Parallel random projection using R high performance computing for planted motif search

TELKOMNIKA, Vol.17, No.3, June 2019, pp.1352~1359
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v17i3.11750  1352
Received November 10, 2018; Revised January 29, 2019; Accepted February 28, 2019
Parallel random projection using R high performance
computing for planted motif search
Lala Septem Riza*1
, Tyas Farrah Dhiba2
, Wawan Setiawan3
,
Topik Hidayat4
, Mahmoud Fahsi5
1,2,3
Department of Computer Science Education, Universitas Pendidikan Indonesia, Indonesia
4
Department of Biology Education, Universitas Pendidikan Indonesia, Indonesia
5
EEDIS Laboratory, Djillali Liabes University, BP 89 22000 Sidi Bel Abbes, Algeria
*Corresponding author, e-mail: lala.s.riza@upi.edu
Abstract
Motif discovery in DNA sequences is one of the most important issues in bioinformatics.
Thus, algorithms for dealing with the problem accurately and quickly have always been the goal of
research in bioinformatics. Therefore, this study is intended to modify the random projection algorithm to
be implemented on R high performance computing (i.e., the R package pbdMPI). Some steps are needed
to achieve this objective, ie preprocessing data, splitting data according to number of batches, modifying
and implementing random projection in the pbdMPI package, and then aggregating the results. To validate
the proposed approach, some experiments have been conducted. Several benchmarking data were used
in this study by sensitivity analysis on number of cores and batches. Experimental results show that
computational cost can be reduced, which is that the computation cost of 6 cores is faster around 34 times
compared with the standalone mode. Thus, the proposed approach can be used for motif discovery
effectively and efficiently.
Keywords: bioinformatics, high performance computing, motif discovery, planted motif search,
R programming language
Copyright © 2019 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Motif discovery in DeoxyriboNucleic Acid (DNA) sequences is one of the most important
issues in the field of bioinformatics since it may help biologists to obtain better understanding on
the structure and function of the molecules in the sequence [1]. A motif is a short pattern that
repeats in the DNA sequence consisting of a combination of four basic nitrogen: Adenine (A);
Guanine (G); Cytosine (C); and Thymine (T) [2]. Issues in motif discovery can be categorized
into 3 types, namely Simple Motif Search (SMS), Edit distance based (EMS), and Planted Motif
Search (PMS) [3]. The purpose of SMS is to find all the motifs from lengths 1 to the specified
length in all sequences of [4] while the purpose of the EMS is to find all the motifs on the desired
number of sequences [5]. PMS aims to find the motive that appears in every sequence
that exists [6].
In PMS, there are two important input parameters: the desired length of motif
symbolized byland the number of mismatches denoted by d [7]. For example, there are three
DNA sequences, as follows: S1=ATTGCTGA, S2=GCATTGAA, and S3=CATGCTTG.
Withl=4 and d=1, we obtain the following repetitive motifs: ATTG and TTGC. It can be seen that
PMS is included in the NP-Hard problem, so that if this algorithm is run to look for all possible
motives that appear in all sequences, then the time spent will be exponential [1]. Random
Projection (RP) [8] is one of the algorithms used for motive search problems in DNA sequences
included in PMS. In this algorithm a piece of the input data in the form of sub-sequences
(l-mers) will be projected according to the random position determined by k (k-mers)
values [1, 9]. RP represents that mutations can occur anywhere so the projection is done
randomly. Even though many algorithms have been introduced, since PMS is NP-hard
problems, an implementation of the algorithms into parallel computing is necessary to be done.
Therefore, this research is aimed to design and implement RP for dealing with PMS in
parallel computing in R programming language. The R programming language [10] is chosen
since it has become the de-facto standard for statistics, data analysis, and visualization.

