author(s)
Aruna Pandey, Shikha Saxena, Raja Ishaq Nabi Khan and Pramod W Ramteke

abstract
Long non-coding RNAs (lncRNAs) has been studied in mammalian genome. However, it remains a challenge to uncover lncRNAs function in a newly assembled species. In the present study, we have predicted a computational pipeline for analysing lncRNAs extracted from public repository. We have applied our pipeline to a goat lncRNAs transcripts dataset, and discussed the possible functions of the lncRNAs transcripts. The lncRNAs transcripts were extracted from Ensembl database and filtered with length parameter (length >=200 nt) and coding potentiality, using a CPC2 tool. The filtered lncRNAs transcripts were further evaluated against MEME and TomTom tool for motif and transcription factors (TF’s) discovery. In addition, the lncRNAs transcripts were classified and annotated further using Infernal and blast tool, respectively. Using our stringent pipeline a total of 1304 lncRNAs transcripts were predicted. 3 motifs and 39 transcription factors were found to be regulating these lncRNAs transcripts. Moreover the lncRNAs studied here, belongs to different ncRNA families and also act as precursors for various microRNAs (miRNAs) such as chi-mir-23b, chi-mir-29a etc. In conclusion, our method can annotate lncRNAs transcripts in goat genome and the predicted pipeline can also be applied to other newly assembled genomes for understanding the function of lncRNAs.