Page QuantProtP4

This is the project page of the quantitative proteomics group.

Students

Mail an alle Gruppenmitglieder: AA2010SS-QuantProt bei lists.spline.de

Name email
Christian Hoppe christian.hoppe@fu-berlin.de
Kersten Döring kdoering@zedat.fu-berlin.de
Stefan Mendt mendtste@zedat.fu-berlin.de
Christoph Standfuß christoph.standfuss@inf.fu-berlin.de
Peter Moor moor@inf.fu-berlin.de
Maria Kruegle mariadg@zedat.fu-berlin.de
Anna Kosenko annakriv@zedat.fu-berlin.de
Matthias Kluge mkluge@inf.fu-berlin.de
Felix Mattes mattesf@inf.fu-berlin.de

Literature

Initial reading (this should be read by all)

In this paper you find an overview of label-free quantitative approaches. In addition to label free approaches there are numerous labeling techniques.

Apart from quantitation, peptide and protein ID are another important topic, which we will discuss. Most database search engines are based on the spectrum graph, which was initially introduced by Dancik et al in this paper.

For the core lecture you should read the Dancik paper.

Further reading

Here you find material that is connected in the quantitative analysis of proteomics data.

Exercises

Exercise on peak detection

The project

Proposal outline

  • Signal processing
    • Overview - low-level preprocessing
    • Precision, accuracy and resolution
    • Peak picking
    • Isotopic patterns

  • Peptide feature detection
    • Alignment of LC-MS data
    • database approaches

  • Peptid ID
    • de novo sequencing
    • spectrum graph

Analysis/Programming Projects

Possible projects

Please note, that if you are interested in one of those we still have to work out the details. These are some ideas.

  1. Comparing elution profiles [Bielow](Details) (Stefan, Peter) Elution profiles of features are usually roughly gaussian shaped and correlate if they stem from the same analyte. This can happen due to multiple charge variants as observed in ESI or adduction of salt ions. The goal of this project is to compare elution profiles based a correlation measure, given a list of corresponding features.
  2. emPAI Status: finished [Andreotti](Details) (Anna) The "exponentially modified protein abundance index" can be used to quantify proteins based on the peptides that were observed. This also involves prediction of "proteotypic" peptides (the ones that ionize well) using machine learning techniques. (http://www.ncbi.nlm.nih.gov/pubmed/15958392)
  3. Simulation of PTM's Status: finished [Bielow](Christian,Kersten) (Details) AMS 3.0 is a software using artificial neural networks and voting to predict modification sites. Predicting PTMs in simulated experiments would add another dimension of realism to the results. (http://www.biomedcentral.com/1471-2105/11/210/abstract)
  4. Mass fingerprinting [Bielow](Details)(Christoph) :To get a feeling for how "unique" a certain peptide mass is, try to digest (in-silico) a proteome (e.g. from human) using trypsin. How many peptides are unique to a single protein? Given a mass precision of x ppm, how many peptides are uniquely identifyable? What happens we additionally allow 1 or 2 PTMs? ...
  5. Baseline estimation [Bielow](Matthias) One of the crucial steps in MALDI data processing, an algorithm by Williams et al seems promising (http://portal.acm.org/citation.cfm?id=1167394)

 

Topic attachments
I Attachment Action Size Date Who Comment
Chen_et_al._2001_dynamic_programming_approach_to_de_novo_peptide_sequencing_via_tandem_mass_spectrometry.pdfpdf Chen_et_al._2001_dynamic_programming_approach_to_de_novo_peptide_sequencing_via_tandem_mass_spectrometry.pdf manage 670 K 23 Apr 2010 - 09:11 SandroAndreotti Dynamic programming algorithm for spectrum graph
Fischer_et_al._2005_NovoHMM_a_hidden_Markov_model_for_de_novo_peptide_sequencing.pdfpdf Fischer_et_al._2005_NovoHMM_a_hidden_Markov_model_for_de_novo_peptide_sequencing.pdf manage 143 K 23 Apr 2010 - 09:15 SandroAndreotti peptide identification with hidden markov model
J_Comput_Biol_1999_DancikDe_novo_peptide_sequencing_via.pdfpdf J_Comput_Biol_1999_DancikDe_novo_peptide_sequencing_via.pdf manage 699 K 24 Mar 2010 - 08:21 KnutReinert Spectrum graph
J_Comput_Biol_2003_LuA_suboptimal_algorithm_for_de.pdfpdf J_Comput_Biol_2003_LuA_suboptimal_algorithm_for_de.pdf manage 222 K 24 Mar 2010 - 15:38 KnutReinert Extension of Chen's dynamic program for suboptimal solutions
Pacific_Symposium_on_Biocomputing_Pacific_Symposium_on_Biocomputing_2006_LangeHigh-accuracy_peak_picking_of_proteomics.pdfpdf Pacific_Symposium_on_Biocomputing_Pacific_Symposium_on_Biocomputing_2006_LangeHigh-accuracy_peak_picking_of_proteomics.pdf manage 274 K 24 Mar 2010 - 15:42 KnutReinert Peak Picking wavelet
Proteomics_2008_AmericaComparative_LC-MS_A_landscape_of.pdfpdf Proteomics_2008_AmericaComparative_LC-MS_A_landscape_of.pdf manage 722 K 24 Mar 2010 - 07:50 KnutReinert Comparative LC-MS
Tanner_et_al._2005_InsPecT_identification_of_posttranslationally_modified_peptides_from_tandem_mass_spectra.pdfpdf Tanner_et_al._2005_InsPecT_identification_of_posttranslationally_modified_peptides_from_tandem_mass_spectra.pdf manage 490 K 16 Jun 2010 - 14:15 SandroAndreotti Peptide ID INSPECT paper
exercise_peakpick.pdfpdf exercise_peakpick.pdf manage 85 K 18 May 2010 - 07:16 UnknownUser First exercise on peak detection
isotope-distribution.pdfpdf isotope-distribution.pdf manage 382 K 24 Mar 2010 - 15:37 KnutReinert Isotope distribution and mass decomposition
peptide-id-inspect.pdfpdf peptide-id-inspect.pdf manage 73 K 24 Mar 2010 - 15:39 KnutReinert Peptide identification, Inspect
peptide-id-scope.pdfpdf peptide-id-scope.pdf manage 231 K 24 Mar 2010 - 15:40 KnutReinert Peptide identification, Scope
proteomics-ms-overview.pdfpdf proteomics-ms-overview.pdf manage 777 K 24 Mar 2010 - 15:34 KnutReinert Proteomics Overview
signal-processing.pdfpdf signal-processing.pdf manage 1 MB 24 Mar 2010 - 15:40 KnutReinert Signal processing for proteomics
uebung.pdfpdf uebung.pdf manage 119 K 08 Jun 2010 - 09:19 UnknownUser excercise 2 for proteomics
Topic revision: r38 - 28 Jul 2010, ChrisBielow
 
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