**8. Limitations in computational analysis of lncRNAs**

The selection of lncRNAs from the complete set of RNAs is broadly based on three criteria: (i) transcript length of ≥200 bp, (ii) small open reading frame with ≤300 bp, and (iii) transcripts without homology to known proteins. In addition to this, several other factors like the type of cDNA libraries or transcriptional sequence data, depth of sequencing, and coding potential of transcripts, also contribute in the screening of lncRNAs. The challenges during computational analysis come when some protein-coding gene which fulfill the basic selection criteria and encode a functional peptide. Besides this, the functional long non-coding transcript may have ORF >300 bp and share homology with known protein-coding genes will also produce hindrance in the identification [90]. Another challenge comes with the transcripts that not only function as an RNA molecule, but also encodes a peptide [91]. The advancement in computational approaches have been made to overcome these limitations and for more accurate differentiation between coding and non-coding transcripts [92]. The use of support vector machines (SVMs) or other machine learning algorithms along with the computational methods have increased the confidence of disparity in between coding and non-coding transcripts [93]. However, the identity and function of computationally identified lncRNA needs to be validated individually by experimentation.

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