A Novel Framework for Short Tandem Repeats (STRs) Using Parallel String Matching

D. Bala MuraliKrishnaInt. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 5, Issue 9, (Part - 2) September 2015, pp.49-53
www.ijera.com 49|P a g e
A Novel Framework for Short Tandem Repeats (STRs) Using
Parallel String Matching
D. Bala MuraliKrishna, Ch. Someswara Rao.
Department of Computer Science, S.R.K.R Engineering College, Bhimavaram, AP, India.
Associate Professor, Department of Computer Science, S.R.K.R Engineering College, Bhimavaram, AP, India.
Abstract
Short tandem repeats (STRs) have become important molecular markers for a broad range of applications, such
as genome mapping and characterization, phenotype mapping, marker assisted selection of crop plants and a
range of molecular ecology and diversity studies. These repeated DNA sequences are found in both Plants and
bacteria. Most of the computer programs that find STRs failed to report its number of occurrences of the
repeated pattern, exact position and it is difficult task to obtain accurate results from the larger datasets. So we
need high performance computing models to extract certain repeats. One of the solution is STRs using parallel
string matching, it gives number of occurrences with corresponding line number and exact location or position
of each STR in the genome of any length. In this, we implemented parallel string matching using JAVA Multi-
threading with multi core processing, for this we implemented a basic algorithm and made a comparison with
previous algorithms like Knuth Morris Pratt, Boyer Moore and Brute force string matching algorithms and from
the results our new basic algorithm gives better results than the previous algorithms. We apply this algorithm in
parallel string matching using multi-threading concept to reduce the time by running on multicore processors.
From the test results it is shown that the multicore processing is a remarkably efficient and powerful compared
to lower versions and finally this proposed STR using parallel string matching algorithm is better than the
sequential approaches.
Keywords: computing model, DNA, STR, parallel string matching, multicore processing.
I. Introduction
DNA molecules are subject to a variety of
mutational events. One of the less well understood is
tandem duplicationin which a stretch of DNA, which
we call the pattern, is converted into two or more
copies, each following the preceding one in a
contiguous fashion. For example we could have
consider the below String TCGGCGGCGGA, and the
pattern CGG.
In which the single occurrence of triplet CGG
has been transformed into three identical, adjacent
copies. The result of a tandem duplication event is
termed a tandem repeat. Over time, individual copies
within a tandem repeat may undergo additional,
uncoordinated mutations so that typically, only
approximate tandem copies are present. Tandem
repeats are presumed to occur frequently in genomic
sequences, comprising perhaps 10% or more of the
human genome [1]. But, accurate characterization of
the properties of tandem repeats has been limited by
the inability to easily detect them. In recent years, the
discovery of the trinucleotide repeat diseases has
piqued interest in tandem repeats.
STRs (Short Tandem Repeats) are the genetic
loci where one or few bases are repeated for varying
numbers of times. Such repetitions occur primarily
due to slipped-strand impairing and subsequent
error(s) during DNA replication, repair, or
recombination [1]. STRs comprising 2–6 base pairs
long, occur frequently and are ubiquitously
interspersed in many genomes [2, 3]. The biological
importance of STR tracts has been clearly delineated.
STR loci show extensive length polymorphism, and
hence they are widely used in DNA fingerprinting
and diversity studies [4, 5]. They are also considered
as ideal genetic markers for the construction of high-
density linkage maps.In spite of its high significance,
a bioinformatics tool for the analysis of these regions
is not available.
Available algorithms directly or indirectly detect
tandem repeats. However, there are many limitations
with these algorithms [6].The drawbacks are high
computational time required by the algorithm and
their inability to predict the positions of STRs in the
genome.Finally, researchers have to understand the
classical methods of pattern matching to develop new
efficient algorithms [7-10]. In this work, we describe
a program called STRs using parallel string matching
that uses Multi-threading to find STRs on multicore
processors. The program Framework for STRs using
parallel string matching locates repeats with
RESEARCH ARTICLE OPEN ACCESS

