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Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.

van Wieringen WN, Unger K, Leday GG, Krijgsman O, de Menezes RX, Ylstra B, van de Wiel MA - BMC Bioinformatics (2012)

Bottom Line: Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl

ABSTRACT

Background: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.

Results: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.

Conclusions: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Show MeSH
Overlap matching: the DNA copy number feature with the maximum percentage of overlap in sequence with the gene is sought. Here feature j-1 overlaps with the gene (indicated by the horizontal solid arrow), whereas features j and j + 1 do not.
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Figure 4: Overlap matching: the DNA copy number feature with the maximum percentage of overlap in sequence with the gene is sought. Here feature j-1 overlaps with the gene (indicated by the horizontal solid arrow), whereas features j and j + 1 do not.

Mentions: Instead of the distance between two features, the percentage of overlap between them may be employed to match the features from two platforms. A gene is matched to that DNA copy number feature with which it has the highest percentage of overlap. Among others De Menezes et al. [14] have used this approach. Table 4 describes the steps of the approach, while Figure 4 visualizes the key problem.


Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.

van Wieringen WN, Unger K, Leday GG, Krijgsman O, de Menezes RX, Ylstra B, van de Wiel MA - BMC Bioinformatics (2012)

Overlap matching: the DNA copy number feature with the maximum percentage of overlap in sequence with the gene is sought. Here feature j-1 overlaps with the gene (indicated by the horizontal solid arrow), whereas features j and j + 1 do not.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475006&req=5

Figure 4: Overlap matching: the DNA copy number feature with the maximum percentage of overlap in sequence with the gene is sought. Here feature j-1 overlaps with the gene (indicated by the horizontal solid arrow), whereas features j and j + 1 do not.
Mentions: Instead of the distance between two features, the percentage of overlap between them may be employed to match the features from two platforms. A gene is matched to that DNA copy number feature with which it has the highest percentage of overlap. Among others De Menezes et al. [14] have used this approach. Table 4 describes the steps of the approach, while Figure 4 visualizes the key problem.

Bottom Line: Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl

ABSTRACT

Background: An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms.

Results: We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes.

Conclusions: Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Show MeSH