Abstract
K-mer Mapping, an internal process for De Novo NGS genome fragments assembly methods, constitutes a computational challenge due to its high main memory consumption. We present a study of index-based methods to deal with this problem, considering a RDBMS environment. We propose an ad-hoc I/O cost model and analyze the performance of hash and B-tree versions for index structures. Furthermore, we present a novel approach for an index based on hashing that takes into account the notion of minimum substrings. An actual RDBMS implementation for experiments with a sugarcane dataset shows that one can obtain considerable performance gains while reducing main memory requirements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bradnam, K.R., Fass, J.N., et al.: Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. GigaScience 2(1), 1–31 (2013)
Butler, J., et al.: ALLPATHS: de novo assembly of whole-genome shotgun microreads. Genome Res. 18(5), 810–820 (2008)
Claros, M.G., Bautista, R., Guerrero-Fernández, D., Benzerki, H., Seoane, P., Fernández-Pozo, N.: Why assembling plant genome sequences is so challenging. Biology 1(2), 439 (2012)
Cook, J.J., Zilles, C.: Characterizing and optimizing the memory footprint of de novo short read DNA sequence assembly. In: Performance Analysis of Systems and Software, ISPASS, pp. 143–152, April 2009
de Armas, E.M., Haeusler, E.H., Lifschitz, S., de Holanda, M.T., da Silva, W.M.C., Ferreira, P.C.G.: K-mer Mapping and de Bruijn graphs: the case for velvet fragment assembly. In: Proceedings IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 882–889 (2016)
Deorowicz, S., Debudaj-Grabysz, A., Grabowski, S.: Disk-based k-mer counting on a PC. BMC Bioinform. 14(1), 160 (2013)
Earl, D., Bradnam, K., et al.: Assemblathon 1: a competitive assessment of de novo short read assembly methods. Genome Res. 21(12), 2224–2241 (2011)
El-Metwally, S., Hamza, T., Zakaria, M., Helmy, M.: Next-generation sequence assembly: four stages of data processing and computational challenges. PLoS Comput. Biol. 9(12), 1–19 (2013)
Kleftogiannis, D., Kalnis, P., Bajic, V.B.: Comparing memory-efficient genome assemblers on stand-alone and cloud infrastructures. PLoS ONE 8(9) (2013)
Kundeti, V., Rajasekaran, S., Dinh, H.: Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs. ArXiv (2010)
Li, R., Zhu, H., et al.: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2009)
Li, Y., Kamousi, P., Han, F., Yang, S., Yan, X., Suri, S.: Memory efficient minimum substring partitioning. PVLDB 6(3), 169–180 (2013)
Luo, R., et al.: SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1(1), 1–6 (2012)
Marcais, G., Kingsford, C.: A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27(6), 764–770 (2011)
Ramakrishnan, R., Gehrke, J.: Database Management Systems, 3rd edn. McGraw-Hill Inc, New York, NY, USA (2003)
Rizk, G., Lavenier, D., Chikhi, R.: DSK: k-mer counting with very low memory usage. Bioinformatics 29(5), 652–653 (2013)
Salzberg, S.L., et al.: GAGE: a critical evaluation of genome assemblies and assembly algorithms. Genome Res. 22(3), 557–567 (2012)
Schatz, M.C., Delcher, A.L., Salzberg, S.L.: Assembly of large genomes using second-generation sequencing. Genome Res. 20(9), 1165–1173 (2010)
Schatz, M.C., Witkowski, J., McCombie, W.R.: Current challenges in de novo plant genome sequencing and assembly. Genome Biol. 13(4), 1–7 (2012)
Simpson, J.T., Wong, K., Jackman, S.D., Schein, J.E., Jones, S.J., Birol, I.: ABySS: a parallel assembler for short read sequence data. Genome Res. 19(6), 1117–1123 (2009)
Zerbino, D.R., Birney, E.: Velvet: algorithms for de novo short read assembly using de bruijn graphs. Genome Res. 18(5), 821–829 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
de Armas, E.M., Ferreira, P.C.G., Haeusler, E.H., de Holanda, M.T., Lifschitz, S. (2020). K-mer Mapping and RDBMS Indexes. In: Kowada, L., de Oliveira, D. (eds) Advances in Bioinformatics and Computational Biology. BSB 2019. Lecture Notes in Computer Science(), vol 11347. Springer, Cham. https://doi.org/10.1007/978-3-030-46417-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-46417-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46416-5
Online ISBN: 978-3-030-46417-2
eBook Packages: Computer ScienceComputer Science (R0)