BITS - Search engines for mass spec data

http://www.bits.vib.be/training

search engines

lennart martens
lennart.martens@ugent.be
Lennart MARTENS
lennart.martens@ebi.ac.uk
Computational Omics and Systems Biology Group
Proteomics Services Group
European Bioinformatics Institute
Department of Medical Protein Research, VIB
Hinxton, Cambridge
United Kingdom
Department of Biochemistry, Ghent University
www.ebi.ac.uk
Lennart Martens Ghent, Belgium
BITS MS Data Processing – Search Engines
lennart.martens@UGent.be UGent, Gent, Belgium – 19 September 2011

THREE TYPICAL PRE-PROCESSING STEPS

Lennart Martens BITS MS Data Processing – Search Engines

Noise thresholding
precursor
Global thresholding

precursor
Local thresholding


Charge deconvolution (peptides)

From: http://www.purdue.edu/dp/bioscience/images/spectrum.jpg


Charge deconvolution (proteins)

From: Gill et al, EMBO Journal, 2000


Centroiding (peak picking)

Monoisotopic mass Average mass

x x

Combined results

A total ion current chromatogram, corrected by
typical pre-processing steps.

From: Last et al, Nature Rev. Mol. Cell Bio., 2007


Data size reduction
60

Q-TOF II
Q-TOF Esquire HCT
Esquire HCT
50

40

File size
File size (MB)

(MB)
30

51.4

20

24.5 25.8
23.7
10

0.7 0.2 0.3 0.1
0
RAW RAW GZIPped Peak lists Peak lists GZIPped
Data type
Data type

See: Martens et al., Proteomics, 2005


MS/MS IDENTIFICATION
PEPTIDE FRAGMENTATION FINGERPRINTING


Peptide sequences and MS/MS spectra

LENNART
intensity

LENNAR
RT

NNART
NART
LEN LENNART
LENNA LENNART
ART ENNART
T LENN
L
LE
L E N N A R T
m/z

Peptide fragment fingerprinting (PFF)
Int
YSFVATAER
m/z
Int
HETSINGK
in silico in silico Int
m/z

MILQEESTVYYR
digest MS/MS
m/z
Int
SEFASTPINK
… m/z

protein sequence database peptide sequences theoretical MS/MS
spectra

1) YSFVATAER 34
in silico
2) YSFVSAIR 12
3) FFLIGGGGK 12 matching
peptide scores
experimental MS/MS spectrum


Three types of PFF identification

Spectral comparison
theoretical compare experimental
database sequence
spectrum spectrum

Sequencial comparison
compare de novo experimental
database sequence
sequence spectrum

Threading comparison
thread experimental
database sequence
spectrum

From: Eidhammer, Flikka, Martens, Mikalsen – Wiley 2007


The most popular algorithms

• MASCOT (Matrix Science)
http://www.matrixscience.com

• SEQUEST (Scripps, Thermo Fisher Scientific)
http://fields.scripps.edu/sequest

• X!Tandem (The Global Proteome Machine Organization)
http://www.thegpm.org/TANDEM

• OMSSA (NCBI)
http://pubchem.ncbi.nlm.nih.gov/omssa/


Overall concept of scores and cut-offs

Incorrect identifications Threshold score

Correct
identifications

False negatives False positives
Adapted from: www.proteomesoftware.com – Wiki pages


Playing with probabilistic cut-off scores

higher stringency

6% 100%

90%
5%
80%

4%
identifications 70%

60%

3% 50%

false positives 40%
2%
30%

20%
1%
10%

0% 0%
p=0.05 p=0.01 p=0.005 p=0.0005


SEQUEST
• Very well established search engine
• Can be used for MS/MS (PFF) identifications
• Based on a cross-correlation score (includes peak height)
• Published core algorithm (patented, licensed to Thermo), Eng, JASMS 1994
• Provides preliminary (Sp) score, rank, cross-correlation score (XCorr),
and score difference between the top tow ranks (deltaCn, ∆Cn)
• Thresholding is up to the user, and is commonly done per charge state

��
• Many extensions exist to perform a more automatic validation of results

�� = � �� ∙ ��(��+��)
��=1
1
+75

XCorr = ��0 − 151 � ��
XCorr 1 − XCorr 2
��=−75
deltaCn=
XCorr 1

SEQUEST: some additional pictures

From: MacCoss et al., Anal. Chem. 2002

From: Peng et al., J. Prot. Res.. 2002


Mascot

• Very well established search engine, Perkins, Electrophoresis 1999
• Can do MS (PMF) and MS/MS (PFF) identifications
• Based on the MOWSE score,
• Unpublished core algorithm (trade secret)
• Predicts an a priori threshold score that identifications need to pass
• From version 2.2, Mascot allows integrated decoy searches
• Provides rank, score, threshold and expectation value per identification
• Customizable confidence level for the threshold score


