Common parameters

Common parameters

• At the beginning one need to set up the parameters.

• http://human.thegpm.org

http://human.thegpm.org/

Common parameters

• Most important: the input experimental spectra– Self-explaining.

Sequest Mascot X!Tandem

.DTA x X X

.RAW X

.MGF X X X

.PKL X X

.PKS X

.mzData X X X

.mzXML X X X

.mzML X

Common parameters

• Taxon, and database– Self-explaining.– E.g. samples form human cells should be queried

against human protein database.– Sometimes Protein Sequence libraries are

available.

Common parameters

• Parent mass tolerance• If it is much smaller than the optimal would be:

– the correct peptide can be eliminated from the search space

– Execution timedecreases 100 200 300 400 500 600 700 800 900 1000 1100

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b1 b2 b3 b4 b5 b6 b7 b8 b9 b10y1 y2 y3 y4 y5 y6 y7 y8 y9 y10

Spectra comparison |)()(| tPMqPM

Common parameters

• Parent mass tolerance• If it is much bigger than the optimal would be:

– decreases the significance of the scores, – makes execution time longer

Spectra comparison

T hscore

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B

probability distributionof random scores

probability distributionof correct scores

p-value of hit h

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Common parameters

• Parent mass tolerance• Usually is around 1Da.

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Spectra comparison |)()(| tPMqPM

Common parameters

• Fragment ion match tolerance– Depends on the instrument accuracy.– If it is mach small than

the optimum:matches will be lost

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Common parameters

• Fragment ion match tolerance– If it is much smaller than the optimal would be:

Correct matched peaks will be lost.Increases the FDR, increases the false negatives, decreases the sensitivity,

T hscore

Freq

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p-value of hit h

T hscore

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p-value of hit h

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Common parameters

• If the fragment ion match tolerance is much bigger than the optimal would be:– Many theoretical peaks will match to an

experimental peak– Increases the random scores and it decreases the

statistical significance

Common parameters

Fragment ion tolerance (T)T = 0.4Da (correct) T = 0.05Da (too small) T = 2.0Da (too large)

Fragment ion tolerance (T)T = 0.4 (correct) T = 0.05 (too small) T = 2.0 (too large)

217 proteins 713 homologs930 proteins



Common parameters

• Instrument– Some database search software's allow you to

select the type of the instruments like ESI QUAD or Quad-TOF

– This fine-tunes the search engine according to which fragment ion series will be used for scoring.

– E.g.: Immonium ions, a series ions, b-, c-, x-, a-NH3,z+H series, y-H2O etc.

Common parameters

• Enzyme,– the enzyme used for enzymatic digestion in the

biological sample preparation.– This will be used for the in silico digestion of

protein sequences for peptide generation.

Common parameters

• E-value cut off

T hscore

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p-value of hit h

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Common parameters

• Ion mass search type– Monoisotopic (default)

• More accurate,

– Average• Might need larger fragment ion tolerance,

Common parameters

• Charge state– Too high charge state increases the FDR.

T hscore

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p-value of hit h

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Common parameters

• Decoy search– Includes reversed dataset in the peptide

identification.– Provides more accurate p-value and FDR estimation– Can double the search time

Common parameters• Error tolerant search. Large number of spectra

remain without significant score. Reasonable number of fragment ion peaks might have not match.– Underestimated mass measurement error (should be

seen in peptide view graphs,– Incorrect determination of precursor charge state– Peptide sequence is not in the database.– Missed cleavage & unexpected cleavage,– Unexpected chemical & post-translational

modification.

Scores:13. 156. 41. 49. 34. 33. 27. 211. 28. 110. 12. 15. 112. 1

Input dataExperimental Spectra

Protein sequence DB

Score: 4Peptide: AELDLNMTR

Score: 32Peptide: SHLITLLLFLFHSETICR

Score: 3Peptide: MEICRGLRScore: 15Peptide: LLHGDPGEEDKScore: 4Peptide: MDHPEDESHSEKScore: 5Peptide: SAEDLEADK

Score: 3Peptide: SIEAKLTLR

Input data Peptide assignment Validation

Protein inference

Quantitation

Interpretation

Cn=(32-4)/32=0.875

Cn=(4-4)/4=0

Cn=(3-3)/3=0

Cn=(15-4)/15=0.733

Keep the peptide assignment that exceeds a certain limit.

