Difference between revisions of "VIZBI2010"

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'''Oliver Kohlbacher'''
 
'''Oliver Kohlbacher'''
 
From Spectra to Networks - Visualizing Proteomics Data
 
From Spectra to Networks - Visualizing Proteomics Data
Shotgun proteomics means fragmenting proteins using enzymes (e.g., trypsin), then separate using mass spectrometry. Tandom-MS the first separation is via mass, then each peak is further broken down using direct collisions (collision-induced dissociation (CID). This enables determination of the sequence.
+
Again, very clear into to proteomics methodology. Shotgun proteomics means fragmenting proteins using enzymes (e.g., trypsin), then separate using mass spectrometry. Tandom-MS the first separation is via mass, then each peak is further broken down using direct collisions (collision-induced dissociation (CID). This enables determination of the sequence.
 +
 
 +
2M maps are obtains: one dimension is charge/mass ratio, the other is retention time.
 +
 
 +
Role of visualization in proteomics: quality, manual/low-throughput analysis; validate automatic analyses (this is where the field is heading, more automation).
 +
 
 +
Primarily visualization is mass spectra themselves > signal process reduces them to 'stick' spectra (reduce data size by an order of magnitude).
 +
 
 +
2D mass spectra - one of the problems is simply getting them into memory: they are up to 200GB.
 +
 
 +
Question: is that even with the 'stick' specrta?
 +
 
 +
A key problem is lack of data standards.
 +
 
 +
One dimension/data volume reduction is to fit the spectra to a mathematical model, then you can replace the data by the model.
 +
 
 +
Retention time and mass (the two primary dimensions) do not have a 'biological' meaning.
 +
 
 +
Can compare two samples (e.g., disease vs healthy tissue), can create expression profiles that are similar to gene expression profiles.
 +
 
 +
Key challenges: data volume (hence need data reduction); however, experimentalists always need to go back to the raw data/spectra; integration with other omics data and networks; rapidly changing experimental techniques (difficult to keep up).
  
 
=== Posters 'T' ===
 
=== Posters 'T' ===

Revision as of 08:58, 4 March 2010

Home < VIZBI2010
This wiki page can be used to provide supplemental information, links, and discussion for topics covered in the VIZBI 2010 conference in Heidelberg March 3-5, 2010 at the EMBL.
Heidelberg
Source: Heidelberg_corr.jpg

VIZBI Links

Special Issue of Nature Methods

The speakers collaborated on a set of papers summarizing the current state of bioimaging visualization that were published as a special issue of Nature Methods.

Comments on friendfeed

Community notes are available on friendfeed: http://friendfeed.com/vizbi2010

Wednesday

MRI

Posters 'W'

Optical Microscopy

Keynote

Thursday

Systems Biology

Matt Hibbs Matt gave a beautifully clear into to expression array analysis. He also discussed his own tool HIDRA enables comparison of several heat maps, each from different experiments.

Oliver Kohlbacher From Spectra to Networks - Visualizing Proteomics Data Again, very clear into to proteomics methodology. Shotgun proteomics means fragmenting proteins using enzymes (e.g., trypsin), then separate using mass spectrometry. Tandom-MS the first separation is via mass, then each peak is further broken down using direct collisions (collision-induced dissociation (CID). This enables determination of the sequence.

2M maps are obtains: one dimension is charge/mass ratio, the other is retention time.

Role of visualization in proteomics: quality, manual/low-throughput analysis; validate automatic analyses (this is where the field is heading, more automation).

Primarily visualization is mass spectra themselves > signal process reduces them to 'stick' spectra (reduce data size by an order of magnitude).

2D mass spectra - one of the problems is simply getting them into memory: they are up to 200GB.

Question: is that even with the 'stick' specrta?

A key problem is lack of data standards.

One dimension/data volume reduction is to fit the spectra to a mathematical model, then you can replace the data by the model.

Retention time and mass (the two primary dimensions) do not have a 'biological' meaning.

Can compare two samples (e.g., disease vs healthy tissue), can create expression profiles that are similar to gene expression profiles.

Key challenges: data volume (hence need data reduction); however, experimentalists always need to go back to the raw data/spectra; integration with other omics data and networks; rapidly changing experimental techniques (difficult to keep up).

Posters 'T'

Sequences and Genomes

Friday

Macromolecular Structures

Posters 'F'

Alignments and Phylgenies