patternsexact positon, line number and number of
occurrences of large number of files and directories
taking as input.
A serious comparison between different tools
should, of course, be performed by a third party
against a sufficient number of carefully selected test
problems, carefully tuning many different search
parameters; meanwhile we can say that in the case of
naive runs with default parameters on easy problems,
all the above programs appear to output basically
equivalent results. With the developments of new
string matching techniques, efficiency and speed are
the main factors in deciding among different options
available for each application area [11-13].However,
a major drawback of all such tools is that, when
running against very long sequences, they produce a
large amount of cumbersome results that require a
painstaking interpretation. In order to provide the
user with some capability of grasping the STRs at
first glance and looking over the drawbacks for large
sequencing files, we decided that the only way to do
this is by introducing parallel programing concept
into STRs identification [14-17]. And since, as far as
we know, no tool with Parallel programing feature is
as yet available [18, 19], a novel framework for the
STRs using parallel string matching of the results
compared with sequential approach has been devised.
STRs parallel string approach developed for
scanning genomes to find repeats of any length, their
exact position on chromosome and number of
occurrence. It can accept large sequences and large
number of files and directories up to GBs as input
can be searched simultaneously on multicore
processors. It divides the files and directories for
different cores and run these files parallel then after it
gives the files getting the output. Thus, the running
time of the program is greatly reduced. The
advantages over many other programs developed for
STR identification includes its ability to do parallel
programing using Multi-threading with multi core
processingto search patterns repeated for a number of
times of large input files and to give the exact
position and number of occurrences of the pattern in
the genome.
II. Methodology
Our goal is efficiently search STRs in large
genomes of given pattern. The proposed
methodology has two phases, Sequential and Parallel
approaches. We model a basic algorithm which is
giving better results than the previous algorithms like
Boyer Moore, KMP and Brute force. In the first
phase we apply on Sequential approach and later in
the second phase the basic algorithm is to be applied
on parallel string matching using Multi-threading
with multi core processing concept. Finally we give a
comparison between Boyer Moore, KMP and Brute
force with our basic algorithm in sequential approach
and then comparison between sequential approach
and parallel approach of basic algorithm.
Methodology comprises of two phases
Phase 1:STR using Sequential String matching
approach
The Sequential string matching approach uses
the basic algorithm which is better than the previous
algorithms and it processes the input file line by line
from a document and run sequential manner to
finding the repeats. The algorithm works with
Improved Right Prefix concept, the below example
demonstrate the working of the algorithm.
Figure 1: Example for Sequential pattern searching
Phase 2:STR Using Parallel String matching
approach
In The second Phase “Short Tandem Repeats
(STRs) Using Parallel String Matching” we apply the
above basic algorithm to this concept and it processes
the input file at first we take the input as a string or
text. The required text that is to be searched is further
divided into further small patterns or processes and
all this patterns are passed on the basic algorithm and
produce the result as number of occurrences of
repeated pattern as shown in the Fig.2 below.
Figure 2: Parallel approach using Multi-Threading
concept
G G C C G A G A A G A T A
G A T C