>IPI:IPI00000044.1|SWISS-PROT:P01127MNRTFGQVVARLVSAEGDPIPEELYEMLSDHSIRSFDDLQRLLHGDPGEEDKAELDLNMTRSHSGGELESLARGRRSLGSLTIAEPAMIAECKTRTEVFEISRRLIDRTNANFLVWPPCVEVQRCSGCCNNRNVQCRPTQVQLRPVQVRKIEIVRKKPIFKKATVTLEDHLACKCETVAAARPVTRSPGGSQEQRAKTPQTRVTIRTVRVRRPPKGKHRKFKHTHDKTALKETLGA


Scores:1. 2

Input data Peptide assignment

Validation Protein inference

Quantitation

Interpretation

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Spectra comparison:

Protein sequence DB

TFGQVVAR FGQVVAR GQVVAR QVVAR VVAR VAR AR TFGQVVA TFGQVV TFGQV TFGQ TFG TF

Unexpected cleavages

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>IPI:IPI00000044.1|SWISS-PROT:P01127MNRCWALFLSLCCYLRLVSAEGDPIPEELYEMLSDHSIRSFDDLQRLLHGDPGEEDKAELDLNMTRSHSGGELESLARGRRSLGSLTIAEPAMIAECKTRTEVFEISRRLIDRTNANFLVWPPCVEVQRCSGCCNNRNVQCRPTQVQLRPVQVRKIEIVRKKPIFKKATVTLEDHLACKCETVAAARPVTRSPGGSQEQRAKTPQTRVTIRTVRVRRPPKGKHRKFKHTHDKTALKETLGA

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Scores:1. 2



Quantitation

Interpretation

Spectra comparison:

Protein sequence DB

Missed cleavages

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Scores:1.22.2



Quantitation

Interpretation

Spectra comparison:

Protein sequence DB

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Scores:1.22.23.1



Quantitation

Interpretation

Spectra comparison:

Protein sequence DB

Missed cleavages

Common parameters

• Automatic error tolerant search.• Chemical and Post-Translational Modifications

(PTMs)• Fixed modification (simply modifies the mass of

the Amino Acid)• Variable modifications (can modify the mass)

• Search engines iteratively insert all combination of the possible PTMs.

Common parameters

• Automatic error tolerant search.

– more peptides can be indentified.– enlarges the search space much more

• Increases the execution time• Decreases the statistical significance, increases the

FDR.

Common parameters• Automatic error tolerant search.• In order to reduce the search space two pass

approach is applied.– 1st pass:

• Identification of perfect peptides (no PTMs, perfect digestion)

– 2nd pass:• Pass the proteins whose one of the peptides was identified

in the 1st pass.• Extensive search in the reduced protein sequence, including

missed and unexpected cleavage, PTMs, point mutations, etc.

Common parameters

• Output parameters– Mainly about formatting the results files. What

and how many details want to see.

Common parameters

• Other program specific parameters. • Different for X!tandem, Mascot, Sequest, etc.

X!Tandem

Outputs – Browsing the results

OMSSA’s search engine

OMSSA’s output

OMSSA’s result

• Good spectrum, good score, bad annotation– Rare if the p-value is significant

• Good spectrum, bad score, bad annotation– Peptide might be modified, non-perfect digestion,

not in the database.

• Bad spectrum, bad score, bad annotation

• Good spectrum, good score, good annotation

Trans-Proteomic Pipeline (TPP)• Trans-Proteomic Pipeline (TPP) is a data analysis

pipeline for the analysis of LC/MS/MS proteomics data. • TPP includes modules for validation of database search

results, quantitation of isotopically labeled samples, and validation of protein identifications, as well as tools for viewing raw LC/MS data, peptide identification results, and protein identification results.

• The XML backbone of this pipeline enables a uniform analysis for LC/MS/MS data generated by a wide variety of mass spectrometer types, and assigned peptides using a wide variety of database search engines.

Trans-Proteomic Pipeline (TPP)

Summary

• Protein identification from MS/MS data is not a black box.

• Always look at the results and understand how it

Common parameters

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