Department of Computer Science and Engineering, University of Louisville, Louisville, Kentucky, United States of America.
Department of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, Kentucky, United States of America.
PLoS Comput Biol. 2020 Oct 20;16(10):e1008338. doi: 10.1371/journal.pcbi.1008338. eCollection 2020 Oct.
Over the past two decades, researchers have discovered a special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs (circRNAs) have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of current studies has been on the tissue-specific circRNAs that exist only in one tissue but not in other tissues or on the disease-specific circRNAs that exist in certain disease conditions, such as cancer, but not under normal conditions. This approach was conducted in the relative absence of methods that analyze a group of common circRNAs that exist in both conditions, but are more abundant in one condition relative to another (differentially expressed). Studies of differentially expressed circRNAs (DECs) between two conditions would serve as a significant first step in filling this void. Here, we introduce a novel computational tool, seekCRIT (seek for differentially expressed CircRNAs In Transcriptome), that identifies the DECs between two conditions from high-throughput sequencing data. Using rat retina RNA-seq data from ischemic and normal conditions, we show that over 74% of identifiable circRNAs are expressed in both conditions and over 40 circRNAs are differentially expressed between two conditions. We also obtain a high qPCR validation rate of 90% for DECs with a FDR of < 5%. Our results demonstrate that seekCRIT is a novel and efficient approach to detect DECs using rRNA depleted RNA-seq data. seekCRIT is freely downloadable at https://github.com/UofLBioinformatics/seekCRIT. The source code is licensed under the MIT License. seekCRIT is developed and tested on Linux CentOS-7.
在过去的二十年中，研究人员发现了一种特殊的选择性剪接形式，可产生 RNA 的环状形式。尽管这些环状 RNA（circRNA）因其生物发生和功能而在科学界引起了相当大的关注，但当前研究的重点是仅存在于一种组织而不存在于其他组织中的组织特异性 circRNA，或者是仅在某些疾病条件（如癌症）下存在而在正常条件下不存在的疾病特异性 circRNA。这种方法是在缺乏分析存在于两种条件下的一组常见 circRNA 的方法的情况下进行的，这些 circRNA 在一种条件下比另一种条件更为丰富（差异表达）。对两种条件下差异表达 circRNA（DECR）的研究将作为填补这一空白的重要第一步。在这里，我们引入了一种新的计算工具 seekCRIT（在转录组中寻找差异表达的环状 RNA），该工具可从高通量测序数据中识别两种条件之间的 DECR。使用来自缺血和正常条件的大鼠视网膜 RNA-seq 数据，我们表明，超过 74%的可识别 circRNA 在两种条件下均有表达，超过 40 个 circRNA 在两种条件之间差异表达。我们还获得了 DECR 的高 qPCR 验证率为 90%，假阳性率（FDR）<5%。我们的结果表明，seekCRIT 是一种使用 rRNA 耗尽 RNA-seq 数据检测 DECR 的新颖而有效的方法。seekCRIT 可在 https://github.com/UofLBioinformatics/seekCRIT 上免费下载。源代码受 MIT 许可证的许可。seekCRIT 在 Linux CentOS-7 上开发和测试。
