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PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing.

Folkes L, Moxon S, Woolfenden HC, Stocks MB, Szittya G, Dalmay T, Moulton V - Nucleic Acids Res. (2012)

Bottom Line: Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript.Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments.By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Sciences and School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.

ABSTRACT
Small RNAs (sRNAs) are a class of short (20-25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

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Venn diagram showing the comparison of results produced by CleaveLand and PAREsnip. The Venn diagram shows the intersection of predictions made by PAREsnip and CleaveLand and is a summary of the results within Supplementary Tables S2 and S3.
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gks277-F5: Venn diagram showing the comparison of results produced by CleaveLand and PAREsnip. The Venn diagram shows the intersection of predictions made by PAREsnip and CleaveLand and is a summary of the results within Supplementary Tables S2 and S3.

Mentions: The results are summarized in Figure 5 (see full results in Supplementary Tables S2 and S3). As can be seen, PAREsnip reports either the same number or slightly more previously validated targets than CleaveLand. The interactions reported by PAREsnip and not by CleaveLand or vice versa are due to the random factor within the P-value systems used by both tools. For example, in contrast to CleaveLand, PAREsnip uses dinucleotide random shuffles when calculating a P-value through the use of uShuffle (31). Furthermore, differences between the interactions predicted by the two tools are probably also due to the reporting of hits that contain a mismatch at position 10 (from 5′ of sRNA), multiple gaps within a duplex and more than 2.5 mismatches or adjacent mismatches within the seed region (positions 1–12 5′ of sRNA) of the duplex. Again, in contrast to CleaveLand, these features within a duplex are not permitted by the Rule-Based Complementarity Search algorithm used by PAREsnip.Figure 5.


PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing.

Folkes L, Moxon S, Woolfenden HC, Stocks MB, Szittya G, Dalmay T, Moulton V - Nucleic Acids Res. (2012)

Venn diagram showing the comparison of results produced by CleaveLand and PAREsnip. The Venn diagram shows the intersection of predictions made by PAREsnip and CleaveLand and is a summary of the results within Supplementary Tables S2 and S3.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3401462&req=5

gks277-F5: Venn diagram showing the comparison of results produced by CleaveLand and PAREsnip. The Venn diagram shows the intersection of predictions made by PAREsnip and CleaveLand and is a summary of the results within Supplementary Tables S2 and S3.
Mentions: The results are summarized in Figure 5 (see full results in Supplementary Tables S2 and S3). As can be seen, PAREsnip reports either the same number or slightly more previously validated targets than CleaveLand. The interactions reported by PAREsnip and not by CleaveLand or vice versa are due to the random factor within the P-value systems used by both tools. For example, in contrast to CleaveLand, PAREsnip uses dinucleotide random shuffles when calculating a P-value through the use of uShuffle (31). Furthermore, differences between the interactions predicted by the two tools are probably also due to the reporting of hits that contain a mismatch at position 10 (from 5′ of sRNA), multiple gaps within a duplex and more than 2.5 mismatches or adjacent mismatches within the seed region (positions 1–12 5′ of sRNA) of the duplex. Again, in contrast to CleaveLand, these features within a duplex are not permitted by the Rule-Based Complementarity Search algorithm used by PAREsnip.Figure 5.

Bottom Line: Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript.Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments.By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Sciences and School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.

ABSTRACT
Small RNAs (sRNAs) are a class of short (20-25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete 'sRNAome' we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

Show MeSH