Antoniewski Christophe
Laboratoire de Biologie du Développement, Drosophila Genetics and Epigenetics, CNRS UMR 7622 and University Paris 6 Pierre et Marie Curie, 9 quai Saint-Bernard, 75252, Paris cedex 05, France,
Methods Mol Biol. 2014;1173:135-46. doi: 10.1007/978-1-4939-0931-5_12.
High-throughput sequencing approaches opened the possibility to precisely map full populations of small RNAs to the genomic loci from which they originate. A bioinformatic approach revealed a strong tendency of sense and antisense piRNAs to overlap with each other over ten nucleotides and had a major role in understanding the mechanisms of piRNA biogenesis. Using similar approaches, it is possible to detect a tendency of sense and antisense siRNAs to overlap over 19 nucleotides. Thus, the so-called overlap signature which describes the tendency of small RNA to map in a specific way relative to each other has become the approach of choice to identify and characterize specific classes of small RNAs. Although simple in essence, the bioinformatic methods used for this approach are not easily accessible to biologists. Here we provide a python software that can be run on most of desktop or laptop computers to compute small RNA signatures from files of sequencing read alignments. Moreover, we describe and illustrate step by step two different algorithms at the core of the software and which were previously used in a number of works.
高通量测序方法开启了精确绘制小RNA完整群体至其起源基因组位点的可能性。一种生物信息学方法揭示了正义链和反义链piRNA在十个以上核苷酸上相互重叠的强烈趋势，并且在理解piRNA生物合成机制方面发挥了重要作用。使用类似方法，可以检测到正义链和反义链siRNA在19个以上核苷酸上重叠的趋势。因此，描述小RNA以特定方式相互映射趋势的所谓重叠特征，已成为识别和表征特定类别小RNA的首选方法。尽管本质上很简单，但生物学家并不容易使用用于此方法的生物信息学方法。在这里，我们提供了一个可以在大多数台式机或笔记本电脑上运行的Python软件，用于从测序读段比对文件中计算小RNA特征。此外，我们逐步描述并说明了该软件核心的两种不同算法，这些算法先前已在许多研究中使用。
