**8. Transcriptome analysis for non-coding RNA studies in chickpea**

Non-coding RNAs usually act as regulatory elements that have a decisive role in fine regulation of gene activity. Non-coding transcripts comprise of small and long non-coding RNAs. Small non-coding RNAs regulate diverse developmental processes by controlling gene expression at transcriptional and post-transcriptional level [72, 73]. MicroRNAs (miRNAs) constitute the major class of small non-coding RNAs and are 20–24 nucleotides long key regulatory elements. They are highly conserved and play an important part in various developmental processes in plants such as leaf development, flowering, formation and maintenance of the shoot, floral and axillary meristems, establishment of organ polarity, root nodule symbiosis, vegetative to reproductive phase transition and response to biotic and abiotic stresses [73–77]. In chickpea, small RNA libraries were sequenced from normal tissues and those under different stress conditions [78–80]. Small RNA sequence data were filtered and processed for miRNA prediction using miRDeep pipeline resulting in identification of distinct conserved miRNAs from shoot (302, including Cat-miR156b-5p, Cat-miR156j.1, Cat-miR159.1, Cat-miR169b-5p), root (280, including Cat-miR156c.1, Cat-miR169n, Cat-miR171k-3p), mature leaf (248, including Cat-miR156k, Cat-miR172d.2, Cat-NovmiR319b, Cat-miR167a, Cat-miR167d.2), stem (268, including Cat-miR172c-3p, Cat-miR159.3, Cat-NovmiR319d, Cat-miR171k-3p), flower bud (247, Cat-miR319g.2, Cat-miR167c.2, Cat-miR167d.1, Cat-miR171b-3p.2), flower (293, CatmiR159.4, Cat-miR159e, Cat-miR171m) and young pod (274, Cat-miR172d.1, Cat-NovmiR159a, Cat-miR167-5p). By ab initio prediction, a total of 109, 76, 123, 100, 106, 98 and 120 novel candidate miRNAs were identified from the above tissues, respectively. Overall 618 miRNAs were identified from all the tissues with the maximum being 373 miRNAs from the shoot and minimum 303 from flower buds. Of the 618 miRNAs predicted, 158 were present in all the tissues, and 29% of the miRNAs were found to be tissue specific. Of the 618 miRNAs, 421 were clustered to 73 miRNA families, and 197 could not find similarity to any miRNA family and were termed putative novel. Chickpea miRNAs targeted a wide range of transcripts involved in diverse cellular processes including protein turnover and modification, metabolism, transcriptional regulation and signal transduction [78]. A similar kind of study performed in leaf and flower tissue resulted in the prediction of 96 highly conserved miRNAs belonging to 38 miRNA families and 20 novel miRNAs belonging to 17 miRNA families in chickpea [80]. In addition to identification of miRNA from different tissues, studies were also conducted for characterization of miRNA in response to different biotic and abiotic stresses. In one such kind of study, three libraries were sequenced for small RNA identification [79]. Libraries were constructed from fungal-infected (*F. oxysporum* f.sp*. ciceris*), salt-treated and untreated seedlings of chickpea and were sequenced using the Illumina GAIIx platform. The analysis identified 122 conserved miRNAs belonging to 25 different families along with 59 novel miRNAs. miR156, miR396 and miR319 were upregulated in response to salt stress. miR156 and miR396 expression was found to be 1.5 times upregulated in both wilt and salt stresses, indicating a common mechanism implied by chickpea involving these miRNAs to cope up with both the stresses. miR530 was found to be significantly upregulated during wilt stress and may be involved in defence to fungal infection. Three legume-specific miRNAs, miR2111, miR2118 and miR5213, were also indicated to play a critical role in defence to pathogen attack. Targets of miR2111 include F-box protein and TIR (Toll/Interleukin-1 Receptor) domain-containing NBS-LRR disease-resistance proteins, and miR2118 and miR5213 also target the same class of R genes. Interestingly, miR2118 is upregulated following wilt infection and downregulated following salt stress [79].