Because threads can operate independently on
different areas of an array for this algorithm, you will
see a clear performance boost on multicore
architectures compared to a sequential algorithm that
would iterate over each process in the array. It uses
the parallel execution of multi cores at a time which
will results the speedup in the number of cores with
minimal effort, because the Multi-threading takes
care of maximizing parallelism.
The Flowchart of the Improved Right Prefix is
shown in Fig 3.It .scans the genome sequence line by
line for given input file and process the linefor given
pattern length e.g. (ATGCATGCATGC)is a
nucleotide with pattern count =3.If STR found then it
identifies the position of the pattern and then
increment the count value and search for next STR, if
not then again reads the line of sequence. The process
is continues until all the given patterns are found then
it gives the number of occurrences of the pattern with
line number and position.
Figure 3: Flowchart of Improved Right Prefix
algorithm
III. Results and Discussion
The sequential approach gives the better results
by using the basic algorithm compared to previous
algorithms Boyer Moore, KMP and Brute force with
additional features like exact position with line
number and number of occurrences. Then this basic
algorithm reduces the time when we are applying in
parallel string matching using Multi-threading with
multi core processing,the results are illustrated in the
histogram below. The sequential approach took
80ms, the parallel string matching using Multi-
threading approach took 40ms.
The Fig 4 shows Execution time vs File size on
sequential search withBrute Force, Improved Right
Prefix Algorithm. This graph shows the performance
difference between Brute Force,Improved Right
Prefix algorithms. From the graph we clearly observe
that IRP is better compared to Brute Force.
Figure 4: Histogram for Brute Forcevs IRP (Time vs
File size)
The Fig 5 shows Execution time vs File size on
sequential search with KMP, Improved Right Prefix
Algorithm. This graph shows the performance
difference between KMP,Improved Right Prefix
algorithms. From the graph we clearly observe that
IRP is better compared to KMP.The Fig 6 shows
Execution time vs File size using Boyer Moore, IRP
Algorithm on Sequential search. From the graph we
clearly observe that IRP is much better than Boyer
Moore.
0
200
400
600
800
1000
5 10 15 20 25 30
Time(ms)
File Size (MB)
Brute Force vs IRP
Bruteforce IRP

Figure 5: Histogram for KMPvs IRP (Time vs File
size)
The most crucial stage in achieving a new
successful system and in giving confidence on the
system for the users that will work efficiently and
effectively. The system will be implemented only
after thorough testing and if it is found to work
according to the specification. For testing our
proposed system we will take the gene sequence data
set, consists of the four nucleotides A, C, G and T
(standing for adenine, cytosine, guanine, and
thymine, respectively) used to encode DNA.
Figure 6: Histogram for Boyer Moore vs IRP (Time
vs File size)
Therefore, the alphabet is O= {A, C, G, T}.
The text is consisted of 7, 50,000 records. Our
algorithm tested on different cores like 2, 4, 8, 12
etc., in the Fig 8 (Table). Here we put some
achievements what we develop and observe, and
finally our system shows that parallel approach is
much better than sequential approach with multi core
processor. The Fig 7 shows (Histogram) Execution
time vs File size on sequential search and parallel
search with Improved Right Prefix. From the Fig (4,
5, 6) we clearly observe that IRP is better compared
to other approaches.
Figure 7: Histogram for Sequential vs Parallel
approach
Figure 8: Table of Parallel execution times with
different cores
IV. Conclusion
In this paper we performed a comparisonbetween
Knuth Morris Pratt, Boyer Moore and Brute force
string matching algorithms with our improved right
prefix algorithm in sequential approach based on the
running time and in our tests with multicore
processing, we used strings of varying lengths and
texts of varying lengths. From the test results it is
shown that the improved right prefix algorithm is
extremely efficient in all cases. We apply this
algorithm in parallel string matching using multi-
0
200
400
600
800
1000
5 10 15 20 25 30 35
Time(ms)
File Size (MB)
KMP vs IRP
KMP IRP
0
200
400
600
800
1000
5 10 15 20 25 30
Time(ms)
File Size (MB)
Boyer Moore vs IRP
Boyer Moore IRP
0
200
400
600
800
1000
5 10 15 20 25 30
Time(ms)
File Size (MB)
Sequential Approach vs Parallel
Approach
Sequential Parallel
Number of Cores Parallel Execution Time (MS)
2 11026
4 8329
8 4208
12 2876

threading concept to reduce the time by running on
multicore processors. We conclude that STR using
parallel string matching is the most efficient
compared to earlier versions. As a future
enhancement, we apply this algorithmto fork and join
concept in JAVA SE7 of parallel string matching
algorithm thereby finding the most efficient
algorithm which can be used in many fields such as
cryptography, molecular biology. Thus the problem
of matching becomes easier.
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