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SWAMP+: Enhanced Smith- Waterman Search for Parallel …...dynamic programming data dependencies....
Transcript of SWAMP+: Enhanced Smith- Waterman Search for Parallel …...dynamic programming data dependencies....
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U N C L A S S I F I E D
SWAMP+: Enhanced Smith-Waterman Search for Parallel
Models
Shannon Steinfadt, Ph.D.Los Alamos National Laboratory
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U N C L A S S I F I E D
OutlineMotivation for Sequence Alignment
Smith-Waterman Local Sequence Alignment
SWAMP
ASC • SWAMP using ASC Emulator
SWAMP+
SWAMP and SWAMP+ on Metal• ClearSpeed• Convey Computer
Contributions
Future Work
Questions?
gcggacgctccacg-tgtc--c—-ct-cgccgcgccc-cgtctacc
gggccctcctggctcccaacagcttctcagttc ccacttc||:|:||||::|-|::|--|--||-|-|:|:|::| ||-|:||
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U N C L A S S I F I E D
Motivation: Sequence Alignment
Given two sequences:
Align them to find the longest, most common subsequence
DNA nucelotides {A, G, T, C} Proteins {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V}
Query:IHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVA
Subject:MFCVQCEQTIRTPAGNGCSYAQGMCGKTAETSDLQDLLIAALQGLSAWAVKAREYGIINHDVDSFAPRAFFSTLTNVNFDSPRIVGYAREAIALREALKAQCLAVDANARVDNPMADLQLVSDDLGELQRQAAEFTPNKDKAAIGENILGLRLLCLYGLKGAAAYMEHAHVLGQYDNDIYAQYHKIMAWLGTWPADMNALLECSMEIGQMNFKVMSILDAGETGKYGHPTPTQVNVKATAGKCILISGHDLKDLYNLLEQTEGTGVNVYTHGEMLPAHGYPELRKFKHLVGNYGSGWQNQQVEFARFPGPIVMTSNCIIDPTVGAYDDRIWTRSIVGWPGVRHLDGDDFSAVITQAQQMAGFPYSEIPHLITVGFGRQTLLGAADTLIDLVSREKLRHIFLLGGCDGARGERHYFTDFATSVPDDCLILTLACGKYRFNKLEFGDIEGLPRLVDAGQCNDAYSAIILAVTLAEKLGCGVNDLPLSLVLSWFEQKAIVILLTLLSLGVKNIVTGPTAPGFLTPDLLAVLNEKFGLRSITTVEEDMKQLLSA
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U N C L A S S I F I E D
Motivation: Sequence Alignment
Given two sequences:
Align them to find the longest, most common subsequence
DNA nucelotides {A, G, T, C} Proteins {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V}
One of the most common fundamental tasks is local sequence alignment
Query: VIA-EPYRE-RLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDK: : :: : :: : : : :
Subject: LVSREKLRHIFLLGGCDGARGERHYFTDFATSVPDDCLILTLACGK
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U N C L A S S I F I E D
Pairwise Local Sequence Alignment
(derived by humans)
(preserved by evolution)
Similar CharactersSimilar Characters Similar StructureSimilar StructureSimilar FunctionSimilar Function
Ancestral RelationshipsGene Functionality
Aid in Drug DiscoveryAssembly of Raw Data
Ancestral RelationshipsGene Functionality
Aid in Drug DiscoveryAssembly of Raw Data
Homologous Sequences
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Compare all possible combinations of sequence characters against each other
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
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U N C L A S S I F I E D
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Aligning using Smith-Waterman Algorithm
Compare all possible combinations of sequence characters against each other
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U N C L A S S I F I E D
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Aligning using Smith-Waterman Algorithm
Compare all possible combinations of sequence characters against each other
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations of sequence characters against each other
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations of sequence characters against each other
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Aligning using Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Compare all possible combinations - but it has dynamic programming data dependencies
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U N C L A S S I F I E D
Smith-Waterman Recursive Matrix Equations
σ−⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧ −
=−
−
ji
jiji D
gCD
,1
,1. max
σ−⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧ −
=−
−
1,
1,, max
ji
jiji I
gCI
( )⎪⎩
⎪⎨⎧
≠
==
ji
jiji S2S1miss_cost
S2S1match_costS2,S1
if
ifd
Ci, j = max
Di .j
I i, j
Ci −1, j −1 + d S1i ,S2j( )0
⎧
⎨
⎪ ⎪
