BITS - Search engines for mass spec data

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

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Lennart MARTENS lennart.martens@ebi.ac.uk

Proteomics Services Group European Bioinformatics Institute

Hinxton, Cambridge United Kingdom www.ebi.ac.uk

search engines

lennart martens

lennart.martens@ugent.be

Computational Omics and Systems Biology Group

Department of Medical Protein Research, VIB Department of Biochemistry, Ghent University

Ghent, Belgium

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

THREE TYPICAL PRE-PROCESSING STEPS

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Global thresholding

Local thresholding

precursor

precursor

Noise thresholding

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

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

Charge deconvolution (peptides)

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

From: Gill et al, EMBO Journal, 2000

Charge deconvolution (proteins)

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

x x

Monoisotopic mass Average mass

Centroiding (peak picking)

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

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

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

Combined results

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

51.4

25.8

0.7 0.3

24.5 23.7

0.2 0.10

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RAW RAW GZIPped Peak lists Peak lists GZIPpedData type

File

siz

e (M

B)

Q-TOF I Esquire HCT

Data type

File size (MB)

Q-TOF I Esquire HCT

See: Martens et al., Proteomics, 2005

Data size reduction

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

MS/MS IDENTIFICATION

PEPTIDE FRAGMENTATION FINGERPRINTING

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

L E N N A R T

L LE

LEN

LENN

LENNA

LENNAR

LENNART

E N N A R T L

T

RT

ART

NART NNART

ENNART

LENNART

m/z

intensity

Peptide sequences and MS/MS spectra

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

protein sequence database

in silico

digest

YSFVATAER

HETSINGK

MILQEESTVYYR

SEFASTPINK

…

peptide sequences

m/z

Int

m/z

Int

m/z

Int m/z

Int in silico

MS/MS

theoretical MS/MS spectra

experimental MS/MS spectrum

in silico

matching

1) YSFVATAER 34 2) YSFVSAIR 12 3) FFLIGGGGK 12

peptide scores

Peptide fragment fingerprinting (PFF)

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Spectral comparison

Sequencial comparison

Threading comparison

database sequence theoretical spectrum

experimental spectrum

compare

database sequence experimental spectrum

compare de novo sequence

database sequence experimental spectrum

thread

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

Three types of PFF identification

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

• 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/

The most popular algorithms

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Incorrect identifications

Correct identifications

False positives False negatives

Threshold score

Adapted from: www.proteomesoftware.com – Wiki pages

Overall concept of scores and cut-offs

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

0%

1%

2%

3%

4%

5%

6%

p=0.05 p=0.01 p=0.005 p=0.00050%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

false positives

identifications

higher stringency

Playing with probabilistic cut-off scores

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

• 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

SEQUEST

XCorr = deltaCn= XCorr1− XCorr 2

XCorr1𝑅0 −

1151

� 𝑅𝑅+75

𝑖=−75

𝑅𝑖 = �𝑥𝑗 ∙ 𝑦(𝑗+𝑖)

𝑛

𝑗=1

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

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

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

SEQUEST: some additional pictures

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

• 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

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

y = 8.3761x - 34.089R2 = 0.9985

0

5

10

15

20

25

30

35

40

6.50 7.00 7.50 8.00 8.50log10(number of AA)

Ave

rage

iden

tity

thre

shol

dA

vera

ge id

enti

ty t

hres

hold

Mascot: some additional pictures

0%

1%

2%

3%

4%

5%

6%

p=0.05 p=0.01 p=0.005 p=0.00050%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

false positives

identifications

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

• A successful open source search engine, Craig and Beavis, RCMS 2003

• Can be used for MS/MS (PFF) identifications

• Based on a hyperscore (Pi is either 0 or 1):

• 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

*0

* !* !n

i i b yi

HyperScore I P N N=

= ∑

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

-10

-8

-6

-4

-2

0

2

4

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0 20 40 60 80 100

hyperscore

log(

# re

sults

)

log(

# re

sults

)

0

0.5

1

1.5

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3

3.5

4

20 25 30 35 40 45 50

hyperscore 0

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hyperscore

# re

sults

Adapted from: Brian Searle, ProteomeSoftware, http://www.proteomesoftware.com/XTandem_edited.pdf

significance threshold

E-value=e-8.2

X!Tandem: some additional pictures

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

A note on how the scores differ

X! T

ande

m

SEQ

UES

T

XCorr

HyperScore

DeltaCn

E-Value

Accuracy Score Relative Score

Adapted from: Brian Searle, ProteomeSoftware

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

• A successful open source search engine, Geer, JPR 2004

• Can be used for MS/MS (PFF) identifications

• 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

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Yeast lysate spectrum, m/z matches of fragment peak matches versus all NCBI nr sequence library. Poisson distribution fitted.

Validation of the Poisson distribution model: mean number of modelled and measured

matching peaks (against the NCBI nr database) for two mass tolerances.

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

OMSSA: some additional pictures

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

COMPARATIVE STUDIES

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Kapp et al., Proteomics, 2005

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

1.6x more?!

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

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

1776

Mascot SEQUEST

Phenyx

ProteinSolver

501

40

212 (+4,2%)

486 (+9,6%)

329 (+6,5%)

380 (+7,5%)

3203

3229 3792

3186 168

348

179

96

146

139 77 195

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

Combining the output of search algorithms

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

SEQUENCIAL COMPARISON

ALGORITHMS

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

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

sequence tag

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

Sequence tags

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

• 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, DirecTag, TagRecon

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

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

GutenTag: some additional pictures

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

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

Algorithms

Lutefisk Sherenga

PEAKS PepNovo

…

References

Dancik 1999, Taylor 2000 Fernandez-de-Cossio 2000

Ma 2003, Zhang 2004 Frank 2005, Grossmann 2005

…

De novo compared to sequence tags

BITS MS Data Processing – Search Engines UGent, Gent, Belgium – 19 September 2011

Lennart Martens lennart.martens@UGent.be

Thank you!

Questions?