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Recently, Zhao Fangqing, a researcher at the Computational Genomics Laboratory at the Beijing Institute of Life Sciences of the Chinese Academy of Sciences, published online in Trends in Trends, based on the title of Computational strategies for exploring circular RNAs.
this paper comprehensively expounds many methods in ring RNA research and data mining, discusses the applicable conditions and advantages and disadvantages of the relevant methods in non-coding RNA data mining, and points out the development trend and challenge of future ring RNA data mining.
ring RNA is a class of structure-closed-ringRNA molecules that have received widespread attention in recent years and has been selected as the 2017 Hot Frontier severat at Clarivate Analytics. The gene source, internal composition, cell location, generation mechanism and biological function of the
ring RNA are more diverse, and it is necessary to study it deeply through the mining of high-throughput sequencing data.
based on the use strategy of the reference genome, the existing recognition algorithm can be divided into two categories based on split-alignment pairing (split-alignment) and pseudo-reference sequence-based (pseudo-reference- based) two categories.
because of the different types of comparison algorithms, each recognition algorithm optimizes the algorithm for the splice-aware and omnial (ar) ratios, respectively.
, these recognition algorithms use different strategies for detecting and pairing end-comparison information in back-cut read segments.
the above key steps affect the performance of the recognition algorithm on the sequence data of different transcription groups, there are significant differences in sensitivity, reliability and scope of application of more than ten existing ring RNA recognition algorithms.
research work has been supported by the National Natural Science Foundation of China's major research projects, outstanding youth fund projects and the Chinese Academy of Sciences.
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