Abstract
Next Generation Sequencing(NGS) is a massively parallel, low cost method capable of sequencing millions of fragments of DNA from a sample. Consequently, huge quantity of data generated and new research challenges to address storage, retrieval and processing of these bulk of data were emerged. microRNAs are non coding RNA sequences of around 18 to 24 nucleotides in length. microRNA expression profiling is a measure of relative abundance of microRNA sequences in a sample. This paper discusses algorithms for pre-processing of reads and a faster Bit Parallel Profiling (BPP) algorithm to quantify microRNAs. Experimental results shows that adapter removal has been accomplished with an accuracy of 91.2 %, a sensitivity of 89.5 % and a specificity of 89.5 %. In the case of profiling, BPP outperform an existing tool, Bowtie in terms of speed of operation.
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A., S., R., A., S.S., V.C. (2016). An Improved Algorithm for MicroRNA Profiling from Next Generation Sequencing Data. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_4
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DOI: https://doi.org/10.1007/978-3-319-40973-3_4
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