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On February 28th briefings in the international academic journal Briefings in Bioinformatics published the latest research results of the Zhao Fangqing team at the Beijing Institute of Life Sciences of the Chinese Academy of Sciences, entitled Circular RNA base on multiple seed matching.
Because of the current problems of high false positive rate and insufficient sensitivity in ring RNA recognition, the team has studied and proposed a new multi-seed matching algorithm and a maximum likely estimation model that can accurately identify ring RNA connector sequences to significantly improve the efficiency of ring RNA identification.
existing ring RNA recognition algorithms are based on the search for ring RNA connector sequences, which can be divided into annotation-based algorithms and head-to-head prediction algorithms.
However, due to the complexity of etonymical transcription and the particularity of ring RNA molecules, both types of recognition algorithms are faced with problems such as low sensitivity, poor reliability, long computing time or high memory use, and their application is limited.
addition, the evaluation system of the above-mentioned recognition algorithm still relies mainly on the simulation data, which is difficult to measure the performance of the relevant algorithm in the real transcription data.
In view of this situation, Zhao Fangqing's team proposed an algorithm based on a multi-seed matching strategy to extract seed sequences in descending order by length for lower-quality genomic regions, and quickly match them with front and rear flank genomic regions.
the same time, the maximum plasmical estimation model is established to determine the true source of the seed sequence and to eliminate interference from linear transcripts or by-products, thus greatly improving the accuracy of ring RNA molecular recognition.
the study abandoned the simulation data evaluation method with large deviation, and used the comparison system of real transcription data before and after RNase R degradation to make a comprehensive evaluation and comparison of 10 existing algorithms.
results show that the method established in this study has obvious advantages in the comprehensive performance (F1 score) including sensitivity and reliability, and its parallel mode can further improve computing speed and memory efficiency.
the algorithm with the team developed CIRI, CIRI-AS and other analytical tools (Genome Biology, 2015; Nature Communications, 2016) will further facilitate research into the composition and function of ring RNA.
the work was completed by Gao Yuan and Zhang Jinyang, graduate students of Zhao Fangqing's research group, and received financial support from the National Natural Science Foundation of China and the Chinese Academy of Sciences.
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