⎩
⎪ ⎪
⎫
⎬
⎪ ⎪
⎭
⎪ ⎪
g : gap extension cost
σ: gap opening cost
g
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U N C L A S S I F I E D
Traceback in the Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
1) Find the maximum computed value
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U N C L A S S I F I E D
Traceback in the Smith-Waterman Algorithm
Cost KeyMatch +10
Miss -3
Insert a Gap -3Extend a Gap -1
Alignment:CATTGC - -TG
1) Find the maximum computed value2) Traceback until you reach ‘0’s
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U N C L A S S I F I E D
Smith-Waterman Vectorization Approaches
Parallel Processing• Allows high-quality results in less time using the Smith-Waterman algorithm
Rognes described four basic approaches:• Vectors along the anti-diagonal (a wavefront) approach described by Wozniak• Vectors along the query (a single column split downward) described by Rognes
and Seeberg• A striped approach introduced by Farrar• Multi-sequence vectors described by Alpern et. al. and again by Rognes
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U N C L A S S I F I E D
Parallelizing the Smith-Waterman Algorithm
Sequential matrixof computed values
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U N C L A S S I F I E D
Parallelizing the Smith-Waterman Algorithm
Tilted data arrangementto parallelize and processa diagonal at a time.
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U N C L A S S I F I E D
Parallelizing the Algorithm: “Tilting” the Matrix
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U N C L A S S I F I E D
Parallelizing the Algorithm: “Tilting” the Matrix
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U N C L A S S I F I E D
Parallelizing the Algorithm: “Tilting” the Matrix
Smith-Waterman using Associative
Massive Parallelism (SWAMP)
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U N C L A S S I F I E D
SWAMP (Smith-Waterman using Associative Massive Parallelism)
Used PEs
Unused PEs
Order of Computations
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U N C L A S S I F I E D
ASC: Associative Architecture
SIMD with special associative featuresFine-grained parallelism
Designed for fast associative searchesContent-based searches, not memory address
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U N C L A S S I F I E D
ASC Advantages
Quick data movement in SIMD• Move raw data in parallel• At each step, PEs follow the algorithmic steps for data movement in lock step
No message passing like MPI/PVM• No store/forward• No headers• No explicit synchronizingVery fast operations for• Finding Maximum / Minimum• Finding if there are “Any Responders”• “Pick One” active PE
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U N C L A S S I F I E D
ASC - SWAMP Algorithm
1) Read in S1 and S2In Active PEs (those with data values for S1 or S2):2) Initialize the two-dimension variables D[$], I[$], C[$] to zeros.3) “Shift” or slide string S2 to create a titled matrix4) For every anti_diagonal (a_d) from 2 to m+n-1 do in parallel {5) If S2[$,a_d] is valid (S2 [$,a_d] ≠ “@” and S2[$,a_d] ≠ “-”) then {
6.1) Calculate score for deletion for D[$,a_d] 6.2) Calculate score for a insertion for I[$,a_d]6.3) Calculate matrix score for C[$,a_d] }7) local_maxPE=MAXDEX(C[$, a_d])8) if C[local_maxPE, a_d] > max_Val then {9.1) max_PE = local_maxPE9.2) max_Val = C[local_maxPE, a_d]) } }
10) Return max_Val, max_PE
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U N C L A S S I F I E D
SWAMP (Smith-Waterman using Associative Massive Parallelism)
Used PEs
Unused PEs
Order of Computations
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U N C L A S S I F I E D
SWAMP on ASC Analysis
Computation takes O(m+n) time with m+1 PEs
Sequential Smith-Waterman (Gotoh)• O(m*n) time, m*n space • When |S1| = |S2|, it becomes an O(n2) algorithm
SWAMP parallel algorithm• If actual number of PEs < m+1, assign {(m+1) / # PEs} work to each PE
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U N C L A S S I F I E D
SWAMP on ASC - Performance
•Based on actual measurements using ASC language and emulator.
• Predictions shown with the dashed line.
Predictions calculated using linear regression and the least squares method.
