Dataset Information

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays.

ABSTRACT:

Background

Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them. The reason is that previously developed summarization methods do not work well if applied to transcripts. In addition, gene structure prediction, i.e., the correspondence between probes and novel isoforms, is a field which is still unexplored.

Results

We have modified and adapted a previous algorithm to take advantage of the special characteristics of the Affymetrix exon arrays. The structure and concentration of transcripts -some of them possibly unknown- in microarray experiments were predicted using this algorithm. Simulations showed that the suggested modifications improved both specificity (SP) and sensitivity (ST) of the predictions. The algorithm was also applied to different real datasets showing its effectiveness and the concordance with PCR validated results.

Conclusions

The proposed algorithm shows a substantial improvement in the performance over the previous version. This improvement is mainly due to the exploitation of the redundancy of the Affymetrix exon arrays. An R-Package of SPACE with the updated algorithms have been developed and is freely available.

SUBMITTER: Anton MA

PROVIDER: S-EPMC3012675 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays.

Anton Miguel A MA Aramburu Ander A Rubio Angel A

BMC bioinformatics 20101126

<h4>Background</h4>Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them. The reason is that previously developed summarization methods do not work well if applied to transcripts. In addition, gene stru ...[more]

PMID: 21110835

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Project description:BACKGROUND: Alternative splicing and isoform level expression profiling is an emerging field of interest within genomics. Splicing sensitive microarrays, with probes targeted to individual exons or exon-junctions, are becoming increasingly popular as a tool capable of both expression profiling and finer scale isoform detection. Despite their intuitive appeal, relatively little is known about the performance of such tools, particularly in comparison with more traditional 3' targeted microarrays. Here, we use the well studied Microarray Quality Control (MAQC) dataset to benchmark the Affymetrix Exon Array, and compare it to two other popular platforms: Illumina, and Affymetrix U133. RESULTS: We show that at the gene expression level, the Exon Array performs comparably with the two 3' targeted platforms. However, the interplatform correlation of the results is slightly lower than between the two 3' arrays. We show that some of the discrepancies stem from the RNA amplification protocols, e.g. the Exon Array is able to detect expression of non-polyadenylated transcripts. More importantly, we show that many other differences result from the ability of the Exon Array to monitor more detailed isoform-level changes; several examples illustrate that changes detected by the 3' platforms are actually isoform variations, and that the nature of these variations can be resolved using Exon Array data. Finally, we show how the Exon Array can be used to detect alternative isoform differences, such as alternative splicing, transcript termination, and alternative promoter usage. We discuss the possible pitfalls and false positives resulting from isoform-level analysis. CONCLUSION: The Exon Array is a valuable tool that can be used to profile gene expression while providing important additional information regarding the types of gene isoforms that are expressed and variable. However, analysis of alternative splicing requires much more hands on effort and visualization of results in order to correctly interpret the data, and generally results in considerably higher false positive rates than expression analysis. One of the main sources of error in the MAQC dataset is variation in amplification efficiency across transcripts, most likely caused by joint effects of elevated GC content in the 5' ends of genes and reduced likelihood of random-primed first strand synthesis in the 3' ends of genes. These effects are currently not adequately corrected using existing statistical methods. We outline approaches to reduce such errors by filtering out potentially problematic data.

Project description:Background:Alternative splicing and isoform level expression profiling is an emerging field of interest within genomics. Splicing sensitive microarrays, with probes targeted to individual exons or exon-junctions, are becoming increasingly popular as a tool capable of both expression profiling and finer scale isoform detection. Despite their intuitive appeal, relatively little is known about the performance of such tools, particularly in comparison with more traditional 3’ targeted microarrays. Here, we use the well studied Microarray Quality Control (MAQC) dataset to benchmark the Affymetrix Exon Array, and compare it to two other popular platforms: Illumina, and Affymetrix U133. Results:We show that at the gene expression level, the Exon Array performs comparably with the two 3’ targeted platforms. However, the interplatform correlation of the results is slightly lower than between the two 3’ arrays. We show that some of the discrepancies stem from the RNA amplification protocols, e.g. the Exon Array is able to detect expression of non-polyadenylated transcripts. More importantly, we show that many other differences result from the ability of the Exon Array to monitor more detailed isoform-level changes; several examples illustrate that changes detected by the 3’ platforms are actually isoform variations, and that the nature of these variations can be resolved using Exon Array data. Finally, we show how the Exon Array can be used to detect alternative isoform differences, such as alternative splicing, transcript termination, and alternative promoter usage. We discuss the possible pitfalls and false positives resulting from isoform-level analysis. Conclusions:The Exon Array is a valuable tool that can be used to profile gene expression while providing important additional information regarding the types of gene isoforms that are expressed and variable. However, analysis of alternative splicing requires much more hands on effort and visualization of results in order to correctly interpret the data, and generally results in considerably higher false positive rates than expression analysis. One of the main sources of error in the MAQC dataset is variation in amplification efficiency across transcripts, which is not adequately corrected using existing statistical methods. We outline approaches to reduce such errors by filtering out potentially problematic data. This SuperSeries is composed of the SubSeries listed below.

Project description:BACKGROUND: The emergence of isoform-sensitive microarrays has helped fuel in-depth studies of the human transcriptome. The Affymetrix GeneChip Human Exon 1.0 ST Array (Exon Array) has been previously shown to be effective in profiling gene expression at the isoform level. More recently, the Affymetrix GeneChip Human Gene 1.0 ST Array (Gene Array) has been released for measuring gene expression and interestingly contains a large subset of probes from the Exon Array. Here, we explore the potential of using Gene Array probes to assess expression variation at the sub-transcript level. Utilizing datasets of the high quality Microarray Quality Control (MAQC) RNA samples previously assayed on the Exon Array and Gene Array, we compare the expression measurements of the two platforms to determine the performance of the Gene Array in detecting isoform variations. RESULTS: Overall, we show that the Gene Array is comparable to the Exon Array in making gene expression calls. Moreover, to examine expression of different isoforms, we modify the Gene Array probe set definition file to enable summarization of probe intensity values at the exon level and show that the expression profiles between the two platforms are also highly correlated. Next, expression calls of previously known differentially spliced genes were compared and also show concordant results. Splicing index analysis, representing estimates of exon inclusion levels, shows a lower but good correlation between platforms. As the Gene Array contains a significant subset of probes from the Exon Array, we note that, in comparison, the Gene Array overlaps with fewer but still a high proportion of splicing events annotated in the Known Alt Events UCSC track, with abundant coverage of cassette exons. We discuss the ability of the Gene Array to detect alternative splicing and isoform variation and address its limitations. CONCLUSION: The Gene Array is an effective expression profiling tool at gene and exon expression level, the latter made possible by probe set annotation modifications. We demonstrate that the Gene Array is capable of detecting alternative splicing and isoform variation. As expected, in comparison to the Exon Array, it is limited by reduced gene content coverage and is not able to detect as wide a range of alternative splicing events. However, for the events that can be monitored by both platforms, we estimate that the selectivity and sensitivity levels are comparable. We hope our findings will shed light on the potential extension of the Gene Array to detect alternative splicing. It should be particularly suitable for researchers primarily interested in gene expression analysis, but who may be willing to look for splicing and isoform differences within their dataset. However, we do not suggest it to be an equivalent substitute to the more comprehensive Exon Array.

Dataset Information

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays.

Background

Results

Conclusions

Publications

Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays.

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