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A low-cost library construction protocol and data analysis pipeline for Illumina-based strand-specific multiplex RNA-seq.

Wang L, Si Y, Dedow LK, Shao Y, Liu P, Brutnell TP - PLoS ONE (2011)

Bottom Line: Our data supports novel gene models and can be used to improve current rice genome annotation.Additionally, using the rice transcriptome data, we compared different methods of calculating gene expression and discuss the advantages of a strand-specific approach to detect bona-fide anti-sense transcripts and to detect intron retention events.Our results demonstrate the potential of this low cost and robust method for RNA-seq library construction and data analysis.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, New York, United States of America.

ABSTRACT
The emergence of NextGen sequencing technology has generated much interest in the exploration of transcriptomes. Currently, Illumina Inc. (San Diego, CA) provides one of the most widely utilized sequencing platforms for gene expression analysis. While Illumina reagents and protocols perform adequately in RNA-sequencing (RNA-seq), alternative reagents and protocols promise a higher throughput at a much lower cost. We have developed a low-cost and robust protocol to produce Illumina-compatible (GAIIx and HiSeq2000 platforms) RNA-seq libraries by combining several recent improvements. First, we designed balanced adapter sequences for multiplexing of samples; second, dUTP incorporation in 2(nd) strand synthesis was used to enforce strand-specificity; third, we simplified RNA purification, fragmentation and library size-selection steps thus drastically reducing the time and increasing throughput of library construction; fourth, we included an RNA spike-in control for validation and normalization purposes. To streamline informatics analysis for the community, we established a pipeline within the iPlant Collaborative. These scripts are easily customized to meet specific research needs and improve on existing informatics and statistical treatments of RNA-seq data. In particular, we apply significance tests for determining differential gene expression and intron retention events. To demonstrate the potential of both the library-construction protocol and data-analysis pipeline, we characterized the transcriptome of the rice leaf. Our data supports novel gene models and can be used to improve current rice genome annotation. Additionally, using the rice transcriptome data, we compared different methods of calculating gene expression and discuss the advantages of a strand-specific approach to detect bona-fide anti-sense transcripts and to detect intron retention events. Our results demonstrate the potential of this low cost and robust method for RNA-seq library construction and data analysis.

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Distribution of reads mapping to expressed and non-expressed genes.(a) Distribution of sense alignments and (b) anti-sense alignments. The RPKM values were calculated from three replicates. The frequency shows the number of gene models per bin (vertical bars). A smoothed curve is plotted. Genes with average RPKM equal to zero are not shown in the histograms.
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pone-0026426-g007: Distribution of reads mapping to expressed and non-expressed genes.(a) Distribution of sense alignments and (b) anti-sense alignments. The RPKM values were calculated from three replicates. The frequency shows the number of gene models per bin (vertical bars). A smoothed curve is plotted. Genes with average RPKM equal to zero are not shown in the histograms.

Mentions: For SS-derived data, we detected 34,455 sense transcripts and the corresponding false discovery rate (FDR) was estimated as 0.6%. The distribution of estimated gene expression values are shown in Figure 7A and indicates that an RPKM value of approximately 0.3 or greater is sufficient for defining a gene as “expressed”. This is reasonable since a value of 0.3 corresponds to approximately 32 reads aligning to an average 1.1 kb gene model from a combination of approximately 90 million mapped reads.


A low-cost library construction protocol and data analysis pipeline for Illumina-based strand-specific multiplex RNA-seq.

Wang L, Si Y, Dedow LK, Shao Y, Liu P, Brutnell TP - PLoS ONE (2011)

Distribution of reads mapping to expressed and non-expressed genes.(a) Distribution of sense alignments and (b) anti-sense alignments. The RPKM values were calculated from three replicates. The frequency shows the number of gene models per bin (vertical bars). A smoothed curve is plotted. Genes with average RPKM equal to zero are not shown in the histograms.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198403&req=5

pone-0026426-g007: Distribution of reads mapping to expressed and non-expressed genes.(a) Distribution of sense alignments and (b) anti-sense alignments. The RPKM values were calculated from three replicates. The frequency shows the number of gene models per bin (vertical bars). A smoothed curve is plotted. Genes with average RPKM equal to zero are not shown in the histograms.
Mentions: For SS-derived data, we detected 34,455 sense transcripts and the corresponding false discovery rate (FDR) was estimated as 0.6%. The distribution of estimated gene expression values are shown in Figure 7A and indicates that an RPKM value of approximately 0.3 or greater is sufficient for defining a gene as “expressed”. This is reasonable since a value of 0.3 corresponds to approximately 32 reads aligning to an average 1.1 kb gene model from a combination of approximately 90 million mapped reads.

Bottom Line: Our data supports novel gene models and can be used to improve current rice genome annotation.Additionally, using the rice transcriptome data, we compared different methods of calculating gene expression and discuss the advantages of a strand-specific approach to detect bona-fide anti-sense transcripts and to detect intron retention events.Our results demonstrate the potential of this low cost and robust method for RNA-seq library construction and data analysis.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, New York, United States of America.

ABSTRACT
The emergence of NextGen sequencing technology has generated much interest in the exploration of transcriptomes. Currently, Illumina Inc. (San Diego, CA) provides one of the most widely utilized sequencing platforms for gene expression analysis. While Illumina reagents and protocols perform adequately in RNA-sequencing (RNA-seq), alternative reagents and protocols promise a higher throughput at a much lower cost. We have developed a low-cost and robust protocol to produce Illumina-compatible (GAIIx and HiSeq2000 platforms) RNA-seq libraries by combining several recent improvements. First, we designed balanced adapter sequences for multiplexing of samples; second, dUTP incorporation in 2(nd) strand synthesis was used to enforce strand-specificity; third, we simplified RNA purification, fragmentation and library size-selection steps thus drastically reducing the time and increasing throughput of library construction; fourth, we included an RNA spike-in control for validation and normalization purposes. To streamline informatics analysis for the community, we established a pipeline within the iPlant Collaborative. These scripts are easily customized to meet specific research needs and improve on existing informatics and statistical treatments of RNA-seq data. In particular, we apply significance tests for determining differential gene expression and intron retention events. To demonstrate the potential of both the library-construction protocol and data-analysis pipeline, we characterized the transcriptome of the rice leaf. Our data supports novel gene models and can be used to improve current rice genome annotation. Additionally, using the rice transcriptome data, we compared different methods of calculating gene expression and discuss the advantages of a strand-specific approach to detect bona-fide anti-sense transcripts and to detect intron retention events. Our results demonstrate the potential of this low cost and robust method for RNA-seq library construction and data analysis.

Show MeSH
Related in: MedlinePlus