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Removal of AU bias from microarray mRNA expression data enhances computational identification of active microRNAs.

Elkon R, Agami R - PLoS Comput. Biol. (2008)

Bottom Line: Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology.The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes.Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets.

View Article: PubMed Central - PubMed

Affiliation: Division of Gene Regulation, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

ABSTRACT
Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3'-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3'-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.

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Related in: MedlinePlus

The AU response bias is related to probe base composition regardless probe location along the target transcript.Probe-level M-AU plot for the comparison between two chips hybridized with a common human brain reference sample. This dataset used the new generation Affymetrix Human Gene 1.0 ST Array, in which probes are located throughout the target transcripts. We generated plots which either included all probes, or included separately only those mapped to the 5′-UTR, CDS, or 3′-UTR of the targets. (As the length of each probe is 25 bases, probe's AU content (X axis) gets only discrete values in the 0–100% range with jumps of 4%). Probes mapped to the different transcript regions exhibited similar level of AU response bias.
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pcbi-1000189-g004: The AU response bias is related to probe base composition regardless probe location along the target transcript.Probe-level M-AU plot for the comparison between two chips hybridized with a common human brain reference sample. This dataset used the new generation Affymetrix Human Gene 1.0 ST Array, in which probes are located throughout the target transcripts. We generated plots which either included all probes, or included separately only those mapped to the 5′-UTR, CDS, or 3′-UTR of the targets. (As the length of each probe is 25 bases, probe's AU content (X axis) gets only discrete values in the 0–100% range with jumps of 4%). Probes mapped to the different transcript regions exhibited similar level of AU response bias.

Mentions: In the vast majority of present chips, probes are selected from the 3′-end of target transcripts. This is also the case for the technical dataset that we have analyzed, which used the Affymetrix HGU133A chip. Therefore, as expected, we observed in this dataset also a strong relationship between probeset AU content and response (similar to the one observed between gene's 3′-UTR AU content and response) (Figure S5). To test whether the AU artifact origins either from base-composition properties of 3′-UTR of target transcripts or of that of the chip probes, the sequence of probes and target 3′-UTRs need to be uncoupled. The new generation Affymetrix chips break this coupling as their probes are selected from all regions of target transcripts. We therefore analyzed a second technical dataset, recently published by Pradervand et al. [22] which used the new Affymetrix Human Gene 1.0 ST Array. In this dataset too, we detected a strong AU response bias. That is, we observed a significant relationship between probeset AU content and response in a comparison between duplicate control chips. Importantly, carrying out a probe-level analysis, we found that probes located at 5′-UTR and CDS regions show a similar AU bias as probes located at 3′-UTRs (Figure 4). This finding indicates that the link between gene's response and 3′-UTR base composition is secondary to a more basic bias in microarray measurements which links gene response with base composition of its probes.


Removal of AU bias from microarray mRNA expression data enhances computational identification of active microRNAs.

Elkon R, Agami R - PLoS Comput. Biol. (2008)

The AU response bias is related to probe base composition regardless probe location along the target transcript.Probe-level M-AU plot for the comparison between two chips hybridized with a common human brain reference sample. This dataset used the new generation Affymetrix Human Gene 1.0 ST Array, in which probes are located throughout the target transcripts. We generated plots which either included all probes, or included separately only those mapped to the 5′-UTR, CDS, or 3′-UTR of the targets. (As the length of each probe is 25 bases, probe's AU content (X axis) gets only discrete values in the 0–100% range with jumps of 4%). Probes mapped to the different transcript regions exhibited similar level of AU response bias.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2533120&req=5

pcbi-1000189-g004: The AU response bias is related to probe base composition regardless probe location along the target transcript.Probe-level M-AU plot for the comparison between two chips hybridized with a common human brain reference sample. This dataset used the new generation Affymetrix Human Gene 1.0 ST Array, in which probes are located throughout the target transcripts. We generated plots which either included all probes, or included separately only those mapped to the 5′-UTR, CDS, or 3′-UTR of the targets. (As the length of each probe is 25 bases, probe's AU content (X axis) gets only discrete values in the 0–100% range with jumps of 4%). Probes mapped to the different transcript regions exhibited similar level of AU response bias.
Mentions: In the vast majority of present chips, probes are selected from the 3′-end of target transcripts. This is also the case for the technical dataset that we have analyzed, which used the Affymetrix HGU133A chip. Therefore, as expected, we observed in this dataset also a strong relationship between probeset AU content and response (similar to the one observed between gene's 3′-UTR AU content and response) (Figure S5). To test whether the AU artifact origins either from base-composition properties of 3′-UTR of target transcripts or of that of the chip probes, the sequence of probes and target 3′-UTRs need to be uncoupled. The new generation Affymetrix chips break this coupling as their probes are selected from all regions of target transcripts. We therefore analyzed a second technical dataset, recently published by Pradervand et al. [22] which used the new Affymetrix Human Gene 1.0 ST Array. In this dataset too, we detected a strong AU response bias. That is, we observed a significant relationship between probeset AU content and response in a comparison between duplicate control chips. Importantly, carrying out a probe-level analysis, we found that probes located at 5′-UTR and CDS regions show a similar AU bias as probes located at 3′-UTRs (Figure 4). This finding indicates that the link between gene's response and 3′-UTR base composition is secondary to a more basic bias in microarray measurements which links gene response with base composition of its probes.

Bottom Line: Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology.The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes.Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets.

View Article: PubMed Central - PubMed

Affiliation: Division of Gene Regulation, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

ABSTRACT
Elucidation of regulatory roles played by microRNAs (miRs) in various biological networks is one of the greatest challenges of present molecular and computational biology. The integrated analysis of gene expression data and 3'-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes. Applying such an integrated analysis, we uncovered a striking relationship between 3'-UTR AU content and gene response in numerous microarray datasets. We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3'-UTRs. Therefore, removal of this bias, which is in order in any analysis of microarray datasets, is of crucial importance when integrating expression data and 3'-UTR sequences to identify regulatory elements embedded in this region. We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs. Our results substantiate that, after removal of AU biases, mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions.

Show MeSH
Related in: MedlinePlus