Implementation of DNA sequence alignment algorithms using Fpga ,ML,and CNN

1. Implementation of DNA/RNA
Sequence Alignment
Algorithms using FPGA
Prepared by :Amr Rashed
Under Supervision of :
Assoc. Prof. Dr. Hossam El-Din Moustafa
Assis. Prof. Dr. Hanan abdelfatah

2. Author publications
Paper title year journal Impact
factor (ISI)
Accelerating DNA pairwise
sequence alignment using
FPGA and a customized
convolutional neural network
Volume
92, June
2021, 107112
Computers & Electrical
Engineering
2.663
Sequence Alignment Using
Machine Learning-based
Needleman–Wunsch Algorithm
Under review IEEE open access

3. Aim of the work Highlights Problem
Definition
Limitations Introduction to
bioinformatics
S/W Implementation
Proposed Fast
Technique ( H/W
implementation)
Convolutional neural
network Machine learning
3
AGENDA

4. Aim of the work
• The proposed implementation relies on
the complete parallelization of commonly
used sequence alignment algorithms (i.e.,
the Smith-Waterman algorithm and the
Needleman–Wunsch algorithm) under
certain limitations using efficient low-cost
hardware and software platforms to
overcome most of the problems of
dynamic programming and hardware
implementation.

5. Highlights
• An implementation based on a look-up-table (LUT)
to accelerate DNA sequence alignment algorithms
under certain limitations is presented.
• Our ROM-based hardware implementation
requires only O(N/4) cycles or calculation steps to
obtain the complete result or a maximum delay of
7.5 ns when implemented using combinational
circuits.
• The derivation of 254 patterns is presented for a
global alignment array for all the input
combinations.
• It represents a new use of classical ML and deep
CNN for global sequence alignment. Fifty-four
Boolean functions are derived for complete parallel
implementation of the sequence alignment
algorithm.
• It is valid for RNA/DNA sequences and applicable
to software and hardware design.
• Hardware implementation can further obtain more
tests and be evaluated for long sequences.

6. Problem Definition
(1) The number of sequences is large, and each of their
lengths can be very long.
(2) Table II shows that the algorithms used to align the
sequences requires O(MN) calculation steps and
consumes O(MN) time (M and N are the lengths of the
two input sequences).
(3) Basic sequence alignment algorithms are internally
dependent on the sequential process.
(4) Hardware implementation of sequence alignment
algorithms does not present an effective solution for
sequential process problems, which affects system
speed.
(5) Practical problems exist owing to parallel
implementations, such as the communication overheads.
(6) Dynamic algorithms guarantee optimal alignment,
but they are slower than FASTA and BLAST, and require
extensive computation time and memory because of the
sequential processes. Although FASTA and BLAST are
fast, they do not guarantee optimum alignment. Our
proposed algorithms
TABLE II COMPARE SPACE AND TIME COMPLEXITY FOR DP
ALGORITHMS [1]
Algorithm Type Space
Complexity
Time
Complexity
SW Local-linear
gap penalty
O(MN) O(MN)
Gotoh Local-affine
gap penalty
O(MN) O(MN)
Miller–
Myers
Local-affine
gap penalty
O(M+N) O(NM)
NW Global-linear
gap penalty
O(MN) O(MN)
Hirschberg Global-linear
gap penalty
O(M+N) O(MN)

7. Limitations
we propose using equal-length sequences (i.e., multiples of four N=4, 8, 12 ...) that can be applied to DNA
or RNA sequences because DNA and RNA sequences consist of four letters of the alphabet, representing
four NeocloBases despite the protein sequence which consists of 20 amino acids (letters).
The type of alignment used in this study is a pair-wise sequence, and our proposed technique is
applied for two algorithms: the SW algorithm for local alignment and the NW algorithm for
global alignment.
The proposed algorithm can also be applied to other local or global alignment algorithms such
as Gotoh, Miller–Myers, and Hirschberg. According to these assumptions, we can implement
fully concurrent or parallel software and hardware, which are faster than all the other
traditional implementations and do not require extensive computations that are time
consuming or power consuming.
This implementation is based on a lookup table (LUT). In a special case (NW algorithm), it can
be based on a DL model (CNN).

8. 8
Introduction to bioinformatics
9 Differences between smith-waterman and Needleman-Wunsch Algorithm
8 Dynamic Programming Algorithm for Sequence Alignment.
7 Public Sequence Databases.
6 Sequence Alignment Types, Description.
5 Dynamic Programming.
4 DNA,RNA Nucleic Acids.
3 Chromosome Structure.
2 From Cell-to-DNA.
1 DEF of Bioinformatics, DNA.

9. Bioinformatics
Bioinformatics is an interdisciplinary research area at between
computer science and biological science.
It is a union of biology and informatics as it involves the
technology that uses computers for
(1)Storage,
(2)Retrieval,
(3)Manipulation and distribution of information related to
biological macromolecules such as DNA, RNA and proteins.
Major research efforts includes:
(1)Sequence alignment,
(2)Gene finding,
(3)Genome assembly, drug design, drug discovery , protein structure
alignment, protein structure prediction, genome-wise association studies
and modeling of association.

10. DNA
DEFINITION
DNA is the hereditary material or complex
molecule founds inside every cell in all living things.
 It contains the instructions an organism needs to
develop, live and reproduce.
 These instructions tell the cell what role it will play
in our body.
 Nearly every cell in a person’s body has the same
DNA.
 Most DNA is located in :
(1)The cell nucleus (where it is called nuclear DNA),
(2) A small amount of DNA can also be found in the
mitochondria (where it is called mitochondrial DNA
or mtDNA).
Because the cell is very small, and because
organisms have many DNA molecules per cell, each
DNA molecule must be tightly packaged. This
packaged form of the DNA is called a chromosome.
An organism's complete set of nuclear DNA is called
its genome (our genome is made of a chemical
called DNA).

