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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

Pipeline for processing the coverages. The data from a short read sequencer may be mapped by any mapper and processed into BAM files with known genomic annotation. Then, using the Bioconductor libraries RSamtools and rnaSeqMap, they are processed as coverage profiles using generators of modifications, normalizations and statistical measures. Finally, the output of the measures and their matching degeneration levels are checked using correlations and ROC curves.
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gkr1249-F2: Pipeline for processing the coverages. The data from a short read sequencer may be mapped by any mapper and processed into BAM files with known genomic annotation. Then, using the Bioconductor libraries RSamtools and rnaSeqMap, they are processed as coverage profiles using generators of modifications, normalizations and statistical measures. Finally, the output of the measures and their matching degeneration levels are checked using correlations and ROC curves.

Mentions: Numeric experiments have been conducted using synthetic, semi-synthetic and real data (Figure 2). In all the cases, a combination of normalization and statistical measure has been tested. In the case of synthetic and semi-synthetic data, appropriate data generators, as described above, have been used. In all the cases, 3000 randomly selected exonic regions from human chromosome 1 have been analysed.Figure 1.


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)

Pipeline for processing the coverages. The data from a short read sequencer may be mapped by any mapper and processed into BAM files with known genomic annotation. Then, using the Bioconductor libraries RSamtools and rnaSeqMap, they are processed as coverage profiles using generators of modifications, normalizations and statistical measures. Finally, the output of the measures and their matching degeneration levels are checked using correlations and ROC curves.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkr1249-F2: Pipeline for processing the coverages. The data from a short read sequencer may be mapped by any mapper and processed into BAM files with known genomic annotation. Then, using the Bioconductor libraries RSamtools and rnaSeqMap, they are processed as coverage profiles using generators of modifications, normalizations and statistical measures. Finally, the output of the measures and their matching degeneration levels are checked using correlations and ROC curves.
Mentions: Numeric experiments have been conducted using synthetic, semi-synthetic and real data (Figure 2). In all the cases, a combination of normalization and statistical measure has been tested. In the case of synthetic and semi-synthetic data, appropriate data generators, as described above, have been used. In all the cases, 3000 randomly selected exonic regions from human chromosome 1 have been analysed.Figure 1.

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