,
Rigbolt

KT,
Bunkenborg

J, Cox J, Foster LJ, Heck AJ,
Blagoev

B,


Andersen JS, Mann M.



Nonlinear calibration



Mol Cell Proteomics. 2010 Mar;9(3):486
-
96.
Epub

2009 Dec 17.


DtaRefinery
, a software tool for elimination of systematic errors from parent ion mass measurements in


tandem mass spectra data sets.


Petyuk

VA,
Mayampurath

AM, Monroe ME,
Polpitiya

AD,
Purvine

SO, Anderson GA, Camp DG 2nd, Smith RD.



Application: Search with large MET,
filteration

with little MET:


Comparison of Database Search Strategies for High Precursor Mass Accuracy MS/MS Data


Edward J.

Hsieh, Michael R.

Hoopmann
, Brendan

MacLean and Michael J.

MacCoss


J. Proteome, 2010, 9 (2):1138

1143


Reduce the system error of m/z measurement,
share the ppb level accuracy in common LTQ/FT
and LTQ
-
Orbitrap experiments

FTDR: ppb level calibration

Is it possible?

Workflow:
Local to global

XIC (or EIC) :
extracted ion chromatogram

Key
Algorithms in FTDR


Parameters extraction and selection



XIC extraction



Parent ion re
-
selection



Local calibration models



Basic:
observed m/z , RT, TIC, parent ion
intensity (log transform), relative parent ion
intensity.



Status:
FT 83, Orbitrap

107

RF

voltage

temperature of ICR
.



Operation:
Ion Injection time, Scan time et al.



isotopic profile:
goodness of fitting, number of
isotopic peaks
.


Parameters for local model

How to obtain these parameters?

m/z, intensity

RT


Nonlinear relations:
mRMR
, minimum
Redundancy Maximum Relevance Feature
Selection


Parameter selection

Chris Ding, and
Hanchuan

Peng

.


Minimum redundancy feature selection from microarray gene expression data.

Journal of Bioinformatics and Computational Biology, 2005 , 3(2):185
-
205.

Recent works used this kind of method:

Reshef

DN, et al. Detecting novel associations in
large data sets.
Science. 2011 Dec
16;334(6062):1518
-
24.


FT

15


Parametee selection

MI Parameter

0.6767

mz experiment

0.5389

Retention time

0.0573

FT IOS +275 Supply (V)

0.3203

IsoNum

0.2836

FT RF1 Amp. Temp. (C)

0.4637

Ambient Temp. (C)

0.0441

Gate Lens (V)

0.3682

FT EA Temp. (C)

0.2030

Nitrogen (%)

0.5298

Source Current (uA)

0.0446

FT EA
-
32 Supply (V)

0.1401

RF Detector Temp (C)

0.3620

RF Generator Temp (C)

0.0567

Relative PInt

0.0348

Front Section (V)

Remove the little MI

MI Parameter

0.6767

mz experiment

0.5389

Retention time

0.3203

IsoNum

0.2836

FT RF1 Amp. Temp. (C)

0.4637

Ambient Temp. (C)

0.3682

FT EA Temp. (C)

0.2030

Nitrogen (%)

0.5298

Source Current (uA)

0.1401

RF Detector Temp (C)

0.3620

RF Generator Temp (C)

MI: Mutual information

Parametee selection


Orbitrap

15

MI Parameter

0.1792

mz

experiment

0.0639

Abs
PInt

0.0512

-
28V Supply Voltage (V)

0.0417

FT IOS
-
275 Supply (V)

0.0393

FT Deflector Measure Voltage (V)

0.0818

IsoNum

0.0362

FT TMPC HS Temp. (
°
C)

0.0434

FT Main RF Amplitude (
Vp
-
p)

0.0329

FT HV Lens 3 (V)

0.0581

Relative
PInt

0.0304

Front Lens (V)

0.0466

FT HV Ion Energy (V)

0.0392

Gate Lens (V)

0.0382

FT Storage
Multipole

Offset (V)

0.0286

IsoMGD

MI>0.05

m/z
experiment

Retention time

Elapsed Scan Time

Relative
PInt

IsoMGD

+24V Supply Voltage (V)

XIC Extraction

Isotopic profile match in each MS








N
i
N
i
T
e
N
i
T
e
I
I
I
I
gd
1
1
2
2
1
e
I
intensity

isotopic

observed
:
e
I
intensity

isotopic

predicted
:
T
I
MET: m/z error tolerance

(1)
4 kind of XIC trunked methods were used in FTDR :
1
st


RT or count gap, 2
nd

RT range, 3
rd

MS signal count,
4
th

Savitzky

Golay (
SG) smoothing and local
minimal points detection.