11. From Cell–
to-DNA
11

12. Chromosome
Structure
• DNA forms the inherited genetic material
inside each cell of a living organism . Each
segment in DNA which encodes for a protein
is called a gene.
12

13. 13
DNA RNA
Nucleic Acids(DNA,RNA)

14. Standard
amino-acids

15. DNA to RNA to Protein, Illustrating the Genetic Code

16. gatcctccat atacaacggt atctccacct caggtttaga tctcaacaac ggaaccattg
ccgacatgag acagttaggt atcgtcgaga gttacaagct aaaacgagca gtagtcagct
ctgcatctga agccgctgaa gttctactaa gggtggataa catcatccgt gcaagaccaa
gaaccgccaa tagacaacat atgtaacata tttaggatat acctcgaaaa taataaaccg
ccacactgtc attattataa ttagaaacag aacgcaaaaa ttatccacta tataattcaa
agacgcgaaa aaaaaagaac aacgcgtcat agaacttttg gcaattcgcg tcacaaataa
attttggcaa cttatgtttc ctcttcgagc agtactcgag ccctgtctca agaatgtaat
aatacccatc gtaggtatgg ttaaagatag catctccaca acctcaaagc tccttgccga
gagtcgccct cctttgtcga gtaattttca cttttcatat gagaacttat tttcttattc tttactctca
catcctgtag tgattgacac tgcaacagcc accatcacta gaagaacaga acaattactt
aatagaaaaa ttatatcttc ctcgaaacga tttcctgctt ccaacatcta cgtatatcaa
gaagcattca cttaccatga cacagcttca gatttcatta ttgctgacag ctactatatc
actactccat ctagtagtgg ccacgcccta tgaggcatat cctatcggaa aacaataccc
cccagtggca agagtcaatg aatcgtttac atttcaaatt tccaatgata cctataaatc
gtctgtagac aagacagctc aaataacata caattgcttc gacttaccga gctggctttc
gtttgactct agttctagaa cgttctcagg tgaaccttct tctgacttac tatctgatgc
gaacaccacg ttgtatttca atgtaatact cgagggtacg gactctgccg
http://www.ncbi.nlm.nih.gov/genbank/samplerecord/
Sample DNA
Sequence

17. MLRGSARTYWTLTGLWVLLRAGTLVVGLLFQRLFDALGAG
GGVWLIIALVAAIEAGRLFLQFGVMINRLEPRVQYGTTARL
RHALLGSALRGSEVTARTSPGESLRTVGEDVDETGFFVAW
APTNLAHWLFVAASVTVMMRIDAVVTGALLALLVLLTLVT
ALAHSRFLRHRRATRAASGEVAGALREMVGAVGAVQAAA
AEPQVAAHVAGLNGARAEAAVREELYAVVQRTVIGNPAPI
GVGVVLLLVAGRMDEGTFSVGDLALFAFYLQILTEALGSIG
MLSVRLQRVSVALGRITNNLGCRLRRSLERASPPIASDAPG
GTGEGAAAPDAGPEPAPPLRELAVRGLTARHPGAGHGIED
VDLVVERHTVTVVTGRVGSGKSTLVRAVLGLLPHERGTVL
WNGEPIADPASFLVAPRCGYTPQVPCLFSGTVRENVLLGR
DGAAFDEAVRLAVAEPDLAAMQDGPDTVVGPRGLRLSG
GQIQRVAIARMLVGDPELVVLDDVSSALDPETEHLLWERLL
DGTRTVLAVSHRPALLRAADRVVVLEGGRVEASGTFEEVM
AVSAEMGRIWTGAGPGGGDAGPAPQSPPAG
https://www.uniprot.org/
Sample
Protein
Sequence

18. Biological Sequence Alignment
• Is a way of arranging two (Pairwise
Alignment) or more (Multiple Sequence
Alignment) biological sequences
(e.g., DNA, RNA, or Protein sequences)
of characters to identify regions of similarity.
Similarities may be a consequence of
functional or evolutionary relationships
between these sequences.
• Main types :
• 1- Pairwise Sequence Alignment.
• 2- Multiple Sequence Alignment.

19. Pairwise Sequence Alignment
Pairwise Alignment
Heuristic programming
BLAST
(local)
FASTA
(Local)
SIM2
Dynamic programming
Global Alignment
Needleman-Wunsch
(NW)
Hirschberg
Local Alignment
Smith-Waterman (SW)
Gotoh
Miller-Myers
Dot-matrix technique
(Global)

20. Pairwise Sequence Alignment
COMPARISON BETWEEN PWSA ALGORITHMS
Type Optimal or Exact
Methods
Suboptimal
Methods
Optimized Methods
Methods SW Local
alignment
, NW Global
alignment
Heuristic
programming
BLAST, FASTA,
SIM2
Gotoh, Miller–Myers local
alignment, Hirschberg
Global alignment
Advantages
/disadvantages
Accurate but not
fast
Fast but not
accurate
Accurate and optimized
than SW, NW

21. Alignment
Types
Alignment Types Algorithm Examples
Exhaustive Alignment Brute Force generates the list of all possible alignments
between two sequences, score them and select the
alignment with the best score. It is practically useless.
Global Alignment Compares the entire sequence of two genomes, end-to-
end.This alignment is useful when comparing closely
related sequences .Examples:Needleman-Wunsch,
Hirschberg.
Semi-Global
(Glocal)Alignment
Searching for the best alignment between a short a
long. semi-global alignment is useful is when one
sequence is short and the other is very long.
Local Alignment Does not look for total sequence but it compares
segments of all possible lengths and optimizes the
similarity measures. More flexible than the global
alignment. Examples :Smith-Waterman, Gotoh, Miller-
Myers.
Database Search BLAST, FASTA.

22. Dynamic Programming
•“Divide-and Conquer” strategy
•Breaks the problem down into
smaller sub-problems
1.Solve the smaller sub-problems optimally.
2.Use the sub-problem solutions to construct
the optimal solution to the original problem.
•Can be applied to problems that consist of overlapping sub-
problems.
22

23. Public Sequence Databases
•NCBI Gene Bank
•http://ncbi.nih.gov
•contains many sub-databases
•Protein Data Bank
•http://www.rcsb.org
•contains protein structures
•SwissProt
•http://www.expasy.org/sprot/
•contains annotated protein sequences
•Prosite
•http://kr.expasy.org/prosite
•contains motifs of protein active sites
23

24. Dynamic
Programming
Algorithm for
Sequence
Alignment, Steps
Process
steps
Set the reference sequence across the top of the NxM matrix
and the read sequence along the side.
1
Initialize the first row and first column of the score matrix
(values depend on algorithm)
2
For each element, derive scores from neighboring above,
above-left, and left units.
3
For each element, compute match and mismatch scores on
above-left score and gap score on above and left scores.
Choose maximum of computed score as final score.
4
Once all elements in matrix are filled, find the highest score,
which is where the last base in the alignment occurs.
5

29. Seq1=TGGTG
Seq2=ATCGT
Seq1=M
Seq2=N
Step 1: Matrix Initialization
M T G G T G
N
A
T
C
G
T
i=0 i=1 i=2 i=3 i=4 i=5
J=0
J=1
J=2
J=3
J=4
J=5
T(4,3)
T(i-1,J-1)=T(3,2)
T(i,J-1)=T(4,2)
T(i-1,J)=T(3,3)
0
Scoring Scheme:
+1 for match
-1 for mismatch
-2 for gap
X
X
= 0-2
-2 -4 -6 -8 -10
-2

