BITS - Protein inference from mass spectrometry data

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BITS MS Data Processing – Protein InferenceUGent, Gent, Belgium – 16 December 2011

Kenny [email protected]

Lennart [email protected]

Proteomics Services GroupEuropean Bioinformatics Institute

Hinxton, CambridgeUnited Kingdomwww.ebi.ac.uk

kenny helsens

[email protected]

Computational Omics and Systems Biology Group

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

Ghent, Belgium

peptide validationand protein inference



Raw data

Peaklists

Peptide sequences

Protein accession numbersdata sizeambiguity

See: Martens and Hermjakob, Molecular BioSystems, 2007

Data processing and information ambiguity



PEPTIDE IDENTIFICATION VALIDATION



Populations and individuals

10,000 peptide-to-spectrum matches

5%decoy hits



Suspect peptide identifications happen.

The problem is that finding them requiresdetailed analysis of a single spectrum and its identifications, amongst thousands of

other spectra…

Eliminating false positives



Automated interpretation

The Netherlands??



Manual interpretation

Tyrosine phosporylation

See: Ghesquière and Helsens, Proteomics, 2010



Peptizer expert system

Aggregation of the votes

Agent a

Agent b

Agent c

Agent d

Agent e

+ 1 + 1 0 -1 + 1Vote casts

Trustedsubset

Suspicioussubset

Confident Peptide Identifications

See: Helsens et al, Molecular and Cellular Proteomics, 2008











PROTEIN INFERENCE



1a 1b 3 4 6a 6b2 5

1a 1b 3 4 6a2 5

1a 1b 6a 6b2 5

1a 1b 3 6a 6b2 5

1b 3 4 6a 6b2 5

2 5

2 3 5

3 4 52

3 4 52

Gene

Transcripts

Translations

Intron Exon UTR Exon CDS Peptide

Peptidesmatching all transcriptsmatching a transcript subsetmatching exactly 1 translation

redundant

Not all peptides are created equal



Sample preparation consequences

See: Nesvizhskii AI et al, Molecular and Cellular Proteomics, 2005



See: Nesvizhskii AI et al, Molecular and Cellular Proteomics, 2005

Sample preparation consequences



peptides a b c d

proteinsprot X x xprot Y xprot Z x x x

Minimal setOccam {

peptides a b c d

proteinsprot X x xprot Y xprot Z x x x

Maximal setanti-Occam {

peptides a b c d

proteinsprot X (-) x xprot Y (+) xprot Z (0) x x x

Minimal set withmaximal annotation {

true Occam?See: Martens and Hermjakob, Molecular BioSystems, 2007

Protein inference: a question of conviction



ALGORITHMS FOR THE

PROTEIN INFERENCE PROBLEM



• IDPickerZhang et al, Journal of Proteome Research, 2007

• ProteinProphetNesvizhskii AI et al, Analytical Chemistry, 2003

• DBToolkitMartens et al, Bioinformatics, 2005http://genesis.UGent.be/dbtoolkit

A few algorithms for protein inference



IDPicker parsimonious protein assembly

(I) Initialize

See: Zhang et al, Journal of Proteome Research, 2007




(II) Collapse





(III) Separate





(IV) Reduce




peptideprobability

peptideweight

proteinprobability

In iteration 1, all weights w start off as 1/n,with n the degeneracy count for the peptide

peptide probability

See: Nesvizhskii AI et al., Analytical Chemistry, 2003

ProteinProphet: the simplified view



peptides a b cd

proteinsprot X(-) x xprot Y(+) xprot Z(0) x x x

Minimal set withmaximal annotation{

DBToolkit protein inference



peptides a b c d

proteinsprot X (-) x xprot Y (+) xprot Z (0) x x x

Some indications from the HUPO BPP



PROTEIN INFERENCE AND

QUANTIFICATION



Some inference examples (i)

See: Colaert et al, Proteomics, 2010

http://genesis.ugent.be/rover/

Nice and easy, 1/1, only unique peptides (blue) and a narrow distribution



Some inference examples (ii)


Nice and easy, down-regulated




Some inference examples (iii)


A little less easy, up-regulated




Some inference examples (iv)


A nice example of the mess of degenerate peptides




Some inference examples (v)


A bit of chaos, but a defined core distribution




Thank you!

Questions?

Technology

BITS - Protein inference from mass spectrometry data