Limits...
A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics.

Pérès S, Molina L, Salvetat N, Granier C, Molina F - BMC Bioinformatics (2008)

Bottom Line: The results are returned under various forms (clickable synthetic gel, text file, etc.).We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.It is very useful for typical clinical proteomic studies with large number of experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr

ABSTRACT

Background: In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.

Results: We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.

Conclusion: Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.

Show MeSH

Related in: MedlinePlus

Graph representations. (a) raw graph (before treatment) and (b) treated graph (after treatment). Graphs were represented with the Tulip software [24]. The raw graph is composed of good SAP (3a, bottom left panel) and noisy SAP (3a, bottom right panel)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2628390&req=5

Figure 3: Graph representations. (a) raw graph (before treatment) and (b) treated graph (after treatment). Graphs were represented with the Tulip software [24]. The raw graph is composed of good SAP (3a, bottom left panel) and noisy SAP (3a, bottom right panel)

Mentions: Nodes are labelled with the name of the gel and the number of the spot. Edges are labelled with their weight. SAP are represented in the graph by high density zones, i.e. zones where a lot of nodes are pairwise adjacent (Figure 3a bottom left panel). Most of the time, there are many associations that make the graph highly incorrectly connected (Figure 3a top panel). It is therefore necessary to clean the graph to find the sets of nodes which represent the same spots. This is done by removing the edges which represent wrong connexions.


A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics.

Pérès S, Molina L, Salvetat N, Granier C, Molina F - BMC Bioinformatics (2008)

Graph representations. (a) raw graph (before treatment) and (b) treated graph (after treatment). Graphs were represented with the Tulip software [24]. The raw graph is composed of good SAP (3a, bottom left panel) and noisy SAP (3a, bottom right panel)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2628390&req=5

Figure 3: Graph representations. (a) raw graph (before treatment) and (b) treated graph (after treatment). Graphs were represented with the Tulip software [24]. The raw graph is composed of good SAP (3a, bottom left panel) and noisy SAP (3a, bottom right panel)
Mentions: Nodes are labelled with the name of the gel and the number of the spot. Edges are labelled with their weight. SAP are represented in the graph by high density zones, i.e. zones where a lot of nodes are pairwise adjacent (Figure 3a bottom left panel). Most of the time, there are many associations that make the graph highly incorrectly connected (Figure 3a top panel). It is therefore necessary to clean the graph to find the sets of nodes which represent the same spots. This is done by removing the edges which represent wrong connexions.

Bottom Line: The results are returned under various forms (clickable synthetic gel, text file, etc.).We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.It is very useful for typical clinical proteomic studies with large number of experiments.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr

ABSTRACT

Background: In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.

Results: We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.

Conclusion: Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.

Show MeSH
Related in: MedlinePlus