*ciceri*. The analysis resulted in identification of 257 differentially expressed genes associated with the early signalling pathway [70]. In order to understand the differential response of susceptible and tolerant/resistant chickpea varieties to *F. oxysporum*, transcriptomes of wilt susceptible (JG62) and wilt-tolerant/wilt-resistant (ICCV2, K850 and WR315) chickpea varieties were analysed using the Illumina platform. Comparison among the transcriptomes led to identification of 303 polymorphic SSRs, 14,462 SNPs and 1864 insertions/deletions (InDels). Moreover, a large number of SNPs and/or InDels were found to be present in defence-related

252 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

In order to identify common genes between biotic and abiotic responses in chickpea, Mantri et al. [71] performed microarray analysis of chickpea ICC 3996 under three abiotic stresses (drought, cold and high salinity) and biotic stress (infection with *A. rabiei*). This analysis revealed 46, 54, 266 and 51 genes to be differentially regulated in drought, cold, high salinity and *A. rabiei* stresses, respectively. *A. rabiei* stress response was found to be more similar to

**8. Transcriptome analysis for non-coding RNA studies in chickpea**

Non-coding RNAs usually act as regulatory elements that have a decisive role in fine regulation of gene activity. Non-coding transcripts comprise of small and long non-coding RNAs. Small non-coding RNAs regulate diverse developmental processes by controlling gene expression at transcriptional and post-transcriptional level [72, 73]. MicroRNAs (miRNAs) constitute the major class of small non-coding RNAs and are 20–24 nucleotides long key regulatory elements. They are highly conserved and play an important part in various developmental processes in plants such as leaf development, flowering, formation and maintenance of the shoot, floral and axillary meristems, establishment of organ polarity, root nodule symbiosis, vegetative to reproductive phase transition and response to biotic and abiotic stresses [73–77]. In chickpea, small RNA libraries were sequenced from normal tissues and those under different stress conditions [78–80]. Small RNA sequence data were filtered and processed for miRNA prediction using miRDeep pipeline resulting in identification of distinct conserved miRNAs from shoot (302, including Cat-miR156b-5p, Cat-miR156j.1, Cat-miR159.1, Cat-miR169b-5p), root (280, including Cat-miR156c.1, Cat-miR169n, Cat-miR171k-3p), mature leaf (248, including Cat-miR156k, Cat-miR172d.2, Cat-NovmiR319b, Cat-miR167a, Cat-miR167d.2), stem (268, including Cat-miR172c-3p, Cat-miR159.3, Cat-NovmiR319d, Cat-miR171k-3p), flower bud (247, Cat-miR319g.2, Cat-miR167c.2, Cat-miR167d.1, Cat-miR171b-3p.2), flower (293, CatmiR159.4, Cat-miR159e, Cat-miR171m) and young pod (274, Cat-miR172d.1, Cat-NovmiR159a, Cat-miR167-5p). By ab initio prediction, a total of 109, 76, 123, 100, 106, 98 and 120 novel candidate miRNAs were identified from the above tissues, respectively. Overall 618 miRNAs were identified from all the tissues with the maximum being 373 miRNAs from the shoot and minimum 303 from flower buds. Of the 618 miRNAs predicted, 158 were present in all the tissues, and 29% of the miRNAs were found to be tissue specific. Of the 618 miRNAs, 421 were clustered to 73 miRNA families, and 197 could not find similarity to any miRNA family and were termed putative novel. Chickpea miRNAs targeted a wide range of transcripts involved

genes [43].

that of high-salinity stress [71].

Long intergenic non-coding (linc) RNAs belong to a class of non-coding transcripts which have a length of at least 200bp lacking coding potential and are transcribed from intergenic region of protein coding genes [81, 82]. Linc RNAs control gene regulation at transcriptional and post-transcriptional level by mechanisms including chromatin modification, promoter binding complex attachment and shielding mRNA degradation by acting as sponge against miRNA [83–85]. RNA-seq data from 11 different tissues of chickpea were used for mining linc-RNA [86]. RNA-seq data were processed using TopHat2 and Cufflinks program using chickpea genome as the reference. From 32,984 transcripts obtained, 5782 putative intergenic transcripts were extracted out and subjected to the optimized pipeline for identification of linc-RNA. After removing potential coding transcripts and transcripts having similarity to protein domains, finally a total of 2248 transcripts were retained as putative chickpea linc-RNAs. About 79%, i.e. 1790 linc-RNAs, could be assigned a putative function. Through expression profiling it was evident that a large number of linc-RNAs have tissue-specific expression in distinct tissues. Along with this several linc-RNAs were found to be targets of miRNAs and were involved in various developmental and reproductive processes [86].
