Abstract:
:Recently circular RNA (circularRNA) has been discovered as an increasingly important type of long non-coding RNA (lncRNA), playing an important role in gene regulation, such as functioning as miRNA sponges. So it is very promising to identify circularRNA transcripts from de novo assembled transcripts obtained by high-throughput sequencing, such as RNA-seq data. In this study, we presented a machine learning approach, named as PredcircRNA, focused on distinguishing circularRNA from other lncRNAs using multiple kernel learning. Firstly we extracted different sources of discriminative features, including graph features, conservation information and sequence compositions, ALU and tandem repeats, SNP densities and open reading frames (ORFs) from transcripts. Secondly, to better integrate features from different sources, we proposed a computational approach based on a multiple kernel learning framework to fuse those heterogeneous features. Our preliminary 5-fold cross-validation result showed that our proposed method can classify circularRNA from other types of lncRNAs with an accuracy of 0.778, sensitivity of 0.781, specificity of 0.770, precision of 0.784 and MCC of 0.554 in our constructed gold-standard dataset, respectively. Our feature importance analysis based on Random Forest illustrated some discriminative features, such as conservation features and a GTAG sequence motif. Our PredcircRNA tool is available for download at .
journal_name
Mol Biosystjournal_title
Molecular bioSystemsauthors
Pan X,Xiong Kdoi
10.1039/c5mb00214asubject
Has Abstractpub_date
2015-08-01 00:00:00pages
2219-26issue
8eissn
1742-206Xissn
1742-2051journal_volume
11pub_type
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pub_type: 杂志文章,评审
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doi:10.1039/c3mb25594h
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pub_type: 杂志文章,评审
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