33. General workflow

34. Smith-
waterman
Algorithm
Example
Create Matrix
Calculate Matrix
Reverse Sequences
Backtrack
Others

35. Comparison between Smith-Waterman Algorithm and the Needleman-Wunch Algorithm
35
Smith-Waterman algorithm Needleman–Wunsch algorithm
Type ,Complexity, and
Running time
Local alignment algorithm,
Complexity is O (n2).
Runs in O(mn) time, where m and n are the lengths of the
two sequences
Global alignment algorithm,
Complexity is O (n2).
Runs in O (mn) time, where m and n are the lengths of the two
sequences .
Partial or Global
Comparison
It does not look for the total sequence, but it compares
segments of all possible lengths and optimizes the similarity
measures
It compares the entire sequence of two genomes, end-to-end.
Flexibility More Less
Suitable for Pairwise alignment Pairwise alignment, similar length, with a significant degree of
similarity throughout
Gaps It does not penalize gaps in the beginning and end of a
sequence.
Penalize gaps in the beginning and end of a sequence.
Steps: Initialization,
scoring
Traceback
The first row and first column are set to 0.
A negative score is set to 0.
Begin with the highest score, end when 0 is encountered
author shows algorithm steps
،
equations
،
and scoring matrix.
The first row and first column are subject to the gap penalty.
The score can be negative.
Begin with the cell at the lower right of the matrix, end at the
top-left cell
Example Sequence 1: GCCCTAGCG
Sequence 2: GCGCAATG
a match score of +1, a mismatch of -1, and a constant gap
penalty of -1
Sequence 1: GCCCTAGCG
Sequence 2: GCGCAATG
a match score of +1, a mismatch of -1, and a constant gap penalty
of -1
GCCCTAGCG
| | |
GCGCAATG
GCCCTAGCG
| | : | | : : |
GCGC – AATG

36. Linear systolic array
• A linear systolic array is
an array of processing
cores where each cell
shares its data with the
other cells in the array.
Each processing core
solves a subproblem and
shares the solution to all
the other cells in the array
to prevent calculation of
the same problem twice.

38. PROPOSED ALGORITHM

39. Binary Representation of DNA Nucleotides

40. PORTION OF NW ALGORITHM TRUTH-TABLE
PORTION OF NW ALGORITHM TRUTH-TABLE
Alignment Arrays (Output) Fixed length of 18
characters
Related DNA Sequences
Truth Table (Binary Data)
Decimal
NW Algorithm
Sequence1-sequence2
Sequence1-sequence2
'AAAA||||AAAA000000'
AAAA-AAAA
00000000-00000000
0
'AAAA||| AAAC000000'
AAAA-AAAC
00000000-00000001
1
….
'AAAA ::CCTT000000' (Full Mismatch)
AAAA-CCTT
00000000-01011111
95
….
'TTTT||||TTTT000000'
TTTT-TTTT
11111111-11111111
65535

41. PORTION OF SW ALGORITHM TRUTH-TABLE
PORTION OF SW ALGORITHM TRUTH-TABLE
Alignment Arrays (Output) Fixed length of 12
characters
Related DNA
Sequences
Truth Table (Binary
Data)
Decimal
SW Algorithm
Sequence1-
Sequence 2
Sequence1-sequence2
'AAAA||||AAAA'
AAAA-AAAA
00000000-00000000
0
'AAA|||AAA000'
AAAA-AAAC
00000000-00000001
1
….
'000000000000' (Full Mismatch)
AAAA-CCTT
00000000-01011111
95
….
'TTTT||||TTTT'
TTTT-TTTT
11111111-11111111
65535

42. Relation
between length
of array and
number of
instances

43. Relation
between
length of
array and
number of
instances
Relationship between Length of Local Alignment Arrays and
Number of Instances of Each Length

44. Mismatch Conditions
DIFFERENCE BETWEEN LOCAL AND GLOBAL ALIGNMENT ARRAYS OF SOME FULL MISMATCH CONDITIONS
Local Alignment array
(Full Mismatch 12 character)
Global Alignment
array
(NCBI)
Global Alignment array
(18 character)
(Matlab Function)
Related DNA
Sequences
Truth Table
(Binary Data)
Decimal
value
Smith-Waterman Algorithm
Needleman-Wunsch
Algorithm
Needleman-Wunsch
Algorithm( Full Mismatch)
Sequence 1-
Sequence 2
Sequence 1-
sequence 2
'000000000000'
'AAAA CCCC'
'AAAA CCCC000000'
AAAA-CCCC
00000000-
01010101
85
'000000000000'
'AAAA CCCG'
'AAAA :CCCG000000'
AAAA-CCCG
00000000-
01010110
86
'000000000000'
'AAAA CCTG'
'AAAA ::CCTG000000'
AAAA-CCTT
00000000-
01011111
95
'000000000000'
'AAAA CGGG'
'AAAA :::CGGG000000'
AAAA-CGGG
00000000-
01101010
106

45. LUT
COMPUTATIONAL
PARAMETER
LUT COMPUTATIONAL PARAMETER
Parameters Settings
Alignment Algorithm Local, and Global
Type of Gap Penalty Linear
Gap Opening Gap Opening -5
Gap Extension Gap Extension -5
Substitution Matrix BLOSUM 50
Word Length for each
sequence
8-bits (default)

46. Analysis of the
SW and NW
Alignment
Arrays
Number of Unique and Repeated Alignment Arrays for
SW and NW Algorithms
NW
Algorithm
Count (%)
SW Algorithm
Count (%)
Number of Alignment
Arrays
65536
(100%)
4836 (7.37%)
Unique Alignment
arrays
0 (0%)
60700
(92.62%)
Remaining Alignment
arrays
65536
(100%)
65536 (100%)
Total Number

47. Analysis of the
SW
Alignment
Arrays
Frequent Alignment Arrays for SW Algorithm
Percentage
Count
Frequent
Alignment Arrays
6.86%
4498
A|A000000000
12.39%
8124
C|C000000000
9.99%
6548
G|G000000000
6.18%
4051
T|T000000000
35.43%
23221 out of
65536
Total

48. Analysis of the
SW and NW
Alignment
Arrays
ALIGNMENT ARRAYS ACCORDING TO NUMBER OF MATCHES
Type of Alignment Array SW Alignment Array
Count
NW Alignment Array
Count
Full mismatch alignment
arrays
1812 (2.76%) 9118 (13.91%)
Alignment arrays with one
match
29371 (44.81%) 24192 (36.91%)
Alignment arrays with two
matches
28310 (43.19%) 24654 (37.62%)
Alignment arrays with three
matches
5787 (8.83%) 7316 (11.16%)
Full match alignment arrays 256 (0.39%) 256 (0.39%)
Total number of alignment
arrays (216)
65536 (100%) 65536 (100%)

