Difference between revisions of "VIZBI2010"

From NAMIC Wiki
Jump to: navigation, search
Line 61: Line 61:
  
 
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).
 
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).
 +
 +
Key difference to gene expression profiling: visualization methods are the same, but the key difference is that with protein expression we need to go back to the raw data.
 +
 +
Uniqueness of sequence fragments: antibodies recognize proteins uniquesly with just 9 residues: 8 residues is already sufficient to have on average only one match in human.
 +
 +
"We are back to sending hard disks by mail"
  
 
=== Posters 'T' ===
 
=== Posters 'T' ===

Revision as of 09:05, 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).

Key difference to gene expression profiling: visualization methods are the same, but the key difference is that with protein expression we need to go back to the raw data.

Uniqueness of sequence fragments: antibodies recognize proteins uniquesly with just 9 residues: 8 residues is already sufficient to have on average only one match in human.

"We are back to sending hard disks by mail"

Posters 'T'

Sequences and Genomes

Friday

Macromolecular Structures

Posters 'F'

Alignments and Phylgenies