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U N C L A S S I F I E D
SWAMP+
SWAMP+ returns multiple non-overlapping sequences• Search and process with SWAMP multiple times• Return top k non-overlapping, non-intersecting sequences• Reveal additional information
— Spatial information— Length of comparisons— Identify regulatory regions and motifs
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U N C L A S S I F I E D
SWAMP+ - 3 variations
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U N C L A S S I F I E D
Moving SWAMP+ to Hardware
Have TheseHave These
Want ThisWant This
Used ThisUsed This
Associative SIMD Model - ASC
ctcgccgcgc ggcggacgct ccacgtgtcc cccgtctacc
gggccctcct ggctcccaac agcttctcag ttcccacttc
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U N C L A S S I F I E D
Hardware Analysis: ClearSpeed
Have TheseHave These
Want ThisWant This
Use ThisUse This
ClearSpeed Advance 620 PCI-X board
50 GFLOPS peak performance25W average power dissipation
ctcgccgcgc ggcggacgct ccacgtgtcc cccgtctacc
gggccctcct ggctcccaac agcttctcag ttcccacttc
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U N C L A S S I F I E D
SWAMP+ on ClearSpeed
Implemented SWAMP and SWAMP+ on the ClearSpeed board• Used the software equivalent of
— Maximum— Any Responders— Pick One
Allows accurate, deterministic timings for algorithms
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U N C L A S S I F I E D
ClearSpeed – SWAMP+ Algorithm1) Read in S1 and S2In Active PEs (those with data values for S1 or S2):2) Initialize Row 0, Col 0 variables D[$], I[$], C[$] to zeros.3) For each PE, shift S2 down 1, copy entire string4) For every a_d from 2 to m+n-1 do in parallel {5) If S2[$,a_d] ≠ “@” and S2[$,a_d] ≠ “-” then {
6.1) Calculate score for deletion and insertion for D[$,a_d]; Calculate matrix score for C[$,a_d] }7) local_maxPE=getPE(max_int(C[$, a_d]))8) if C[local_maxPE] > max_Val then {9.1) max_PE = local_maxPE9.2) max_Val = C[local_maxPE]) } }
10) Return max_Val, max_PE 11) Perform traceback12) Mark aligned values 13) Run alignment for all k values (2-12)
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U N C L A S S I F I E D
ClearSpeed SWAMP+ CUPS Performance
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U N C L A S S I F I E D
Average Calculation with Eight Highest Outliers RemovedCycle Counts
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
10 20 30 40 50 60 70 80 90 96
Sequence Lengths
1 Alignment
2 Alignments (1st)
2 Alignments (2nd)
3 Alignments (1st)
3 Alignments (2nd)
3 Alignments (3rd)
4 Alignments (1st)
4 Alignments (2nd)
4 Alignments (3rd)
4 Alignments (4th)
5 Alignments (1st)
5 Alignments (2nd)
5 Alignments (3rd)
5 Alignments (4th)
5 Alignments (5th)
ClearSpeed Calculation Times
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U N C L A S S I F I E D
Average Traceback Cycle Counts for all Alignments
0
50000
100000
150000
200000
250000
300000
350000
400000
10 20 30 40 50 60 70 80 90 96
Sequence Lengths
1 Alignment2 Alignments (1st)2 Alignments (2nd)3 Alignments (1st)3 Alignments (2nd)3 Alignments (3rd)4 Alignments (1st)4 Alignments (2nd)4 Alignmens (3rd)4 Alignments (4th)5 Alignments (1st)5 Alignments (2nd)5 Alignments (3rd)5 Alignments (4th)5 Alignments (5th)
First (Longest) Traceback Cycle
Counts
Shorter (2 through k) or Second to Fifth Alignments in this
instance
Clearspeed Traceback Timings
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U N C L A S S I F I E D
ClearSpeed Computation Time Comparison
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U N C L A S S I F I E D
SWAMP+ on ClearSpeed Analysis
Computation was O(m+n) using m+1 PEs
Showed similar performance for theoretical speedup as the ASC code
When m = n, the running time of O(n) with a coefficient of 2
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U N C L A S S I F I E D
Hardware Analysis: Conveyctcgccgcgc ggcggacgct ccacgtgtcc cccgtctacc
gggccctcct ggctcccaac agcttctcag ttcccacttc
Have TheseHave These
Want ThisWant This
Use ThisUse This
Convey Computer HC-1FPGA + x86 HybridConsists of a personality & application768 GCUPS peak from single machine
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U N C L A S S I F I E D
SWAMP+ on Convey Computer System
FPGA + x86 Hybrid system• Daughter FPGA board and an x86 closely married hardware
The Smith-Waterman alignment application has multiple components• Product consists of a personality (SW02) and an aligner application (cnysws)• Implements multiple systolic arrays on the AEs to perform parallel Smith-Waterman