(2)
The 1
st

is used in any XIC searching step in FTDR. The
2
nd

is only used in the calibration step and will be
automatically disabled when the 4th rules was used.
The 3
rd

is used to limited the volume of training
datasets by counting the observations.

Parent ion re
-
selection

Monoisotopic peak

All possible interpretations

overlap

Incorrect position

E:\zhangj y\...\raw\B06-11071
2006-11-6 21:06:23
18_Mi x
RT:
0.00 - 79.99
0
10
20
30
40
50
60
70
Ti me (mi n)
0
10
20
30
40
50
60
70
80
90
100
Relative Abundance
31.07
50.28
55.04
40.45
45.73
36.48
65.77
61.64
78.71
56.13
30.30
74.11
26.23
68.35
21.48
5.12
19.31
10.68
NL:
3.41E7
TIC MS
B06-11071
B06-11071

#
771
RT:
12.97
AV:
1
NL:
1.33E1
T:
ITMS + p NSI d w Ful l ms2 533.30@ci d30.00 [135.00-1080.00]
200
300
400
500
600
700
800
900
1000
m/z
0
10
20
30
40
50
60
70
80
90
100
Relative Abundance
433.75
424.75
218.17
729.33
631.33
365.33
496.83
313.33
667.58
561.25
829.33
201.33
297.17
895.75
1026.33
761.42
E:\zhangj y\...\raw\B06-11071
2006-11-6 21:06:23
18_Mi x
RT:
0.00 - 79.99
0
10
20
30
40
50
60
70
Ti me (mi n)
0
10
20
30
40
50
60
70
80
90
100
Relative Abundance
31.07
50.28
55.04
40.45
45.73
36.48
65.77
61.64
78.71
56.13
30.30
74.11
26.23
68.35
21.48
5.12
19.31
10.68
NL:
3.41E7
TIC MS
B06-11071
B06-11071

#
770
RT:
12.95
AV:
1
NL:
1.29E3
T:
FTMS + p NSI Ful l ms [300.00-1600.00]
530
531
532
533
534
535
536
537
538
539
m/z
0
200
400
600
800
1000
1200
Intensity
536.16626
532.79095
530.49158
538.16180
Absent

Multiple possibility

Parent ion re
-
selection

E:\data_source\...\Raw\yeast_2_01
0
20
40
60
80
Time (min)
0
50
100
Relative Abundance
33.33
38.42
69.56
31.99
50.10
22.91
86.40
NL:
2.55E8
TIC MS
yeast_2_01
500
1000
1500
2000
m/z
0
50
100
Relative Abundance
902.77
1133.87
800.40
571.27
1213.27
288.20
1753.94
1517.79
E:\data_source\...\Raw\yeast_2_01
0
20
40
60
80
Time (min)
0
50
100
Relative Abundance
33.33
38.42
69.56
31.99
50.10
22.91
86.40
NL:
2.55E8
TIC MS
yeast_2_01
500
1000
1500
2000
m/z
0
50
100
Relative Abundance
956.53
749.41
1137.55
1362.97
1579.60
1852.61
E:\data_source\...\Raw\yeast_2_01
2006-9-8 04:01:49
RT:
0.00 - 99.99
0
20
40
60
80
Time (min)
0
20
40
60
80
100
Relative Abundance
33.33
38.42
69.56
31.99
70.38
50.10
56.81
22.91
15.80
15.05
86.40
14.09
7.31
NL:
2.55E8
TIC MS
yeast_2_01
846
848
850
852
854
856
858
860
m/z
0
20
40
60
80
100
Relative Abundance
852.14
852.48
848.75
857.92
852.81
857.68
855.83
850.97
847.12
+

+

C1

C2

C3

+2

+2

+1

Decompose each isotopic profile group

Segment into different
isotopic profile group

Extract all peaks

Assign back to the
MS/MS spectrum

Result: one MS/MS spectrum may generate multiple targets

Fitting to the predict distribution


Linear model: parameter transform



Local linear:
multivariate
(hard to implement)





Local Linear

piecewise on RT



Nonlinear

卖S

牥r牥r獩潮o⡵獩湧n
LIBSvm

source code)

Local models (try and
implement
)

Robustness and Accuracy.

