COME: a robust coding potential calculation tool for lncRNA identification and characterization based on multiple features.
Recent genomic studies suggest that novel long non-coding RNAs (lncRNAs) are specifically expressed and far outnumber annotated lncRNA sequences. To identify and characterize novel lncRNAs in RNA sequencing data from new samples, we have developed COME, a coding potential calculation tool based on multiple features. It integrates multiple sequence-derived and experiment-based features using a decompose-compose method, which makes it more accurate and robust than other well-known tools. We also showed that COME was able to substantially improve the consistency of predication results from other coding potential calculators. Moreover, COME annotates and characterizes each predicted lncRNA transcript with multiple lines of supporting evidence, which are not provided by other tools. Remarkably, we found that one subgroup of lncRNAs classified by such supporting features (i.e. conserved local RNA secondary structure) was highly enriched in a well-validated database (lncRNAdb). We further found that the conserved structural domains on lncRNAs had better chance than other RNA regions to interact with RNA binding proteins, based on the recent eCLIP-seq data in human, indicating their potential regulatory roles. Overall, we present COME as an accurate, robust and multiple-feature supported method for the identification and characterization of novel lncRNAs. The software implementation is available at https://github.com/lulab/COME.
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology and Center for Plant Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.|PKU-Tsinghua-NIBS Graduate Program, School of Life Sciences, Peking University, Beijing 100871, China.
Long
L
Hu
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology and Center for Plant Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Zhiyu
Z
Xu
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology and Center for Plant Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China.
Boqin
B
Hu
MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology and Center for Plant Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing 100084, China zhilu@tsinghua.edu.cn.
Zhi John
ZJ
Lu
D000818:Animals; D017360:Arabidopsis / Q000235:genetics; D001483:Base Sequence; D001665:Binding Sites; D017173:Caenorhabditis elegans / Q000235:genetics; D003196:Computer Graphics; D030561:Databases, Nucleic Acid; D004331:Drosophila melanogaster / Q000235:genetics; D016678:Genome; D006801:Humans; D020407:Internet; D051379:Mice; D058977:Molecular Sequence Annotation; D009690:Nucleic Acid Conformation; D011485:Protein Binding; D062085:RNA, Long Noncoding / Q000145:classification / Q000235:genetics* / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000235:genetics* / Q000378:metabolism; D017423:Sequence Analysis, RNA; D012984:Software
D062085:RNA, Long Noncoding; D016601:RNA-Binding Proteins