49. Software Performance Evaluation(SW)
SW ALGORITHM SOFTWARE IMPLEMENTATION RESULTS FOR SINGLE-CORE AND SIX-CORE PLATFORMS
Platform Query
Length
4 5000 10k 1M 2M 3M 4M 4.4M
Single
core
Time(s) 30.7µs 0.0360 0.0704 6.8336 13.0377 20.7643 27.2987 30.21
GCUPS 5.6153e-
04
0.645 1.4198 146.3361 290.03 433.43 586.109 640
6-cores Time(s) 45.152
µs
0.0828 0.0821 2.0798 4.0322 5.9160 7.6441 7.8607
GCUPS 3.5436e-
04
0.3019 1.2174 480.8071 992.0214 1521.3 2093.1 2462.9

50. Software Performance Evaluation(NW)
NW ALGORITHM SOFTWARE IMPLEMENTATION RESULTS FOR SINGLE-CORE AND SIX-CORE PLATFORMS
Platform Query
Length
4 5000 10k 1M 2M 3M 4M 4.4M
Single
core
Time(s) 44.8µs 0.0363 0.0773 6.9019 16.4631 21.5660 28.9469 31.57
GCUPS 3.5714e-
04
0.6883 1.2933 144.8879 242.9680 417.3234 552.7355 630
6-cores Time(s) 57.995 µs 0.0824 0.0958 2.5491 4.0174 7.2047 8.6390 8.3676
GCUPS 2.7589e-
04
0.3035 1.0435 392.2945 995.6808 1249.2 1852.1 2313.7

51. Performance comparison with other state-of-the-art implementations
Performance comparison with other state-of-the-art implementations
Paper Year Platform Sequence Pairs Time (s) GCUPS
[15] 2014 1 Xeon Phi D4.4 vs D4.6M 700 29.2
[15] 2014 2 Xeon Phis D4.4 vs D4.6M 396 51.7
[15] 2014 4 Xeon Phis D4.4 vs D4.6M 203 100.7
[16] 2014 Intel® Core™ i7-3770 CPU @ 3.40GHz×8. 256NT vs 265NT 0.317 --
[17] 2019 2×Xeon Gold 6138 Max Query length=5478 -- 734
Ours (SW
algorithm)
Intel Core I7-9750H 6-cores 2.60 GHz
CPUs
Same sequence pairs in [21], after
cropping the second sequence to 4.4M
7.8607 2462.9
Ours (SW
algorithm)
Intel Core I7-9750H 6-cores 2.60 GHz
CPUs
256NT vs 265NT same length as in [15] 0.1745 --
Ours (NW
algorithm)
Intel Core I7-9750H 6-cores 2.60 GHz
CPUs
Same sequence pairs in [21], after
cropping the second sequence to 4.4M
8.3676 2313.7

52. Hardware Implementation of SW Algorithm
Encode Alignment's Arrays
Convert characters into
Hexadecimal
SW Algorithm
Hardware implementation
With virtex6 FPGA ROM (with &
without clock)
Two Design Comparison
Flowchart of local alignment hardware implementation

53. Encode
Alignment's
Arrays of Smith-
Waterman
Algorithm
CHARACTERS AND PROPOSED HEX-DECIMAL REPRESENTATION
Decimal
Binary
Hex-
Decimal
Function
Description
Character
0
0000
X’0’
Padding
Zero
‘0’
1
0001
X’1’
Match
Vertical bar
‘|’
2
0010
X’2’
Mismatch
Colon
‘:’
3
0011
X’3’
Gap
Hyphen
‘-‘
4
0101
X’4’
Mismatch
Space
‘ ‘
10
1010
X’A’
Nucleotide
A
‘A’
11
1011
X’B’
Nucleotide
C
‘C’
12
1100
X’C’
Nucleotide
G
‘G’
13
1101
X’D’
Nucleotide
T
‘T’

54. Examples
EXAMPLES OF LOCAL ALIGNMENT ARRAYS AND ASSOCIATED HEX-DECIMAL REPRESENTATION
DNA INPUT
SEQUENCES
DECIMAL
VALUE
ALIGNMENT ARRAYS HEXADECIMAL
REPRESENTATION
‘AAAA-AAAA’ 0 ‘AAAA||||AAAA’ ‘AAAA1111AAAA’
‘AAAA-AAAC’ 1 ‘AAA|||AAA000’ ‘AAA111AAA000’
‘AAAA-AAAG’ 2 ‘AAA|||AAA000’ ‘AAA111AAA000’
‘AAAA-AAAT’ 3 ‘AAA|||AAA000’ ‘AAA111AAA000’
‘AAAA-AACA’ 4 ‘AAAA|| |AACA’ ‘AAAA1141AABA’

55. Hardware
Implementation
of SW Algorithm

56. Simulation results

57. Simulation results
Simulation Result of 65536 ×48 ROM using Altera Quartus Prime Pro 16.0

58. System Design Summary using FPGA Virtex 6
Table XVII System Design Summary using Virtex 6- XC6VLX240T-1FF1156
Design Name/Basic
Feature
Number
Using
Block
RAM
Only
Number of Slices
LUT
Number of
LUT flip flop
pairs used
Maximum
Delay(ns) or Max
frequency (MHZ)
Estimated
Power
using
Xpower
Design 1 (with
clock)
96/416(2
3%)
-- -- 400MHZ,2.499(ns) 3.422 W
Design 2(without
clock)
-- 95500/150720(63%) 95500(100%) 65MHZ,15.355(ns) 3.422 W

59. Hardware Implementation of NW Algorithm
Comparison between three CNN
designs
NW Algorithm
Encode alignment's arrays
Convert characters to binary
Class Reduction
1- Replace amino acid letters by asterisk '*' (254 classes)
2- Merge all full mismatched patterns into one pattern (239
classes)
Test classical ML classifiers with four
datasets
Reshape input sequences to 2D input
matrix Implementation of 2D-CNN
Boolean function minimizations
Hardware implementation using Xilinx
FPGA combinational circuits
Comparison between four
different designs
Block diagram of the software and hardware implementation of global alignment algorithm

60. Encoding and Class Reduction for NW Algorithm
ORIGINAL ALIGNMENT ARRAY'S CHARACTERS TO BINARY ENCODING
Symbol Description Function Binary Decimal
' ' Space Mismatch & padding 000 0
A Nucleotide Letter in alignment array 001 1
C Nucleotide Letter in alignment array 010 2
G Nucleotide Letter in alignment array 011 3
T Nucleotide Letter in alignment array 100 4
'|' Vertical bar Match 101 5
':' Colon Mismatch 110 6
'-' Hyphen Gap 111 7

61. CHARACTERS TO BINARY REPRESENTATION AFTER USING
AN ASTERISK
CHARACTERS TO BINARY REPRESENTATION AFTER USING AN ASTERISK
Symbol Description Function Binary Decimal
'*' Asterisk Represent letters in alignment
array
000 0
':' Colon Mismatch 001 1
' ' Space Mismatch & padding 010 2
'-' Hyphen Gap 011 3
'|' Vertical bar Match 100 4

