FPGAs are proposed as a solution to speed up DNA variation identification while lowering power consumption compared to GPUs. FPGAs can handle both the large amounts of genomic data involved in analysis as well as the sequential and parallel processes like Hidden Markov Models 6 times faster than GPUs. FPGAs are also more power efficient than GPUs for algorithms like k-Nearest Neighbors that are 8 times less power consuming on FPGAs. Based on these factors, FPGAs are concluded to be the best solution for algorithms like XHMM and CLAMMS used in genomics analysis.

1. RATIONALE
BEHIND
FPGA
clementi.letizia@hotmail.it
matteo3.crespi@mail.polimi.it
claudio.scandella@outlook.it

2. 2OUR GOAL
Speed up the identification of
DNA variation while lowering
the power consumption

3. 3
HARDWARE AND
GENOMICS
GENOMICS ANALYSIS
INVOLVES
• Big amount of data
• Both sequential and parallel
processes
• Hidden Markov Model
(6 times faster on FPGA then on GPU)
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1287823

4. ALGORITHMS’ PIPELINE: XHMM
Mean per-target
coverage using GATK
Filter out «extreme»
target and simple
Filter target and samples
and calculate z-score
Mean center each
target and run PCA
HMM to merge targets
and call per-sample CNVs

5. ALGORITHMS’ PIPELINE: CLAMMS
• High Scalability
• Both parallel and sequential
processes
• k-Nearest Neighbors
(8 times less power consuming on
FPGA then on GPU)
• Depth Of Coverage with GATK
• HMM
https://www.ncbi.nlm.nih.gov/pubmed/26382196
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7160066

6. CONCLUSION
FPGA
are the best solution for
both algorithms

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