PLIT：一个用于植物转录组数据集中长链非编码RNA识别的免计算工具
PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets
摘要：
长链非编码RNA（longnon-codingRNA，lncRNA）是一类在多种生物学过程中发挥重要作用的非编码RNA。基于RNA-seq的转录组测序已广泛用于lncRNA的鉴定。然而，在RNA-seq数据集中准确识别lncRNA对于探索其在基因组中的特征功能至关重要，因为大多数编码潜能计算（CPC）工具无法在转录组数据中准确识别它们。众所周知的CPC工具，如CPC 2，lncScore，CPAT，主要设计用于基于GENCODE，NONCODE和CANTATAdb数据库预测lncRNA。在转录组数据集上测试时，这些工具的预测准确性往往会下降。这会导致函数注释过程中出现更高的假阳性结果和不准确性。在这项研究中，我们提出了一种新的工具，PLIT，用于识别植物RNA-seq数据集中的lncRNA。PLIT实现了一种基于L1正则化和迭代随机森林（iRF）分类的特征选择方法，用于选择最佳特征。基于序列和密码子偏好特征，其将RNA-seq衍生的FASTA序列分为编码或长非编码转录物。使用L1正则化，获得了31个最佳特征的基础上lncRNA和蛋白质编码转录本从8种植物。使用10倍交叉验证在7个植物RNA-seq数据集上评估该工具的性能。该分析显示出优越的准确性时，对目前可用的国家的最先进的CPC工具进行评估。
英文摘要：
Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools.
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