62. COMPARISON BETWEEN WIDELY USED LOGIC FUNCTION MINIMIZATION
METHODS
COMPARISON BETWEEN WIDELY USED LOGIC FUNCTION MINIMIZATION METHODS
Karnaugh Map (K-Map) Quine–McCluskey (QM) Espresso algorithm
Definition
Method for simplifying Boolean
algebra expressions
Known as the tabulation method or the technique of prime
implicants, it is functionally similar to Karnaugh Maps.
A radically different approach,
the algorithm manipulates "cubes," performing the product
terms in the ON-, DC-, and OFF covers iteratively.
Features
Four variables.
Unsuitable for more than 6 input variables
Tedious and error-prone process
It can be performed manually and does
not support more than 8 input bits.
Challenge to be implemented in computer
programs[66][67].
K-Map is not suitable for our algorithms
because these have 16 input bits.
Can still be performed manually on paper
Scales to many variables (Can handle up to 40 variables)
One of the highly effective techniques for simplifying Boolean
expressions.
More convenient to be implemented in computer programs.
Has a tabular form that makes it more efficient for use in
computer algorithms
Has a settled methodology to test whether the minimal form of a
Boolean function has been attained.
For a larger number of input variables, QM is more effective in
minimizing logic functions than K-Map[66][67].
Is not guaranteed to be the global minimum.
Practically, it is very closely approximated and free from
redundancy.
Computationally efficient in terms of both memory
requirement and time than the other methods (K-Map and
QM) by several orders of magnitude
Used as a standard logic expression minimization in logic
synthesis tools

63. EVOLUTION OF THE NUMBER OF MINTERMS IN EACH BOOLEAN
FUNCTION
EVOLUTION OF THE NUMBER OF MINTERMS IN EACH BOOLEAN FUNCTION
Functions Original "A,C,G,T"
minterms count
First Reduction (254)
minterms count
Second Reduction
(239)
minterms count
Fast Minimization
minterms count
Exact
Minimization
minterms count
F0 23627 0 0 0 0
F1 35562 8590 8590 782 763
F2 37410 8590 8590 782 763
F3 17854 0 0 0 0
F4 34350 2347 2347 467 467
…
F52 616 65102 65102 150 150
F53 732 452 452 176 176
TOTAL 1101952 701012 701012 29858 26538

64. NW ALIGNMENT STATISTICS
NW ALIGNMENT STATISTICS
NW Alignment arrays after Replacement of each letter by an asterisk
Count
Alignment Arrays
254
Number of Unique
65282
Number of Repeated
65536
Total Number of Alignment

65. CHARACTERS TO BINARY REPRESENTATION AFTER USING AN ASTERISK FOR
ALIGNMENT ARRAY
CHARACTERS TO BINARY REPRESENTATION AFTER USING AN ASTERISK FOR ALIGNMENT ARRAY
Binary Representation
54-bits
After replacement of
each letter by an
asterisk (18 characters)
NW Algorithm
Alignment Arrays (18
characters)
Sequence 1-
Sequence 2
Decimal
Value
000-000-000-000-100-100-100-100-000-
000-000-000-010-010-010-010-010-010
'****||||**** '
'AAAA||||AAAA000000'
AAAA-AAAA
0
000-000-000-000-100-100-100-010-000-
000-000-000-010-010-010-010-010-010
'****||| **** '
'AAAA||| AAAC000000'
AAAA-AAAC
1
….
000-000-000-000-001-001-001-001-000-
000-000-000-010-010-010-010-010-010
'****::::**** '
'AAAA ::CCTG000000'
Full mismatch
AAAA-CCTT
95
….
000-000-000-000-100-100-100-100-000-
000-000-000-010-010-010-010-010-010
'****||||**** '
'TTTT||||TTTT000000'
TTTT-TTTT
65535

66. Block diagram for Boolean function minimization
Truth Table of NW Algorithm
Functions Derivation
(54 functions)
Functions Minimization
(fast method, exact method)
MATLAB to VHDL syntax
conversion
Block diagram for Boolean function minimization
Check

67. Example

68. Example
Figure Example of F0=0 and F1 (782 minterms) Minterm Boolean functions after fast minimization

69. A portion of minimized F1 Boolean function
after altering syntax to MATLAB syntax
A portion of minimized F1 Boolean function after altering syntax to MATLAB syntax

70. A portion of minimized F1 Boolean function after changing the syntax to VHDL syntax

71. Simulation results
System Behavioral Simulation (ISim) using ISE Design suite 14.7

72. System
design
System Design Summary
Device Family Virtex 6
Device Name XC6VLX240T-1FF1156
Analysis and synthesis resource usage summary
Slice Logic Utilization
Design Name/Basic Feature Design 1 (No Signals
or Variables)
Design 2 (Signals
inside Process)
Design 3 (Variables
Used)
Design 4 (Signals used
without process)
Number of Slice
LUTs
21658/150720 (14%) 21835/150720 (14%) 21592/150720 (14%) 21872/150720 (14%)
Number used as Logic 21658/150720 (14%) 21835/150720 (14%) 21592/150720 (14%) 21872/150720 (14%)
IO Utilization
Number of bonded
IOBs
70/600 (11%) 70/600 (11%) 70/600 (11%) 70/600 (11%)
Timing Summary
Maximum Combinational
Path Delay
8.047 ns 7.904 ns 7.731 ns 7.511 ns
Power Analysis
Estimated Power 3.422 W 3.422 W 3.422 W 3.422 W

73. System design summary
SYSTEM DESIGN SUMMARY USING VIRTEX 6- XC6VLX240T-1FF1156
Design Name/Basic Feature Number of Slices
LUT
Maximum
Combinational
Path Delay (ns)
Estimated Power
Using Xpower
Design 1 (No Signals or
Variables)
21658/150720
(14%)
8.047 ns 3.422 W
Design 2 (Signals inside
Process)
21835 /150720
(14%)
7.904 ns 3.422 W
Design 3 (Variables Used) 21592 /150720
(14%)
7.731 ns 3.422 W
Design 4 (Signals used
without process)
21872 /150720
(14%)
7.511 ns 3.422 W

74. Performance evaluation(1)
COMPARISON OF THE PERFORMANCE OF VARIED SINGLE-DEVICE IMPLEMENTATIONS OF THE SW/NW ALGORITHM
Paper Year Algorithm Circuit Type Technique Device Frequency
(MHz)
Time
(ns)
GCUPS
[21] 2009 SW/NW Sequential Systolic cell Xilinx Virtex-4 FX100 100 -- 25.6
[22] 2014 SW Sequential Systolic cell Altera Stratix IV
EP4SGX230
57.9 17.27 3.71
[23] 2016 SW Sequential Systolic cell Xilinx XC3S1600E 98.7 10.13 23.79
[24] 2017 SW Sequential Systolic cell -- 250 -- --
[24] 2017 SW Sequential Systolic cell -- 250 -- --
Ours -- SW -Design1 Sequential LUT Xilinx Virtex 6
XC6VLX240T
400 2.499 25.6102
Ours -- NW -Design 4 Combinational LUT Xilinx Virtex 6
XC6VLX240T
133 7.511 8.5333

