Postal Address:
Functional Genomics Center Zurich
Winterthurerstrasse 190 /Y32H50
CH-8057 Zurich
Location:
47°23'54'' N / 08°32'59'' E 505.9
NN
Voice:
+41-44-63-53 910
E-mail:
| Christian Panse |
|
|
Postal Address: Location: |
Voice: E-mail: |
| Present |
R code(3.4K)
Label-free methods are an attractive alternative to labeling
approaches for proteomics researchers seeking for accurate
quantitative results we evaluated several open-source analysis
tools in terms of performance on two reference data sets,
explicitly generated for this purpose. In our publication we
present an implementation, tNpq (top N protein quantification), of the method
suggested by Silva and colleagues (PMID: 16219938) for
LC-MS(E) applications and we demonstrate its applicability to data
generated on FT-ICR instruments acquiring in data dependent
acquisition (DDA) mode. PMID:
20576481
The trellis plot visualizes the elution of peptides of three
proteins over five fractions. It depicts that one single peptide
does not elute over all fractions.
svg(100K)
Due to the advent of accurate and fast sampling mass spectrometers, proteomic experiments often contain thousands of peptide fragmentation spectra. Although it is commonly accepted that no manual validation of individual spectra in such experiments is feasible, annotated spectra of the peptides assignments with their modifications are required for publication and reviewing purposes. Here we demonstrate a software application called peakplot that retrospectively labels the spectra from a peptide sequence assignments by the Mascot search algorithm with the appropriate fragment ion labels. The application uses Perl for the manipulation of data and R for label heuristics and plotting. The standard output in PDF. peakplot greatly facilitates the visualisation of peptide fragmentation spectra and aides with the quality assessment of modification sites such as phosphorylation. rejected PROTEOMICS Technical Brief PDF(438K)
| Past |
animation(2867K)
We introduce an intuitive and effective novel approach for projection-based similarity visualization for interactive discrimination analysis, data exploration, and visual evaluation of metric space effectiveness. The approach is based on the convex hull metaphor for visually aggregating sets of points in projected space, and it can be used with a variety of different projection techniques. The effectiveness of the approach is demonstrated by application on two well-known data sets. Statistical evidence supporting the validity of the hull metaphor is presented. We advocate the hull-based approach over the standard symbol-based approach to projection visualization, as it allows a more effective perception of similarity relationships and class distribution characteristics. PDF (4858K)
Technical Specification:
Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In our cartogram project, we deal with the contiguous cartogram problem which strictly retains the topology of the polygon mesh. We develop an algorithm to solve the problem which uses an iterative relocation of the vertices based on a modified medial axes transformation of the polygon mesh. Experiments using real data sets show that our algorithm is capable of producing high-quality cartograms in interactive time even for very large polygon meshes. A number of application examples show the high potential of our algorithm. (see also: Cartogram Central)
| Lectures |
Algorithms for Massive Data - WS2005/2006, UKN
Information Visualization - SS2005, UKN
UNIX and Shell Scripts - SS2005, UKN
| Selected Publication [DBLP PubMed Google Patent Search] |