Result & discussion


Local calibration and global calibration on
ISB_FT dataset



Global calibration on the
Yeast_FT_dataset



Compare with MaxQuant



Try on the label free quantification dataset

Model types

Linear

Multivariate

Linear

Local regression

SVM

MET(ppm)

2.46

2.13

2.19

1.56

Performance comparison

Note: (1) not XIC global calibration,



(2) linear models: mz
2
, TIC*mz
2
tansform

, SVM dose not use



(3) MET is estimated by the residual distribution.

-6
-4
-2
0
2
4
6
8
0
0.1
0.2
0.3
0.4
0.5
0.6
ppm
Density


-4
-2
0
2
4
6
8
0
0.2
0.4
0.6
0.8
ppm
Density


-5
0
5
0
0.1
0.2
0.3
0.4
0.5
0.6
dm (ppm)
Density


-10
-5
0
5
10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Data
Density


res data
N(-0.0148,0.5275)
Dataset: ISB_FT Mix 3, original MET 5ppm

Ref:
Klimek

J et al. The standard protein mix database: a diverse data set to assist in
the production of improved Peptide and protein identification software tools. J
Proteome Res. 2008 Jan;7(1):96
-
103.


Model

SVR


MET

0.46ppm

Performance of Global Calibration

)
,
(
~
Re
sigma
mu
N
s
0
,
3


mu
sigma
MET
Breitwieser

FP, et al. General statistical modeling of data from protein relative
expression isobaric tags. J Proteome Res. 2011 Jun 3;10(6):2758
-
66.

0
2
4
6
8
10
12
14
16
18
20
-1.5
-1
-0.5
0
0.5
1
1.5
2
sqrt(MS
-
Num)
m/z error (ppm)


m/z error vs sqrt(MS
-
Num)
Upper bound=mu+3*std(MS
-
Num)
Lower bound=mu-3*std(MS
-
Num)
b
MSNum
a
sigma


/
a=
0
.
429970

b=
0
.
058963

-1.5
-1
-0.5
0
0.5
1
1.5
0
0.5
1
1.5
2
2.5
3
m/z error (ppm)
Density


m/z error (ppm)
Normal: mu=-0;0099,sd=0.1536
Signal intensity relative MET
is more reasonable!

Database search results


Dataset: Yeast_FT_RP10


Search: Mascot V 2.1


Different for different mgf

Test

Submitted MS/MS spectrum

Total PSMs

Validated

PSMs*

ppb level

MET min

MET max

Before calibration
15ppm

59828

45813

29400

2371

-
4.321589

12.939775

After

calibration
1.3ppm

91430#

47128

14299

14255

-
1.011142

1.139910

After calibration
15ppm

91430#

58245

24071

23900


-
1.099507

1.178323

After calibration
15ppm

91430#

58245


37225

36104

-
3.2@

3.2@

*Validate method: 2d cutoff model, FDR=1%, ref to: Ma J, et al. Proteomics. 2010;10(23):4293
-
300.

Liu K, Zhang J, Wang J, Zhao L,
Peng

X,
Jia

W, Ying W, Zhu Y,
Xie

H, He F,
Qian

X.
Relationship between Sample Loading
Amount and Peptides Identification and Its
Effect on Quantitative Proteomics. Anal Chem.
2009;81(4):1307
-
14.

+/
-
1.3ppm, 15ppm,

#

91430: with parent ion re
-
selection, @ max range given by Intensity model

Conclusion:
The m/z error filtration can
affect the database search and result
validation model.

FTDR performance on 6 datasets

Datasets

Database

Search MET (ppm)

Validate m/z error
range

(ppm)

Validated

PSMs

ppb level

PSMs

Percent of ppb
level PSMs (%)

D1

B

20

[
-
1.52, 5.09]

10,783

3,277

30.39

A

20

[
-
0.65, 0.63]

10,507

10,507

100.00

D2

B

20

[
-
9.16, 2.15]

5,980

478

7.99

A

20

[
-
1.70, 1.59]

6,817

6,264

91.89

D3

B

20

[
-
4.80, 17.82]

16,715

560

3.35

A

20

[
-
1.74, 1.72]

27,182

24,492

90.10

D4

B

20

[
-
6.13, 6.08]

19,758

8,325

42.13

A

20

[
-
1.00, 0.93]

34,008

34,008

100.0

D5

B

10

[
-
5.18, 8.78]

14,290

4,525

31.67

A

10

[
-
1.15, 1.20]

35,126

33,283

94.75

D6

B

20

[
-
1.67, 8.89]

44,382

1,974

4.45

A

20

[
-
1.41, 1.49]

53,017

50,534

95.32

D1&D2:
Klimek J et al. The Standard Protein Mix Database: A Diverse Data Set To Assist in the Production of Improved Peptide and
Protein Identification Software Tools. J. Proteome Res. 2008, 7 (1): 96
-
103.