75. Performance evaluation(2)
COMPARISON OF USAGE OF FPGA RESOURCES
Paper Year Device Frequency
(MHz)
LUT BRAM Others
[25] 2017 -- 250 3056/ 537600 (0.57%) 94/3456 (2.72%) FF- 2468 (0.23%)
[26] 2018 Stratix IV
EP4SGX230KF40C2
125 70,839 / 182,400
(39%)
1,834,562 /
14,625,792 (13%)
Total Registers 73,850
[27] 2018 Kintex7 XC7K480T
FFG901-3
100 17462 (8%) -- FF-1402 (1%)
Latches-1483 (1%)
LUT-FF pairs 216 (5%)
Ours -- Xilinx Virtex 6
XC6VLX240T
400 0% 96/416 (23%) 0%
Ours -- Xilinx Virtex 6
XC6VLX240T
-- 21872 /150720 (14%) 0% 0%

76. Performance evaluation(3)
COMPARISON OF NUMBER OF OPERATIONS, STEPS, OR CLOCK CYCLES FOR COMPLETE ALIGNMENT
Paper Year Algorithm Technique # of Operations for complete
alignment
[9] 2012 SW Systolic array (N+M−1+Trace-back operations)
[28] 2018 SW Systolic array (N+M−1+Trace-back operations)
[29] 2020 SW Systolic array (N+M−1+Trace-back operations)
Ours -- SW/NW LUT (N/4)

77. Performance evaluation(4)
PERFORMANCE EVALUATION OF SOFTWARE AND HARDWARE IMPLEMENTATION
Sequence Pairs Algorithm Type Time
4NT vs 4NT SW Software-single core 30.7 µs
4NT vs 4NT NW Software-single core 44.8 µs
4NT vs 4NT SW Hardware-FPGA-Design 1 2.499 (ns)
4NT vs 4NT NW Hardware-FPGA-Design 4 7.511 (ns)

78. CNN for GLOBAL
SEQUENCE ALIGNMENT
CONVOLUTIONAL NEURAL NETWORK

79. Label
Encoding for
254 alignment
patterns
Label Encoding for 254 alignment patterns
Encoded Indices
Unique Alignment (254) Patterns
130
'****||||**** '
128
'****||| **** '
129
'****|||:**** '
124
'****|| |**** '
122
'****|| **** '
123
'****|| :**** '
127
'****||:|**** '
125
'****||: **** '
126
'****||::**** '

80. A Frequency Table for Alignment 239 Patterns
A Frequency Table for Alignment Patterns in Descending Order
Percentage
Count
Unique Alignment Patterns (239 Patterns)
13.9130 %
9118
'****::::**** '
1.7578 %
1152
'**** | **** '
1.7578 %
1152
'**** | **** '
1.5228 %
998
'**** || **** '
1.4038 %
920
'****| | **** '
1.4038 %
920
'**** | |**** '
1.3824 %
906
'**** |: **** '
1.3824 %
906
'**** :| **** '
1.2970 %
850
'**** | :**** '
1.2970 %
850
'****: | **** '

81. DATASET
DESCRIPTION
DATASET DESCRIPTION
Database 1 Database 2 Database 3
Reshape
of Input
Data
16-bit input
are reshaped
as 4 × 4 matrix
Divide input bits into
two rows of 8 bits,
and zero pad other
bits to complete the 8
× 8 matrix.
Divide input bits into
two rows of 8 bits.
Then, repeat each
sequence four times
to complete the 8 ×
8 matrix
Number
of images
65536 65536 65536
Number
of labels
254 254 254

82. TRAINING
HYPERPARAMETERS
TRAINING HYPERPARAMETERS
Parameter Value Parameter Value
Programming
language
MATLAB 2020 a Data splitting
(train/test)
80/20,
randomized
Optimizer ADAM, SGDM,
RMSPROP
Gradient
Threshold
Inf
Maximum
Iterations
12270 Max Epochs 30
Learn Rate
Schedule
Constant Mini Batch Size 128
Learn Rate Drop
Factor
0.1000 Execution
Environment
Single GPU
Learn Rate Drop
Period
10 Shuffle Every-epoch
L2Regularization 10-4 Momentum: 0.9000
Gradient
Threshold Method
'l2norm' Initial Learn Rate 0.0100

83. Block diagram of customized CNN models
Classification Output
crossentropyex
Softmax
Fully Connected
fully connected layer with 254 output
classes
Dropout
50% dropout
Fully Connected
fully connected layer with 500 output
classes
ReLU
Batch Normalization
Convolution
5 filters of size 5x5 convolutions with
stride 1 and padding 3
Image Input
8x8x1 or 4x4x1 images with
'zerocenter' normalization
Classification Output
crossentropyex
Softmax
Fully Connected
fully connected layer with 254 output
classes
ReLU
Batch Normalization
Convolution
5 filters of size 5x5 convolutions with
stride 1 and padding 3
Image Input
8x8x1 or 4x4x1 images with
'zerocenter' normalization

84. Result
summary
Result summary
MODEL Optimizer Accuracy
(DB1)
Accuracy
(DB2)
Accuracy
(DB3)
MODEL 1
7 layers
SGDM 96.69% 85.83% 84.93%
RMSPROP 85.45% 79.45% 78.09%
ADAM 90.40% 81.51% 81.90%
MODEL 2
9 layers
SGDM 98.08% 98.36% 98.37%
RMSPROP 84.58% 83.10% 84.02%
ADAM 91.72% 83.03% 87.63%

85. Best model
performanc
e evaluation
CNN best model performance evaluation
Platform Query
length
5000 20K 50K 100K 200K
GPU Time(s) 6.5 16 39.5 77.8 156
GCUPS 0.0038 0.0248 0.0633 0.1286 0.2563

86. Block diagram of testing phase
Figure 10. Best model learning progress curve
Figure 11. Best model loss curve
Sequence 2
Sequence 1
Image Encoding
i.e.: reshape input data as DB3
Best Model
i.e.: Model 2, DB3, SGDM
Labels: 1, 2 …,254
Label Decoding 1
i.e.: '****||||**** '
Result
i.e.: 'ACGT||||ACGT '
Decoding
2
i.e.:
Replace
*
by
corresponding
letter
Block diagram of testing phase

87. Classical machine learning for
global Sequence Alignment
15 ML classifiers for global Sequence Alignment

88. Proposed
parallel
workflow
(a) Proposed parallel workflow and (b) traditional NW algorithm sequential
workflow

89. DESCRIPTION OF DATASETS
DESCRIPTION OF DATASETS
Dataset1 Dataset2 Dataset3 Dataset4 Dataset5 Dataset 6
Number of
Instances (Size)
65536 65536 65536 65536 65483 65483
Input Attributes
(Features)
8 (Decimal) 8 (Decimal) 16 (Binary) 16 (Binary) 8 (Decimal) 16 (Binary)
Output Classes 254 239 254 239 221 221

90. IEEE Dataport

91. General
machine
learning
process

92. Block diagram
of the
proposed
workflow

93. Data
Cleaning
Data cleaning involves fixing systematic problems or
errors in “messy” data.
Using statistics to define normal data and identify
outliers.
Identifying columns that have the same value or no
variance and removing them
Identifying duplicate rows of data and removing
them.
Marking empty values as missing.
Imputing missing values using statistics or a learned
model.

