Institute for Systems Biology Seattle, WA, USA ; Pós-Graduaçao em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília Brasília, Brazil ; Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília Brasília, Brazil.
Front Genet. 2013 May 15;4:81. doi: 10.3389/fgene.2013.00081. eCollection 2013.
Since microRNAs (miRNAs) were discovered, their impact on regulating various biological activities has been a surprising and exciting field. Knowing the entire repertoire of these small molecules is the first step to gain a better understanding of their function. High throughput discovery tools such as next-generation sequencing significantly increased the number of known miRNAs in different organisms in recent years. However, the process of being able to accurately identify miRNAs is still a complex and difficult task, requiring the integration of experimental approaches with computational methods. A number of prediction algorithms based on characteristics of miRNA molecules have been developed to identify new miRNA species. Different approaches have certain strengths and weaknesses and in this review, we aim to summarize several commonly used tools in metazoan miRNA discovery.
自 microRNAs（miRNAs）被发现以来，它们对调节各种生物活性的影响一直是一个令人惊讶和兴奋的领域。了解这些小分子的全部组成部分是更好地理解它们功能的第一步。近年来，高通量发现工具，如下一代测序，大大增加了不同生物体中已知 miRNAs 的数量。然而，能够准确识别 miRNAs 的过程仍然是一个复杂而困难的任务，需要将实验方法与计算方法相结合。已经开发了许多基于 miRNA 分子特征的预测算法来识别新的 miRNA 种类。不同的方法有其自身的优势和局限性，在这篇综述中，我们旨在总结后生动物 miRNA 发现中几种常用的工具。
