PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules.
PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules.
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DOI：
10.1093/nar/gky609
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发表时间：
2018-09-28
影响因子：
14.9
通讯作者：
Moulton V
中科院分区：
文献类型：
--
作者：
Thody J;Folkes L;Medina-Calzada Z;Xu P;Dalmay T;Moulton V
小RNA(SRNAs)是一种短的、非编码的RNA，在许多重要的生物途径中发挥着关键作用。它们通过将信使RNA诱导的沉默复合体定向到其序列特异性的mRNAs靶标(S)来抑制信使RNA(MRNAs)的翻译。在植物中，这通常会导致信使核糖核酸的裂解和随后的信使核糖核酸降解。由此产生的mRNA片段或降解组为这些相互作用提供了证据，因此降解组分析已成为预测sRNA靶标的重要工具。尽管如此，随着测序技术的不断进步，不仅测序的基因组更大、更复杂，而且降解组和相关数据集的数量和读数都在增长。因此，现有的降级穹顶分析工具无法在不施加巨大资源和时间要求的情况下处理产生的数据量。此外，这些工具使用严格的、不可配置的目标规则，这降低了它们的灵活性。在这里，我们提出了一个新的和用户可配置的软件工具，它使用了一种新的搜索算法和序列编码技术来减少分析过程中的搜索空间。该工具显著减少了执行降解穹顶分析所需的时间和资源，在某些情况下提供了比当前方法快两个数量级以上的速度。
Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods.
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影响因子：
14.9
作者：
Brousse C;Liu Q;Beauclair L;Deremetz A;Axtell MJ;Bouché N
通讯作者：
Bouché N
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通讯作者：
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影响因子：
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通讯作者：
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通讯作者：
Axtell, Michael J.
影响因子：
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作者：
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通讯作者：
Axtell, Michael J.