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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Grada, A., Weinbrecht, K.: Next-generation sequencing: methodology and application. Soc. Invest. Dermatol. 133(8), e11 (2013)
Wang, Z., Gerstein, M., Snyder, M.: RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009)
Li, H., Durbin, R.: Fast and accurate short read alignment with burrows wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)
Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, r25 (2009)
Li, R., Li, Y., Kristiansen, K., Wang, J.: SOAP: short oligonucleotide alignment program. Bioinformatics 24(5), 713–714 (2008)
Miller, J.R., Koren, S., Sutton, G.: SOAP: short oligonucleotide alignment program. Genomics 95(6), 315–326 (2010)
Hach, F., Sarrafi, I., Hormozdiari, F., Alkan, C., Eichler, E.E., Sahinalp, S.C.: Mrsfast-ultra: a compact, snp-aware mapper for high performance sequencing applications. Nucleic Acid Res. 42, W494–500 (2014)
Li, Y., Kowdley, K.V.: MicroRNAs in common human diseases. Genomics Proteomics Bioinf. 10, 246–253 (2012)
Reshmi, G., Vinod Chandra, S.S., Mohan Babu, V.J., Babu, P.S.S., Santhi, W.S., Ramachandran, S., Lakshmi, S., Nair, A.S., Pillai, M.R.: Identification and analysis of novel microRNAs from fragile sites of human cervical cancer: computational and experimental approach. Genomics 97(6), 333–340 (2011)
Salim, A., Vinod Chandra, S.S.: Computational prediction of microRNAs and their targets. J. Proteomics Bioinform. 7(7), 193–202 (2014)
Landi, M.T., Zhao, Y., Rotunno, M., Koshiol, J., Liu, H., Bergen, A.W., Rubagotti, M., Goldstein, A.M., Linnoila, I., Marincola, F.M., Tucker, M.A., Bertazzi, P.A., Pesatori, A.C., Caporaso, N.E., McShane, L.M., Wang, E.: MicroRNA expression differentiates histology and predicts survival of lung cancer. Clin. Cancer Res. 16(2), 430–441 (2010)
Schee, K., Lorenz, S., Worren, M.M., Günther, C.-C., Holden, M., Hovig, E., Fodstad, Ø., Meza-Zepeda, L.A., Flatmark, K.: Deep sequencing the microRNA transcriptome in colorectal cancer. PLoS ONE 8(6), e66165 (2013)
Schulte, J.H., Marschall, T., Martin, M., Rosenstiel, P., Mestdagh, P., Schlierf, S., Thor, T., Vandesompele, J., Eggert, A., Schreiber, S., Rahmann, S., Schramm, A.: Deep sequencing reveals differential expression of microRNAs in favorable versus unfavorable neuroblastoma. Nucleic Acids Res. 38(17), 5919–5928 (2010)
Chang, H.T., Li, S.C., Ho, M.R., Pan, H.W., Ger, L.P., Hu, L.Y., Yu, S.Y., Li, W.H., Tsai, K.W.: Comprehensive analysis of microRNAs in breast cancer. BMC Genomics 13(6), S18 (2012)
Horspool, R.N.: Practical fast searching in strings. Softw. Pract. Experience 10(6), 501–506 (1980)
Wojcicka, A., Swierniak, M., Kornasiewicz, O., Gierlikowski, W., Maciag, M., Kolanowska, M., Kotlarek, M., Gornicka, B., Koperski, L., Niewinski, G., Krawczyk, M., Jazdzewski, K.: Next generation sequencing reveals microRNA isoforms in liver cirrhosis and hepatocellular carcinoma. Int. J. Biochem. Cell Biol. 53, 208–217 (2014)
Tatusova, T., Ciufo, S., Fedorov, B., O’Neill, K., Tolstoy, I.: Refseq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42, D553–D559 (2014)
Huang, W., Li, L., Myers, J.R., Marth, G.T.: Art: a next-generation sequencing read simulator. Bioinformatics 28(4), 593–594 (2012)
Eminaga, S., Christodoulou, D.C., Vigneault, F., Church, G.M., Seidman, J.G.: Quantification of microRNA expression with next-generation sequencing. In: Current Protocols in Molecular Biology, Chapter 4, Unit-4.17 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-40973-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40972-6
Online ISBN: 978-3-319-40973-3
eBook Packages: Computer ScienceComputer Science (R0)