D3:
Chen M et al. Analysis of human liver proteome using replicate shotgun strategy. Proteomics, 2007. 7(14): 2479
-
88.

D4:
Cox, J.; Mann, M., MaxQuant enables high peptide identification rates, individualized ppb
-
range mass accuracies and proteome
-
wide protein quantification. Nature Biotechnology 2008, 26 (12):1367
-
1372.

D5:
Jedrychowski M et al. Evaluation of HCD
-

and CID
-
type fragmentation within their respective detection platforms for murine
phosphoproteomics. Mol Cell Proteomics 2011, 10 (12):M111 009910.

D6:
Liu K et al. Relationship between Sample Loading Amount and Peptide Identification and Its Effects on Quantitative Proteomics
.
Anal. Chem. 2009, 81: 1307
-
1314.

Database search: Mascot 2.3

Different search engines

m/z
error range
(ppm)

Validate PSMs

ppb
level PSMs
(%)

FDR
Act
(%)

Sequest

B

[
-
1.45, 5.17]

11,581

31.18

0.61

A

[
-
0.68, 0.72]

11,625

100.00

0.57

Mascot

B

[
-
1.52, 5.09]

10,783

30.39

0.49

A

[
-
0.65, 0.63]

10,507

100.00

0.40

X!Tandem

B

[1.78, 5.48]

8,299

37.06

1.04

A

[
-
1.23, 1.04]

8,188

98.24

0.66

MassMatrix

B

[
-
1.35, 4.88]

6,492

32.10

0.92

A

[
-
0. 61, 0.67]

8,224

100.00

0.73

Parameters: 2ppm, 0.6Da

Dataset: ISB_control_FT Mix 3

B: Before re
-
calibration

A: After re
-
calibration

In common experiments


Dataset: Yeast total, 10 repeat LC
-
Runs

Liu K, Zhang J, Wang J, Zhao L,
Peng

X,
Jia

W, Ying W, Zhu Y,
Xie

H, He F,
Qian

X.
Relationship between Sample Loading
Amount and Peptides Identification and Its
Effect on Quantitative Proteomics. Anal Chem.
2009;81(4):1307
-
14.

LC
-
Run

Original m/z
error

mean

Original m/z
error std

Calibrated m/z
error mean

Calibrated m/z
error std

Total calibrated
MS2 spectrum

(Predicted*)

on ppb level

Yeast_FT_01

4.473023

3.086881

0.000000

0.352747

5145

2189

Yeast_FT_02

4.403325

3.051344

0.000000

0.357674

5237

2312

Yeast_FT_03

4.379363

3.033093

0.000000

0.347622

5283

2643

Yeast_FT_04

4.379597

2.991206

0.000000

0.350134

5387

2591

Yeast_FT_05

4.232066

2.900684

0.000000

0.352454

5347

2763

Yeast_FT_06

4.332880

2.946208

0.000000

0.334838

5322

2899

Yeast_FT_07

4.260813

2.879833

0.000000

0.346009

5301

2578

Yeast_FT_08

4.244320

2.855727

0.000000

0.352397

5358

2742

Yeast_FT_09

4.263944

2.858887

0.000000

0.329935

5427

2961

Yeast_FT_10

4.259115

2.801903

0.000000

0.367498

5412

1589

*The ppb level record is
conservatively

predicted by the signal intensity model.

Compare with MaxQuant


Label free search on
Yeast_FT_dataset

-5
0
5
10
0
0.2
0.4
0.6
0.8
mass error (ppm)
Density


after calibration
mu=0.0297, sigma=0.5121
0
5
10
15
20
0
0.05
0.1
0.15
0.2
0.25
mass error (ppm)
Density


before
mu=3.956, sigma=1.5566
*Dose not provide the m/z errors for the records after calibration.

Test

Submitted MS/MS spectrum

Total PSMs

Validated

PSMs*

ppb level

MET min

MET max

After calibration
15ppm (FTDR)

91430

58245


37225

36104

-
3.2@

3.2@

MaxQuant

63629

26982

21817

20037

-
5.4939

10.4580

For label free dataset


Dataset: Yeast total, 10 repeat LC
-
Runs


Database search : X!Tandem


Quantification: MassChroQ

0
0.2
0.4
0.6
0.8
1
1.2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1


X: 0.4384
Y: 0.9505
CV
Cumulative probability
X: 0.153
Y: 0.5662
before
after
No obvious improvement on the CV

B.

