Table 2.
Comparison of selected computational tools.
| Tool name and reference* | Model# | Code available | Application | Performance results |
|---|---|---|---|---|
| AnnoLnc (196) | Statistical approach | http://annolnc.cbi.pku.edu.cn | Annotation of human lncRNAs. | Not reported. |
| FEELnc (197) | Random forest | https://github.com/tderrien/FEELnc | Annotation of lncRNAs. | High classification power (AUC = 0.97). |
| LncADeep (198) | Deep belief network built as a stack of restricted Boltzmann machines | https://github.com/cyang235/LncADeep | Identification and functional annotation for lncRNAs | With 10-fold cross validation, average sensitivity of 98.1% and specificity of 97.2% and an average harmonic mean of 97.7% |
| LncFunTK (199) | Statistical approach | https://github.com/zhoujj2013/lncfuntk | To integrate ChIP-seq, CLIP-seq and RNA-seq data to predict, prioritize and annotate lncRNA functions. | Calculates a Functional Information Score (FIS) to quantitatively predict functional importance. |
| lncLocator (200) | Ensemble of support vector machine and random forest classifiers. | http://www.csbio.sjtu.edu.cn/bioinf/lncLocator/ | To predict lncRNA subcellular localizations. | Accuracy of 59% for prediction. |
| PennDiff (201) | Regression-based statistical approach | https://github.com/tigerhu15/PennDiff | To detect differential transcript isoforms from RNA-seq data | Based on both annotations (RefSeq and Ensembl), estimates from PennDiff have Spearman correlation coefficients of 0.87 and 0.76, respectively. |
| SEEKR (202) | Statistical approach | https://github.com/CalabreseLab | Prediction of lncRNA subcellular localization, protein interactors | LncRNAs of related function have similar k-mer profiles, despite linear sequence similarity |
| UClncR (203) | Statistical approach | http://bioinformaticstools.mayo.edu/research/UClncR | Performs transcript assembly, prediction of lncRNA candidates in bulk RNA-seq data, quantification and annotation both known and novel lncRNA candidates. | For lincRNA prediction, UClncR reported 66 “novel” lincRNA transcripts and 12 lncRNAs overlapping with nearby genes (the recall rate of 90.7%). |
| A support vector machine based method to distinguish long non-coding RNAs from protein transcripts (204) | Support vector machine | https://github.com/hugowschneider/longdist.py | To distinguish lncRNAs from protein coding transcripts. | 98.21% accuracy in classifying long non-coding RNAs from protein coding transcripts. |
*
Three of the publications have not been constructed into available tools but rather represent a framework for analysis.
#
Model type does not include preprocessing which may or may not including alignment of protein-coding regions. ∧The link is provided if the code is available otherwise the column is marked with an “X”. AUC, area under the curve.