Mascot: some additional pictures

40
Average identity threshold

35 y = 8.3761x - 34.089
2
6%R = 0.9985 100%
Average identitythreshold

30

25 90%
5%
20 80%
15 70%
4%
10
identifications 60%
5
3% 50%
0
6.50 7.00 7.50 8.00 8.50 40%
2% log10(number of AA)
30%
false positives
20%
1%
10%

0% 0%
p=0.05 p=0.01 p=0.005 p=0.0005


X!Tandem

• A successful open source search engine, Craig and Beavis, RCMS 2003
 n 
• Based on a hyperscore (Pi is either 0 or 1): HyperScore =  ∑ Ii * Pi  * Nb !* Ny !
 i =0 
• Relies on a hypergeometric distribution (hence hyperscore)
• Published core algorithm, and is freely available
• Provides hyperscore and expectancy score (the discriminating one)
• X!Tandem is fast and can handle modifications in an iterative fashion
• Has rapidly gained popularity as (auxiliary) search engine


X!Tandem: some additional pictures
60 4

3.5
50
3

log(# results)
40
# results

2.5

30 2

1.5
20
1

10 0.5

0
0 20 25 30 35 40 45 50
0 20 40 60 80 100 hyperscore
hyperscore
significance
6
threshold
4
log(# results)

2

0

-2 Adapted from: Brian Searle, ProteomeSoftware,
-4
http://www.proteomesoftware.com/XTandem_edited.pdf
-6

-8
E-value=e-8.2
-10
0 20 40 60 80 100
hyperscore

A note on how the scores differ

SEQUEST Accuracy Score Relative Score

XCorr DeltaCn
X! Tandem

HyperScore E-Value

Adapted from: Brian Searle, ProteomeSoftware


OMSSA

• A successful open source search engine, Geer, JPR 2004
• Relies on a Poisson distribution
• Published core algorithm, and is freely available
• Provides an expectancy score, similar to the BLAST E-value
• OMSSA was recently upgraded to take peak intensity into account
• Good really good marks in a recently published comparative study


OMSSA: some additional pictures

Yeast lysate spectrum, m/z matches of Validation of the Poisson distribution model:
fragment peak matches versus all NCBI nr mean number of modelled and measured
sequence library. Poisson distribution fitted. matching peaks (against the NCBI nr
database) for two mass tolerances.

Adapted from: Geer et al., J. Prot. Res., 2004


COMPARATIVE STUDIES


Kapp et al., Proteomics, 2005


Balgley et al., Mol. Cell. Proteomics, 2007

1.6x more?!


Combining the output of search algorithms

Mascot SEQUEST
3229 3792
212 486
(+4,2%) (+9,6%)
ProteinSolver
3203
179 168 Phenyx
40
3186
329 380
(+6,5%) 501 348 (+7,5%)

1776
139 96
195 77

146

Figure courtesy of Dr. Christian Stephan, Medizinisches Proteom-Center,
Ruhr-Universität Bochum; Human Brain Proteome Project


SEQUENCIAL COMPARISON
ALGORITHMS


Sequence tags

sequence tag

The concept of sequence tags was introduced by Mann and Wilm
(Mann,and Wilm, Anal. Chem. 1994, 66: 4390-4399).

Image from: Matthias Wilm, EMBL Heidelberg, Germany
http://www.narrador.embl-heidelberg.de/GroupPages/PageLink/activities/SeqTag.html

GutenTag, DirecTag, TagRecon

• Tabb, Anal. Chem. 2003, Tabb, JPR 2008, Dasari, JPR 2010
• Recent implementations of the sequence tag approach
• Refine hits by peak mapping in a second stage to resolve ambiguities
• Rely on a empirical fragmentation model
• Published core algorithms, DirecTag and TagRecon freely available
• Most useful to retrieve unexpected peptides (modifications, variations)
• Entire workflows exist (e.g., combination with IDPicker)


GutenTag: some additional pictures

From: Tabb et al., Anal. Chem., 2003


De novo compared to sequence tags

Example of a manual de novo of an MS/MS spectrum
No more database necessary to extract a sequence!

Algorithms References

Lutefisk Dancik 1999, Taylor 2000
Sherenga Fernandez-de-Cossio 2000
PEAKS Ma 2003, Zhang 2004
PepNovo Frank 2005, Grossmann 2005
… …


Thank you!
Questions?

BITS - Search engines for mass spec data

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