94. Overview of
feature
selection
techniques

95. FEATURE SELECTION BY USING THE FILTER METHOD (CHI-SQUARED) FOR THE BEST DATASETS
FEATURE SELECTION BY USING THE FILTER METHOD (CHI-SQUARED) FOR THE BEST DATASETS
χ² (Dataset6)
χ² (Dataset3)
Feature Number
χ² (Dataset6)
χ² (Dataset3)
Feature Number
497.2075
514.3212
fr3
2335.2803
2928.6962
fr8
478.7286
495.2717
fr11
2318.7507
2912.0966
f16
455.0509
472.0676
fr5
2168.6677
2749.3369
fr4
451.2413
468.8299
f13
2161.9829
2742.2791
fr12
407.9344
430.3850
fr9
2140.9491
2735.2433
fr10
402.7023
425.5596
fr1
2130.2249
2727.2088
fr2
355.1349
374.5339
fr7
2120.4733
2700.1408
fr6
356.3789
375.7883
fr15
2112.1734
2692.3486
fr14

96. Feature importance using Xgboost
(a) Feature importance for Dataset 5 (b) Feature importance for Dataset 6

97. Accuracy for all the
datasets by using
selected features
(i.e., the wrapper
method) for the
XGBoost classifier
Dataset 1 Dataset 2 Dataset 3
Numb
er of
Featur
es
Thresho
ld
Accura
cy
Numb
er of
Featur
es
Thresho
ld
Accura
cy
Numb
er of
Featur
es
Thresho
ld
Accura
cy
16 -- -- 16 -- -- 16 0.052 0.9641
15 -- -- 15 -- -- 15 0.056 0.2990
8 0.117 0.9683 8 0.028 0.9564 14 0.057 0.3517
7 0.119 0.3323 7 0.029 0.3830 13 0.057 0.1807
6 0.123 0.2048 6 0.031 0.3097 12 0.059 0.0984
5 0.124 0.1532 5 0.120 0.2158 10 0.061 0.0973
4 0.125 0.0825 4 0.130 0.0258 9 0.063 0.1145
3 0.130 0.0606 3 0.141 0.1131 8 0.065 0.0737
2 0.130 0.0294 2 0.240 0.0322 2 0.069 0.0232
1 0.132 0.0220 1 0.282 0.1332 1 0.070 0.0216

98. Accuracy for all the
datasets by using
selected features
(i.e., the wrapper
method) for the
XGBoost classifier.
Dataset 4 Dataset 5 Dataset 6
Number
of
Features
Threshold Accuracy
Number
of
Features
Threshold Accuracy
Number
of
Features
Threshold Accuracy
16 0.057 0.9632 16 -- -- 16 0.050 0.9613
15 0.057 0.3380 15 -- -- 15 0.052 0.3295
14 0.057 0.3620 8 0.023 0.9186 14 0.056 0.1810
13 0.058 0.2024 7 0.027 0.3783 13 0.056 0.2377
12 0.058 0.1529 6 0.028 0.2571 12 0.063 0.2015
11 0.059 0.1558 5 0.035 0.2207 11 0.063 0.2407
10 0.061 0.1765 4 0.166 0.0146 10 0.064 0.2152
9 0.061 0.1612 3 0.225 0.1348 9 0.064 0.1660
8 0.061 0.1579 2 0.233 0.1389 2 0.067 0.1348
7 0.064 0.1544 1 0.263 0.1348 1 0.071 0.1348

99. MODEL HYPER-PARAMETERS
Model Model Hyper-parameters
1-Logistic Regression (LR)-LASSO Regularization type: lasso (L1), strength C=1
2-Logistic Regression (LR)-Ridge Regularization type: Ridge (L2), strength C=1
3-Neural Network (MLP) Number of hidden neurons =100, activation=ReLu, solver=SGD, regularization, α=0.0001, number of iterations=1000, replicable training
4-SVM-RBF SVM TYPE=SVM, Cost(C)=1, Regression loss epsilon (Ꜫ)=0.1, Kernel=RBF, g: auto, numerical tolerance=0.001, iteration limit=100
5-SVM –Linear SVM TYPE=SVM, Cost(C)=1, Regression loss epsilon (Ꜫ)=0.1, Kernel=linear, g: auto, numerical tolerance=0.001, iteration limit=100
6-KNeighbors (kNN) Number of neighbors =5, metric=Euclidean, weight=uniform
7-Random Forest Number of trees=10, do not split subsets smaller than= 5
8-Gaussian Naive Bayes (GNB) Default
9-Stochastic Gradient Descent (SGD) Classification loss function =Hinge, regularization method=Ridge(L2), learning rate=constant, initial learning rate=0.01, number of
iterations=1000, Tolerance (stopping criterion) =0.001
10-Decision Tree Induce binary tree, min number of instances in leaves =2, do not split subsets smaller than= 5, limit the maximal tree depth=100, stop when
majority reaches=95%
11-AdaBoost Base estimator=tree, number of estimators=50, learning rate=1, classification algorithm=SAMME, regression loss function=exponential
12-CN2 rule inducer Rule ordering=ordered, covering algorithm=exclusive, rule search: evaluation measure=Entropy, Beam width=5, Rule Filtering, Minimum rule
coverage=1, maximum rule length=5
13-Extra Gradient Boosting (XGBoost) Default
14-Extra Trees Max-depth=4, criterion='entropy', random-state=123
15-Bagging Classifier Base classifier=MLP Classifier, max-samples=0.5, max-features=0.5

