The last decade has witnessed huge effort in the understanding of the various mechanisms by which small non coding RNAs regulate the various life processes associated with a cell. Such efforts have led to the identification of multiple small non coding RNAs such as miRNA, siRNA, tasiRNA, piRNA, rasiRNA and many more. Of them microRNAs with their ubiquitous presence in all domains of life has become the molecule of choice of many research efforts and mechanisms involving their biogenesis and final function in the cell has been elucidated through these works. However, still certain grey areas exist in our understanding regarding the phenomenon of regulation of these non-coding wonders. As we all are aware that most microRNAs are differentially regulated during the entire lifespan of an organism, it becomes very clear that certain regulatory proteins and their interacting partners play important role in the process. With this background this work was performed where the precursor sequences of microRNAs of Pan troglodytes were considered as query and through position specific weight matrix evaluation and subsequent validation a large number of RNA regulatory elements were identified in those sequences.

Next generation sequencing (NGS) technologies produces very large amount of data at low cost. Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genetic variation and there is a need to identify SNPs with less time consuming, user-friendly and accurate tools so as to minimize the efforts of researchers in analyzing data. There are various tools available for variant discovery produced from NGS data but no study presents the comparison of the SNPs discovery with CLC and SeqMan. Here we present the performance of CLC and SeqMan for prediction of potential SNPs from human buccal cancer and healthy transcriptome data obtained from Roche 454 sequencing technology based on the software utility, time, memory, disk space and accuracy of results. It was found that, performance of SeqMan seems better than that of CLC in terms of utility and accuracy. Though SeqMan required more time, memory as well as disk space than that of CLC. Both the tools are equipped with user friendly options and provide proper guideline for running.