searches
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U N C L A S S I F I E D
Convey Smith-Waterman Personality‡
4 AEs each contain 4 tiles• Query loaded into each tile• May join 2 or 4 tiles• Up to 1280 query length• Continuously process reference DB• Efficient for searching a database with many
short strings
‡Slide courtesy of Convey Computer
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U N C L A S S I F I E D
Convey Computer Smith-Waterman Performance‡
‡Graph courtesy of Convey Computer
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U N C L A S S I F I E D
Convey ComputerSWAMP+ on Non-Associative Parallel System
1) Run parallel Convey cnysws application with traceback for two files containing sequences (S1^) and database queries (S2^)2) Capture alignment information for all query and database matchesFor each query:
For each database match:3) Copy query and sequence into altered{Query,Database}_n_n.faa4) Mark aligned bases for each query-database match While # of iterations <k-1:
For each pair of files created in instruction 3:5) Run cnysws6) Repeat Step 27) If score for hit * δ < current score:
7.1) Track match7.2) Mark aligned bases as matched for query-database match
8) Output the k sub-alignments
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U N C L A S S I F I E D
Convey Computer SWAMP+ Run Times
0
2
4
6
8
10
12
14
16
Tim
e (
seco
nd
s)
Sequence Length
Average Computation Times
Avg. Program Time Reported
Avg. System Seconds Recorded Externally
*Other files are single sequence to sequence comparison. AA consists of 2 amino acid (AA) queries
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U N C L A S S I F I E D
SWAMP+ on Convey Analysis
Smith-Waterman execution efficient• X86 version uses a combination of the most efficient Smith-Waterman alignment
— Makes use Farrar extensions with SSE as well as North neighbor approximations
• Will use the x86 for larger comparisons and traceback• Computation was O(m+n) using m+1 PEs
Handles largest datasets of the implementationsUses the BLOSUM 25 and BLOSUM 50 substitution lookup tables for evolutionary models of similarityMaximum GCUPS values for hardware• HC-2: 1920 cells, 259 GCUPS• HC-2ex: 5120 cells, 768 GCUPS
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U N C L A S S I F I E D
Contributions
Created a new algorithm for the ASC platform• Implemented, tested SWAMP in the ASC language and emulator• Showed promising results and good scaling
Extended SWAMP to find more information between the sequences• Created the SWAMP+ suite of algorithms
— Single-to-multiple— Multiple-to-single— Multiple-to-multiple
Analyzed different hardware for best fit for ASC algorithmsImplemented the SWAMP and SWAMP+ algorithms on ClearSpeed Co-ProcessorDesigned and implemented SWAMP+ adaptation on Convey Hybrid System
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U N C L A S S I F I E D
Future Work
Convey Computer • Extend and update output to be more comprehensive• Quantitative / Qualitative comparison of BLAST results against SWAMP+ results• Timings and work analysis
Running against MPI implementations • Communication and system overhead impacts on cluster vs. more tightly coupled
system in addition to compute time
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U N C L A S S I F I E D
ReferencesO. Gotoh, "An improved algorithm for matching biological sequences," Journal of Molecular Biology, vol. 162, pp. 705-708, Dec 15 1982.S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, "Basic Local Alignment Search Tool," Journal of Molecular Biology, vol. 215, pp. 403-410, 1990.T. Rognes, "Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation," BMC Bioinformatics, vol. 12, 2011.A. Wozniak, "Using video-oriented instructions to speed up sequence comparison," Computer Applications in the Biosciences (CABIOS), vol. 13, pp. 145 - 150, 1997.M. Farrar, "Striped Smith-Waterman speeds database searches six times over other SIMD implementations," Bioinformatics (Oxford, England), vol. 23, pp. 156-161, Jan 15 2007.T. Rognes and E. Seeberg, "Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors," Bioinformatics (Oxford, England), vol. 16, pp. 699-706, Aug 2000.
…Please see paper for full reference list
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U N C L A S S I F I E D
Q & A
Contact Info:
Dr. Shannon Steinfadt
http://www.SwampAlign.com