100. Training accuracy summary results
on all the datasets
Model Training Time (s)
Dataset 1
Accuracy
Dataset 2
Accuracy
Dataset 3
Accuracy
Dataset 4
Accuracy
Dataset 5
Accuracy
Dataset 6
Accuracy
1-Neural Network
(MLP)
24560.971 0.9650 0.9570 0.9847 0.9818 0.9584 0.9838
2-XGBoost 70999 0.9758 0.9168 0.9641 0.9694 0.8929 0.9689
3-AdaBoost 12.294 0.5217 0.5214 0.2912 0.3788 0.5231 0.3802
4-Random Forest 24.251 0.4362 0.4681 0.3449 0.3927 0.4649 0.3877
5-Decision Tree 447.251 0.4025 0.4000 0.2188 0.2493 0.4014 0.2480
6-SVM–RBF 12967.743 0.3486 0.3667 0.8272 0.8415 0.3670 0.8414
7-k-NN 78.96 0.2247 0.2770 0.2565 0.3070 0.2745 0.3089
8-CN2 Rule Inducer 150303.332 0.1946 0.2405 0.1699 0.2110 0.2362 0.2108
9-Extra Trees 67.152 0.0678 0.1391 0.0713 0.1391 0.1392 0.1392
10-GNB 0.573 0.0218 0.0878 0.0090 0.1066 0.1460 0.1403
11-SGD 139.872 0.0147 0.0393 0.0225 0.0484 0.0279 0.0453
12-SVM-Linear 4718.23 0.0095 0.0131 0.0232 0.0241 0.0130 0.0247
13-LR-Ridge 36780.96 0.0069 0.1391 0.0224 0.1398 0.1392 0.1399
14-LR-Lasso 1300.71 0.0068 0.1391 0.0226 0.1395 0.1392 0.1396
15- Bagging Classifier 224.31 0.2481 0.1511 0.1947 0.1402 0.1518 0.1405

101. Model Optimizer Classification Accuracy F-measure Precision Recall
Neural Network (MLP) ADAM 0.9925 0.9924 0.9924 0.9925
Neural Network (MLP) SGD 0.9847 0.9842 0.9841 0.9847
Neural Network (MLP) L-BFGS-B 0.9842 0.9840 0.9840 0.9842
Model Optimizer
Classification
Accuracy
F-measure Precision Recall
Neural Network
(MLP)
ADAM 0.9927 0.9927 0.9927 0.9927
Neural Network
(MLP)
SGD 0.9838 0.9833 0.9833 0.9838
Neural Network
(MLP)
L-BFGS-B 0.9853 0.9853 0.9854 0.9853
Summary of the experiment results of a neural network (i.e., MLP classifier with different
optimizers) on Dataset 3 and Dataset 6

102. Evaluation result summary of a neural network (i.e.,
MLP classifier (for Datasets 3T and 6T (testing phase)
Model Optimizer
Classification
Accuracy
F-measure Precision Recall
Neural Network
(MLP)- Dataset 3T
ADAM 0.859 0.859 0.859 0.859
Neural Network
(MLP)- Dataset 6T
ADAM 0.860 0.859 0.859 0.860

103. Proposed techniques for preventing overfitting
TECHNIQUE USED TECHNIQUE USED
Using more data No Increasing L2 penalty regularization Yes
Shuffling data Yes Dropout No
Cross-validation Yes Augmentation No
Automatic batch size Yes
Simplifying the model by using fewer
hidden neurons
Yes
Adaptive learning rate Yes Feature selection No
Early stopping Yes Ensemble methods No

104. Evaluation of the best model’s prediction runtime
Platfor
m
Query
Length
(NT)
256 52.4 k 80 k 128 k 3.3 M 4.1 M
Single
Core
Time
(s)
0
0.1964
8
0.1436
9
0.2197
7
5.805
5
6.041
7
GCUPS --
13.974
8
44.540
3
74.550
7
1939 2912

105. Performance comparison with other state-of-the-art
implementations
REFERENCE YEAR SEQUENCE PAIRS TIME (S) GCUPS
[16] 2013 1024 NTs 7.065 1.4842×10−4
[38] 2014 D4.4 vs. D4.6 203 100.7
[18] 2015 14336 NTs 12 0.0171
[22] 2019 20 k NTs 0.5995 0.6672
[23] 2019 50 k NTs 1157.8305 (19 min) 0.0022
[20] 2020 3 M NTs 29499 (492 min) 0.3051
Proposed 52.4 k NTs (Dataset 3T) 0.19648 13.9748
Proposed 4.1 M NTs (Dataset 4.1 M) 6.0417 2912

106. Strategy for testing
new data

107. Training and
testing
learning
curves

108. Conclusion
• Most of the previous studies aimed to accelerate the alignment algorithms in
different ways without providing any effective solution for sequential process
problems. Our proposed algorithms depend on the parallelization of common
alignment algorithms for DNA sequences under certain limitations to overcome
the main problems of DP and hardware implementation. It can also be applied
to RNA. This technique can be applied to any other local or global alignment
method and for short as well as very long sequences. The proposed technique
using MATLAB achieves considerably better elapsed time and GCUPS than the
state-of-the-art technique for local and global alignment. FPGA is demonstrated
as a cost-effective, energy-efficient platform for the implementation of sequence
alignment algorithms. This study presents a 65,536 × 48 ROM (LUT)-based
hardware implementation for the local alignment algorithm using VHDL
language.
• Our proposed implementation requires only one clock cycle to obtain full
alignment working on a 400 MHZ frequency. The same technique (ROM-based)
design can also be used for the NW algorithm, but we opt to use another
approach and check its performance.
• The combinational circuit design for SW algorithm achieves maximum path
delays of 15 and 7.511 ns (as in the fourth design) for NW algorithm on
XilinxVirtex6 FPGA. Moreover, the estimated power is approximately the same
for the two alignment algorithms. The improvement that occurs in NW alignment
(maximum path delay in combinational circuit design) over SW alignment
algorithm is due to our minimization procedure (reduction techniques used in
NW alignment). The third design (NW algorithm) achieves the least number of
logic circuits used (21,592) based on two reduction techniques and logic
minimization techniques. A customized CNN model is used for the software
implementation of a NW algorithm, and 98.3% accuracy is achieved. This
accuracy can reach 99.21% in absence of a dropout layer.

109. Future work
• Using different opening gap values in NW design does not
substantially affect the HW performance as well as the number of
characters representing the alignment array (still 18 characters), but
reviewing and standardizing the alignment array can (i.e., use a
single pattern for all full-mismatch conditions [’****::::****’] or a single
symbol to represent mismatch condition [colons only] instead of
using two symbols [space and colon]. In addition, this
standardization will reduce the number of classes (the target for the
CNN model) and enhance the performance of the CNN model. ML
techniques used in this study do not a achieve reasonable accuracy
which can be improved later by using python auto-ML libraries like
Auto-Sklearn, TPOT, HyperOpt, and AutoKeras. Auto-ML libraries
can also improve CNN model accuracy by tuning the
hyperparameters with the use of recent methods such as Bayesian
optimization, or automating feature selection, preprocessing, and
construction or searching for the best architecture. Our CNN model
can be later implemented using FPGA, which may improve
hardware design speed, device usage utility, and performance.