Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors

By Torbjørn Rognes, Erling Seeberg. 2000

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Accelerated Profile HMM Searches (2011, methodology): “Several vector methods have been described for accelerating classical Smith/Waterman local sequence alignment [34,35], and methods for accelerating Smith/Waterman dynamic programming (DP) recursions are readily adapted to profile HMMs.”
CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units (2009, methodology): “Farrar [9] exploited the SSE2 instruction set to compute the SW algorithm in a striped pattern, outperforming the previous SIMD based SW implementations by Wozniak [15] and Rognes [16].”
SHRiMP: Accurate Mapping of Short Color-space Reads (2009, methodology): “The SHRiMP algorithm draws upon three recent developments in the field of sequence alignment: q-gram filter approaches, introduced by Rasmussen et al [14]; spaced seeds, introduced by Califano and Rigoutsos [15] and popularized by the PatterHunter family of tools [7,10]; and specialized vector compu”
Striped Smith-Waterman speeds database searches six times over other SIMD implementations (2007, unknown): “These sequences were used to test other algorithms including BLAST 2 (Altschul et al ., 1997) and SWMMX (Rognes et al ., 2000) Smith-Waterman implementation.”
CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment (2008, methodology): “As suggested by Rognes and Seeberg [9], to avoid the lookup of W(qi, dj) in the internal cycle of the algorithm, we pre-compute a query profile parallel to the query sequence for each possible residue.”
Fast and accurate de novo genome assembly from long uncorrected reads (2016, unknown): “4; Algorithm 3) is inspired by the Rognes and Seeberg Smith-Waterman intra-set parallelization approach (Rognes and Seeberg 2000).”
Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation (2011, background): “(B) Vectors along the query, described by Rognes and Seeberg [9].”
Linear Motif Atlas for Phosphorylation-Dependent Signaling (2008, methodology): “Pairwise sequence similarity was calculated [with the ParAlign software ( 55 )] as self-normalized bit scores between all domain sequences in the kinase, SH2, and PTB domain trees.”
160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA) (2007, methodology): “Alternative implementations have been used recently to accelerate the SW algorithm using software parallel programming on common microprocessors with a speed improvement up to six-fold [8].”