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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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Related in: MedlinePlus

Data analysis pipeline.Pink boxes highlight functions that are based on genome sequence and annotation and are not dependent on experimental data. Blue boxes highlight functions executed for each experimental run.
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pone-0026426-g008: Data analysis pipeline.Pink boxes highlight functions that are based on genome sequence and annotation and are not dependent on experimental data. Blue boxes highlight functions executed for each experimental run.

Mentions: We have compiled our data analysis pipelines into an integrated package of annotated Perl and R-based modules. The source codes are easily accessible and adaptable from (http://c3c4.tc.cornell.edu/resource.aspx). It is also possible to run the script directly from iPlant website (Matt Vaughn, personal communication), providing a mechanism for community access. The overall flow of the pipeline is illustrated in Figure 8. Importantly, these scripts are well documented and can be easily modified and improved as RNA-seq technologies advance. As such, we have intentionally left the pipeline highly annotated and modular, so that modification and improvement can be easily made. Thus, this computational pipeline can serve as a useful resource that the community can improve and adapt upon and may accelerate the unification of RNA-seq data analysis.


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)

Data analysis pipeline.Pink boxes highlight functions that are based on genome sequence and annotation and are not dependent on experimental data. Blue boxes highlight functions executed for each experimental run.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0026426-g008: Data analysis pipeline.Pink boxes highlight functions that are based on genome sequence and annotation and are not dependent on experimental data. Blue boxes highlight functions executed for each experimental run.
Mentions: We have compiled our data analysis pipelines into an integrated package of annotated Perl and R-based modules. The source codes are easily accessible and adaptable from (http://c3c4.tc.cornell.edu/resource.aspx). It is also possible to run the script directly from iPlant website (Matt Vaughn, personal communication), providing a mechanism for community access. The overall flow of the pipeline is illustrated in Figure 8. Importantly, these scripts are well documented and can be easily modified and improved as RNA-seq technologies advance. As such, we have intentionally left the pipeline highly annotated and modular, so that modification and improvement can be easily made. Thus, this computational pipeline can serve as a useful resource that the community can improve and adapt upon and may accelerate the unification of RNA-seq data analysis.

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