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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.

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Related in: MedlinePlus

Synthetic gels. (a) SAP of the synthetic gel, (b) Gel1 image, (c) raw spot list from Gel1, (d) rejected spots from Gel1 and (e) SAP-related spots of Gel1.
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Figure 5: Synthetic gels. (a) SAP of the synthetic gel, (b) Gel1 image, (c) raw spot list from Gel1, (d) rejected spots from Gel1 and (e) SAP-related spots of Gel1.

Mentions: The analysis showed that 152 SAP contained several spots from the same gels. This suggests that for a given SAP, gels where single spots are found have a lower resolution than gels with duplicated spots in the corresponding SAP (for instance, see spots c2 and c3 of Figure 2). As a consequence, the resolution of a specific spot of a low resolution gel could be enhanced by using the corresponding spot from a better resolution gel. This set of heterogeneous SAP is provided to the user to allow specific analysis. Our software provided a synthetic gel which corresponds to all the SAP found among all the gels which have been identified (Figure 5a) with the algorithm. Figure 5 shows the raw spots from Gel1 before edge reduction (Figure 5c) built from the Gel 1 image (Figure 5b) and the difference between the SAP-related spots from Gel 1 (Figure 5d) and the rejected spots (Figure 5e). When we compared the percentage of the volume of the SAP-related spots in the synthetic gel to that of the rejected spots for Gel 1, we observed an average conservation of 80% (Figure 6) of the original signal. Indeed, out of the 947 spots of Gel1 (100% of volume), 717 spots (88% of volume) were related to a SAP of the synthetic gel. The rejected spots, which represented on average the remaining 12% of the spots, were considered as ambiguous signals (see rejected spots set for Gel1 in figure 5d).


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)

Synthetic gels. (a) SAP of the synthetic gel, (b) Gel1 image, (c) raw spot list from Gel1, (d) rejected spots from Gel1 and (e) SAP-related spots of Gel1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Synthetic gels. (a) SAP of the synthetic gel, (b) Gel1 image, (c) raw spot list from Gel1, (d) rejected spots from Gel1 and (e) SAP-related spots of Gel1.
Mentions: The analysis showed that 152 SAP contained several spots from the same gels. This suggests that for a given SAP, gels where single spots are found have a lower resolution than gels with duplicated spots in the corresponding SAP (for instance, see spots c2 and c3 of Figure 2). As a consequence, the resolution of a specific spot of a low resolution gel could be enhanced by using the corresponding spot from a better resolution gel. This set of heterogeneous SAP is provided to the user to allow specific analysis. Our software provided a synthetic gel which corresponds to all the SAP found among all the gels which have been identified (Figure 5a) with the algorithm. Figure 5 shows the raw spots from Gel1 before edge reduction (Figure 5c) built from the Gel 1 image (Figure 5b) and the difference between the SAP-related spots from Gel 1 (Figure 5d) and the rejected spots (Figure 5e). When we compared the percentage of the volume of the SAP-related spots in the synthetic gel to that of the rejected spots for Gel 1, we observed an average conservation of 80% (Figure 6) of the original signal. Indeed, out of the 947 spots of Gel1 (100% of volume), 717 spots (88% of volume) were related to a SAP of the synthetic gel. The rejected spots, which represented on average the remaining 12% of the spots, were considered as ambiguous signals (see rejected spots set for Gel1 in figure 5d).

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