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Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage.

Okoniewski MJ, Leśniewska A, Szabelska A, Zyprych-Walczak J, Ryan M, Wachtel M, Morzy T, Schäfer B, Schlapbach R - Nucleic Acids Res. (2011)

Bottom Line: Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared.As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve.They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this.

View Article: PubMed Central - PubMed

Affiliation: Functional Genomics Center Zurich, UNI ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. michal@fgcz.ethz.ch

ABSTRACT
The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers.

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Related in: MedlinePlus

Scatterplots of MDA without normalization and MPP with density normalization against the log2 fold change and P-value from the DESeq test for the 3000 exons in the real data experiment.
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gkr1249-F8: Scatterplots of MDA without normalization and MPP with density normalization against the log2 fold change and P-value from the DESeq test for the 3000 exons in the real data experiment.

Mentions: The comparison to the count-based methods with the best performing pipelines (MDA without normalization and MPP with density normalization) is shown in the Figure 8. Although the correlation between count-based fold changes and the measures reaches 0.4 in some cases, there is no clear correspondence between the count-based methods and the studied pipelines. This proves that there is always a group of exons that will not be found as differentially expressed according to counts, but will be clearly different in terms of the shape of coverage. Examples of such exon coverages from the two real data samples are presented in the Supplementary Figure S1.Figure 8.


Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage.

Okoniewski MJ, Leśniewska A, Szabelska A, Zyprych-Walczak J, Ryan M, Wachtel M, Morzy T, Schäfer B, Schlapbach R - Nucleic Acids Res. (2011)

Scatterplots of MDA without normalization and MPP with density normalization against the log2 fold change and P-value from the DESeq test for the 3000 exons in the real data experiment.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351146&req=5

gkr1249-F8: Scatterplots of MDA without normalization and MPP with density normalization against the log2 fold change and P-value from the DESeq test for the 3000 exons in the real data experiment.
Mentions: The comparison to the count-based methods with the best performing pipelines (MDA without normalization and MPP with density normalization) is shown in the Figure 8. Although the correlation between count-based fold changes and the measures reaches 0.4 in some cases, there is no clear correspondence between the count-based methods and the studied pipelines. This proves that there is always a group of exons that will not be found as differentially expressed according to counts, but will be clearly different in terms of the shape of coverage. Examples of such exon coverages from the two real data samples are presented in the Supplementary Figure S1.Figure 8.

Bottom Line: Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared.As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve.They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this.

View Article: PubMed Central - PubMed

Affiliation: Functional Genomics Center Zurich, UNI ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. michal@fgcz.ethz.ch

ABSTRACT
The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers.

Show MeSH
Related in: MedlinePlus