Table 3 Descriptions of other methods to identify lncRNAs

Experimental methods of lncRNA identification

Microarray [67]

It uses Computational and annotation pipeline to determine the expression and regulation potential of lncRNA transcripts. It has higher efficiency in high put lncRNA analysis. However, its detection potential is low due to its low sensitivity and low expression level of the lncRNA

SAGE [60, 68]

The serial analysis of gene expression (SAGE) is a technology which identifies the lncRNA known and unknown transcripts by producing short sequence tags and is the highly effective method to study lncRNAs but, it is expensive and not applicable in large scale researches.

EST [69, 70]

Expressed sequence tag (EST) is a cDNAs short subsequence generated from cDNA clone by one shot sequencing to discover novel and functional transcripts of lncRNA in mammalian. This public database helps to search the transcripts in the intergenic regions of genes and reconstruct lncRNA transcript assemblies.

RNA-Seq [71]

It is a shotgun sequencing of whole transcriptome in the next generation sequencing technologies and is used to identify novel lncRNA transcripts and gene expression analysis.

RNA-IP [72]

RNA-immunoprecipitation one of the latest techniques that used antibodies of protein to discover and isolate the lncRNA that interacts with protein complexes or specific proteins by constructing cDNA library and deep sequencing of lncRNAs.

Chromatin Signature Based Approach [11]

Is a method that do not target directly on the RNA transcripts but directly involves in the identification of lncRNA expression regulation mechanisms using Chromatin signatures and their regulation factors.

Computational methods of lncRNA identification

ORF Length Strategy [73]

This strategy is a method used to differentiate the lncRNA from the mRNA by the Open Reading Frame (ORF) length cutoff based on codons length.

Sequence and Secondary Structure Conservation Strategy [74,75,76]

This strategy is used to differentiate the non-coding genes from the protein coding genes by using different methods and strategies such as conservation potential, measure of coding potential, codon substitution frequency scores, reading frame conservation and PhyloCSF. The other methods that are used to explore the RNA secondary structure conservation include the programs of QRNA, EvoFOLD and RNAz.

Machine Learning Strategies [77]

Due to the complexity of lncRNAs, a new machine learning systems have been increasingly developed such as SVM (support vector machine) based machine learning technique like CONC (coding or non-coding), and other models to integrate and utilize various protein features to distinguish the lncRNAs from mRNAs.