Genomic Data Clustering on FPGAs for Compression
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 . 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.
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.
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