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R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data.

Mittal VK, McDonald JF - Nucleic Acids Res. (2012)

Bottom Line: We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets.R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading.In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

View Article: PubMed Central - PubMed

Affiliation: School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.

ABSTRACT
The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

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Distribution of RefSeq transcripts detected by R-SAP using MAQC Reference Human dataset. ‘Normally spliced’ RefSeq transcripts (5039 transcripts) showed no novel transcriptional events. ‘Single novel event’ transcript (15 796 RefSeq transcripts) and ‘Multiple novel event’ transcripts (9239 RefSeq transcripts) were detected to have only one type and more than one type of novel transcriptional event, respectively.
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gks047-F5: Distribution of RefSeq transcripts detected by R-SAP using MAQC Reference Human dataset. ‘Normally spliced’ RefSeq transcripts (5039 transcripts) showed no novel transcriptional events. ‘Single novel event’ transcript (15 796 RefSeq transcripts) and ‘Multiple novel event’ transcripts (9239 RefSeq transcripts) were detected to have only one type and more than one type of novel transcriptional event, respectively.

Mentions: In summary, the MAQC Reference Human RNA-Seq data mapped to 30 074 of the RefSeq transcripts (27 068 protein coding and 3006 non-protein coding). R-SAP classified these detected reference transcripts as either normally or aberrantly spliced (Figure 5).Figure 5.


R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data.

Mittal VK, McDonald JF - Nucleic Acids Res. (2012)

Distribution of RefSeq transcripts detected by R-SAP using MAQC Reference Human dataset. ‘Normally spliced’ RefSeq transcripts (5039 transcripts) showed no novel transcriptional events. ‘Single novel event’ transcript (15 796 RefSeq transcripts) and ‘Multiple novel event’ transcripts (9239 RefSeq transcripts) were detected to have only one type and more than one type of novel transcriptional event, respectively.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351179&req=5

gks047-F5: Distribution of RefSeq transcripts detected by R-SAP using MAQC Reference Human dataset. ‘Normally spliced’ RefSeq transcripts (5039 transcripts) showed no novel transcriptional events. ‘Single novel event’ transcript (15 796 RefSeq transcripts) and ‘Multiple novel event’ transcripts (9239 RefSeq transcripts) were detected to have only one type and more than one type of novel transcriptional event, respectively.
Mentions: In summary, the MAQC Reference Human RNA-Seq data mapped to 30 074 of the RefSeq transcripts (27 068 protein coding and 3006 non-protein coding). R-SAP classified these detected reference transcripts as either normally or aberrantly spliced (Figure 5).Figure 5.

Bottom Line: We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets.R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading.In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

View Article: PubMed Central - PubMed

Affiliation: School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.

ABSTRACT
The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

Show MeSH
Related in: MedlinePlus