Long and short non-coding RNAs are gaining importance in the field of gene regulation due as their role and importance in various developmental stages and cellular processes. In general our knowledge on their transcript structure and function is very limiting and a number of new examples have been published mainly in eukaryotes. In bacteria also their these ncRNA are shown to be responsible for performing some of the most fundamental tasks in living cells. Here we explore structure and function of lncRNA locus, transcript structure and function to further elucidate their role in molecular biology of bacteria. Using deep high throughput sequence it is now possible to generate data at the whole transcriptome level. We require robust tools to analyse this data to predict and classify lncRNAs and also to predict their function ato enhance our understanding of this poorly understood class of non-coding RNAs. We used whole genome and transcriptome data from 20 different bacterial strains from 4 different species to screen expression profile, structure and other sequence information based classification of various predicted lncRNAs. In parallel we have also screened the datasets to predict known and potentially new short ncRNA and mapped their annotations to look at important biological pathways. The results of ncRNA sequence, structure and pathway level mapping will be presented. We observed that only a small fraction ncRNAs showed sequence level conservations. We exploited multiple level of ncRNA annotations along with machine learning approaches to extract features and perform classification and secondary predictions and discovered rare, less or non-conserved ncRNA transcripts.