BITS - Protein inference from mass spectrometry data

transcript

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

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

Kenny Helsenskenny.helsens@UGent.be

Lennart MARTENSlennart.martens@ebi.ac.uk

Proteomics Services GroupEuropean Bioinformatics Institute

Hinxton, CambridgeUnited Kingdomwww.ebi.ac.uk

kenny helsens

kenny.helsens@ugent.be

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

3 4 52

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

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?

BITS - Protein inference from mass spectrometry data

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