Abstract
Current sequencing machine technology generates very large and redundant volumes of genomic data for each biological sample. Today data and associated metadata are formatted in very large text file assemblies called FASTQ carrying the information of billions of genome fragments referred to as “reads” and composed of strings of nucleotide bases with lengths in the range of a few tenths to a few hundreds bases. Compressing such data is definitely required in order to manage the sheer amount of data soon to be generated. Doing so implies finding redundant information in the raw sequences. While most of it can be mapped onto the human reference genome and fits well for compression, about 10% of it usually does not map to any reference [1]. For these orphan sequences, finding redundancy will help compression. Doing so requires clustering these reads, a very time consuming process. Within this context this paper presents a FPGA implementation of a clustering algorithm for genomic reads, implemented on Pico Computing EX-700 AC-510 hardware, offering more than a \(1000\times \) speed up over a CPU implementation while reducing power consumption by a 700 factor.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Ignoring the non-overlapping ends of length d.
- 3.
This usually means the sequencing machine could not determine the exact nucleotide.
- 4.
- 5.
The memory is used as a circular buffer and only written to or read from in bursts to maximize the read/write speeds.
- 6.
- 7.
- 8.
- 9.
The values following the \(\approx \) sign in Table 2 are extrapolated.
References
Cox, A.J., Bauer, M.J., Jakobi, T., Rosone, G.: Large-scale compression of genomic sequence databases with the burrows-wheeler transform. Bioinformatics 28(11), 1415–1419 (2012)
Deorowicz, S., Grabowski, S.: Compression of DNA sequence reads in FASTQ format. Bioinformatics 27(6), 860–862 (2011)
Du, K.L.: Clustering: a neural network approach. Neural Netw. 23(1), 89–107 (2010)
Fritz, M.H.Y., Leinonen, R., Cochrane, G., Birney, E.: Efficient storage of high throughput DNA sequencing data using reference-based compression. Genome Res. 21(5), 734–740 (2011)
Hussain, H.M., Benkrid, K., Seker, H., Erdogan, A.T.: FPGA implementation of k-means algorithm for bioinformatics application: an accelerated approach to clustering microarray data. In: 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 248–255, June 2011
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010). Award Winning Papers from the 19th International Conference on Pattern Recognition (ICPR) 19th International Conference in Pattern Recognition (ICPR)
Pinho, A.J., Pratas, D., Garcia, S.P.: Green: a tool for efficient compression of genome resequencing data. Nucleic Acids Res. 40(4), e27 (2011)
Pollard, K.S., van der Laan, M.J.: Bioinformatics and computational biology solutions using R and bioconductor. In: Gentleman, R., Carey, V.J., Huber, W., Irizarry, R.A., Dudoit, S. (eds.) Cluster Analysis of Genomic Data, pp. 209–228. Springer, New York (2005)
Stephens, Z.D., Lee, S.Y., Faghri, F., Campbell, R.H., Zhai, C., Efron, M.J., Iyer, R., Schatz, M.C., Sinha, S., Robinson, G.E.: Big data: astronomical or genomical? Plos Biol. 13(7), e1002195 (2015)
Winterstein, F., Bayliss, S., Constantinides, G.A.: FPGA-based k-means clustering using tree-based data structures. In: 23rd International Conference on Field programmable Logic and Applications. pp. 1–6, September 2013
Acknowledgments
The reasearch presented in this paper was funded by the Swiss PASC initiative in the framework of the PoSeNoGap (Portable Scalable Concurrency for Genomic Data Processing) project. The authors would like to thank all the participants for the fruitful discussions, namely Ioannis Xenarios, Thierry Schüpbach and Daniel Zerzion from SIB, Marco Mattavelli and Claudio Alberti from EPFL.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Petraglio, E., Wertenbroek, R., Capitao, F., Guex, N., Iseli, C., Thoma, Y. (2017). Genomic Data Clustering on FPGAs for Compression. In: Wong, S., Beck, A., Bertels, K., Carro, L. (eds) Applied Reconfigurable Computing. ARC 2017. Lecture Notes in Computer Science(), vol 10216. Springer, Cham. https://doi.org/10.1007/978-3-319-56258-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-56258-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56257-5
Online ISBN: 978-3-319-56258-2
eBook Packages: Computer ScienceComputer Science (R0)