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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‚ÄČnt) 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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Interactions reported by PAREsnip with P-value increases. Starting from the smallest P-value of 0.00, we see a progressive increase in the number of small RNA/mRNA interactions reported. The P-value cut-off of 0.05 captures 94.5% of total validated interactions reported by PAREsnip and is the default setting.
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gks277-F6: Interactions reported by PAREsnip with P-value increases. Starting from the smallest P-value of 0.00, we see a progressive increase in the number of small RNA/mRNA interactions reported. The P-value cut-off of 0.05 captures 94.5% of total validated interactions reported by PAREsnip and is the default setting.

Mentions: To examine the usefulness of the P-value computed by PAREsnip as a confidence score upon which predicted interactions can be excluded, we ran it on all known mature A.thaliana miRNAs, GSM278370 (18,33) degradome and the A.thaliana transcriptome (representative gene model, TAIR release 10) (32) with increasing P-value thresholds. The predictions were compared with previously validated interactions (Supplementary Table S1) to provide an insight into the number of validated interactions retained along with the number of other interactions reported in relation to the increasing threshold (Figure 6). Note that a P-value cut-off of 1 captures all possible predictions. PAREsnip reported a total of 91 validated and 1026 non-validated interactions using a P-value cut-off of 1. We find that a threshold of 0.05 captures 94.5% of possible validated interactions (a loss of 5.5% validated interactions) while capturing 7.6% of the total non-validated interactions. In light of this and other similar experiments we have chosen a default P-value setting for PAREsnip of 0.05.Figure 6.


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)

Interactions reported by PAREsnip with P-value increases. Starting from the smallest P-value of 0.00, we see a progressive increase in the number of small RNA/mRNA interactions reported. The P-value cut-off of 0.05 captures 94.5% of total validated interactions reported by PAREsnip and is the default setting.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gks277-F6: Interactions reported by PAREsnip with P-value increases. Starting from the smallest P-value of 0.00, we see a progressive increase in the number of small RNA/mRNA interactions reported. The P-value cut-off of 0.05 captures 94.5% of total validated interactions reported by PAREsnip and is the default setting.
Mentions: To examine the usefulness of the P-value computed by PAREsnip as a confidence score upon which predicted interactions can be excluded, we ran it on all known mature A.thaliana miRNAs, GSM278370 (18,33) degradome and the A.thaliana transcriptome (representative gene model, TAIR release 10) (32) with increasing P-value thresholds. The predictions were compared with previously validated interactions (Supplementary Table S1) to provide an insight into the number of validated interactions retained along with the number of other interactions reported in relation to the increasing threshold (Figure 6). Note that a P-value cut-off of 1 captures all possible predictions. PAREsnip reported a total of 91 validated and 1026 non-validated interactions using a P-value cut-off of 1. We find that a threshold of 0.05 captures 94.5% of possible validated interactions (a loss of 5.5% validated interactions) while capturing 7.6% of the total non-validated interactions. In light of this and other similar experiments we have chosen a default P-value setting for PAREsnip of 0.05.Figure 6.

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‚ÄČnt) 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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