Valot
, O.

Langella
, E.

Nano
, and M.

Zivy
,

Masschroq
: A versatile tool for mass
spectrometry quantification,” Proteomics,
vol.

11, no.

17, pp.

3572

3577, 2011.

Liu K, Zhang J, Wang J, Zhao L,
Peng

X,
Jia

W,
Ying W, Zhu Y,
Xie

H, He F,
Qian

X. Relationship
between Sample Loading Amount and Peptides
Identification and Its Effect on Quantitative
Proteomics. Anal Chem. 2009;81(4):1307
-
14.

XIC is robust to MET?


Smoothing and other filtration can reduce the
noise signal?


The high resolution instrument provide the
“clean” signals?

62
63
64
65
66
67
68
69
70
0
0.5
1
1.5
2
2.5
3
x 10
5
Time
Intensity


5ppm
10ppm
20ppm
30ppm
40ppm
50ppm
100ppm
500ppm
E:\data_source\...\Raw\yeast_2_01
2006-9-8 04:01:49
RT:
59.16 - 73.95
60
62
64
66
68
70
72
Time (min)
0
50000
100000
150000
200000
250000
Intensity
63.34
63.48
63.25
63.85
63.15
63.04
64.05
62.94
64.18
64.27
62.81
71.26
64.60
68.82
65.02
66.36
71.72
62.52
59.33
60.50
NL: 2.93E5
Base Peak m/z=
1195.53560-
1196.73174 F: FTMS +
p NSI Full ms
[400.00-2000.00] MS
yeast_2_01
yeast_2_01

#
6624-6811
RT:
62.69-64.99
AV:
100
NL:
1.07E5
F:
FTMS + p NSI Full ms [400.00-2000.00]
1195.0
1195.5
1196.0
1196.5
1197.0
1197.5
1198.0
1198.5
m/z
0
10
20
30
40
50
60
70
80
90
100
Relative Abundance
1196.63595
1196.13498
1197.13716
1197.63972
1198.14109
1198.64736
1195.95280
1195.16541
No preponderant signals in a large range

Discussion


Implement specific MET search for each spectrum?
Like
Andromeda
.


Only can be tried on open source database search
engine: X!Tandem, Inspect and Crux.


Initial result: no obvious difference on speed and
results for X!Tandem.


Possible reasons: X!Tandem is so fast, and provide
less results than Sequest or Mascot.

Modified mgf
header

Modified source
code

Software design


GUI


Multiple
Threads


Output:
mzXML
,
mzML
, or
mgf


Quick result
view


Workspace
save and load


Advance
parameters


PTM search



LC
-
MS
E

data processing



Label free quantification with UPLC(narrow
XIC)?

Other applications under considering

Acknowledgement


ISB: control dataset


Dr.
Jie

Ma, BPRC


Prof.
Yunping

Zhu, BPRC


Prof
. Xiaohong
Qian
, BPRC


Our team: Prof.Hong wei Xie,
Wei Zhang,
Changming

Xu
.

E:\data\10FT-RAW\yeast_2_01
2006-9-8 4:01:49
RT:
0.00 - 100.00
0
20
40
60
80
Time (min)
0
100
0
100
0
100
0
100
0
100
Relative Abundance
0
100
0
100
0
100
43.24
22.91
50.10
32.98
15.71
68.78
83.83
10.57
85.16
22.88
43.26
50.12
42.00
15.84
69.91
81.02
85.92
1.02
50.13
43.24
32.98
22.68
15.70
69.33
53.88
80.81
85.76
2.79
43.09
22.61
49.92
32.84
16.47
69.72
56.71
10.80
80.88
85.73
43.22
49.96
22.54
32.84
15.59
57.20
70.00
85.68
0.24
32.51
42.76
49.68
21.92
15.36
56.43
69.77
80.88
13.56
86.16
42.82
32.45
21.84
49.85
15.34
56.56
69.95
80.81
13.71
88.20
32.47
42.72
21.76
49.79
15.08
56.58
70.04
85.81
13.89
NL: 5.25E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_01
NL: 6.20E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_02
NL: 5.64E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_03
NL: 5.36E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_04
NL: 5.67E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_05
NL: 5.27E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_06
NL: 5.61E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_07
NL: 6.16E7
Base Peak F: FTMS + p NSI Full ms
[400.00-2000.00] MS yeast_2_08
Thank you for your attention!