
@article {NelTow16A,
	title = {RAMPS: A Reconfigurable Architecture for Minimal Perfect Sequencing},
	journal = {IEEE Transactions on Parallel and Distributed Systems (TPDS)},
	volume = {27},
	year = {2016},
	abstract = {The alignment of many short sequences of DNA, called reads, to a long reference genome is a common task in molecular biology. When the problem is expanded to handle typical workloads of billions of reads, execution time becomes critical. In this paper we present a novel reconfigurable architecture for minimal perfect sequencing (RAMPS). While existing solutions attempt to align a high percentage of the reads using a small memory footprint, RAMPS focuses on performing fast exact matching. Using the human genome as a reference, RAMPS aligns short reads hundreds of thousands of times faster than current software implementations such as SOAP2 or Bowtie, and about a thousand times faster than GPU implementations such as SOAP3.  Whereas other aligners require hours to preprocess reference genomes, RAMPS can preprocess the reference human genome in a few minutes, opening the possibility of using new reference sources that are more genetically similar to the newly sequenced data.},
	author = {Chad Nelson and Kevin Townsend and Osama Attia and Phillip Jones and Joseph Zambreno}
}
@conference {NelTow12A,
	title = {Shepard: A Fast Exact Match Short Read Aligner},
	booktitle = {Proceedings of the International Conference on  Formal Methods and Models for Codesign (MEMOCODE)},
	year = {2012},
	month = {July},
	abstract = {The mapping of many short sequences of DNA, called reads, to a long reference genome is an common task in molecular biology. The task amounts to a simple string search, allowing for a few mismatches due to mutations and inexact read quality. While existing solutions attempt to align a high percentage of the reads using small memory footprints, Shepard is concerned with only exact matches and speed. Using the human genome, Shepard is on the order of hundreds of thousands of times faster than current software  implementations such as SOAP2 or Bowtie, and about 60 times faster than GPU implementations such as SOAP3. 

Shepard contains two components: a software program to preprocess a reference genome into a hash table, and a hardware pipeline for performing fast lookups. The hash table has one entry for each unique 100 base pair sequence that occurs in the reference genome, and contains the index of last occurrence and the number of occurrences. To reduce the hash table size, a minimal perfect hash table is used. The hardware pipeline was designed to perform hash table lookups very quickly, on the order of 600 million lookups per second, and was implemented on a Convey HC-1 high  performance reconfigurable computing system. Shepard streams all of the short reads through a custom hardware pipeline and writes the alignment data (index of last occurrence and number of occurrences) to a binary results array.},
	author = {Chad Nelson and Kevin Townsend and Bhavani Satyanarayana Rao and Phillip Jones and Joseph Zambreno}
}