TELKOMNIKA ISSN: 1693-6930 
Parallel random projection using R high performance... (Lala Septem Riza)
1353
Nowadays, there are many algorithms, collected in software libraries/packages, that have been
implemented and saved in the Comprehensive R Archive Network (CRAN) at
https://cran.r-project.org/. In this repository, one of packages in R used for high performance
computing and big data analysis is pbdMPI [11] that is used in this research.
In the literature, we found some relevant articles discussing implementations of motif
discovery in parallel computing. For example, in Clemente & Adorna's study [12], random
Projection algorithm was developed in the concept of GPU (Graphic Processing Units). Each
processor will be directed into threads that work within the device or GPU. Meanwhile, the
sequential process will be executed on the host or CPU. TEIRESIAS has been introduced to
improve the speed on finding maximal pattern [13]. An enhancement of the PMSPRUNE
algorithm has been proposed with two additional features: neighbor generation on a demand
basis and omitting the duplicate neighbor checking [14]. Furthermore, there are some different
approaches for dealing with patterns matching in various fields. For instance, multiple patterns
matching methods was introduced for large multi-pattern matching [15]. Improving the scanning
mode of Square Non-symmetry and Antipacking Model (SNAM) for binary-image is obtained by
proposing the new neighbor-finding algorithm [16].
The rest of the paper is organized as follows: first, the global procedure of this research
is presented in section 2. In section 3, a main contribution, which is a modification and
implementation of parallel random projection by using the pbdMPI package, is discussed.
To validate and analyze the proposel computational model, we conduct some experiments in
section 4 and some analysis in section 5. Finally, we conclude the research in section 6.
2. Research Method
Figure 1 shows the research design done in this study. It can be seen that first, we
perform some preparation, such as identifying problems, research objectives, and literature
study. These activities have been presented in the previous section. Then, we present a main
contribution of this research, which is designing and implementing parallel random projection
with R high performance computing (i.e., the pbdMPI package). This part will be explained in the
next section. After that, we conduct some experiments and their analysis of the results. Drawing
some conclusión is presented in the end.
Figure 1. Research design to conduct parallel random projection
3. Parallel Random Projection with the pbdMPI package
Basically, the computational model proposed in this research can be seen in Figure 2.
First, after reading and converting the input data from the .falsa file, we perform a modification
of random projection by utilizing R high perfomance computing (i.e., the pbdMPI package),

 ISSN: 1693-6930
TELKOMNIKA Vol. 17, No. 3, June 2019: 1352-1359
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called parallel random projection with pbdMPI. Detailed explanation regarding the proposed
approach can be seen in Figure 3. The results of this model is all motifs, their starting indices,
and computational costs.
Figure 2. The computational model of parallel random projection with pbdMPI
According to Figure 3, it can be seen that besides supplying some parameters related to
the RP algorithm, we need to input the number of cores and batches. Since the R programming
language needs to load data into random access memory (RAM), we need to define the number
of batches so that each batch just takes less than 20% of total memory capacity. Furthermore,
actually Step 1 to 3 and Step 6 to 8 illustrated in Figure 3 are the same as the RP algorithm on
the standalone mode. However, from Step 4 to 5 the tasks are conducted in parallel computing
by using pbdMPI commands. An important part of these steps is a rule to divide the sequence
into numbers of batches. Moreover, the rule should prevent all possible motif including the
sequence even though it has been splitted into several batches. So, in this case we implement
the (1) and (2):
   * 1 2 ,i
s
L
index floor i l
b
  
     
  
(1)
* ,i
e
L
index floor i
b
 
  
 
(2)

1355
where
i
sindex and
i
eindex are starting and ending indices for cutting the batch of i . , ,L b and l
are the length of sequence, number of batches, and length of pattern, respectively. It should be
noted that the starting index starts from i =2. For example, it is give the sequence
S=CAGTGACGTAATCA, and the length of pattern is 3. So, according to (1) and (2), we obtain
the following batches: S1=CAGT; S2=GTGACG; and S3=CGTAATCA. By following how the
algorithm random projection generates k-mers, we obtain the following k-mers on all batches
that are the same as k-mers on the sequence (without splitting into batches): CAG; AGT;GTG;
TGA; GAC; ACG; CGT; GTA; TAA; AAT; ATC; and TCA. It means that even though the
sequence has been splited and processed by different cores, the results of RP and parallel
random projection are the same.
Figure 3. The pseudo code of parallel random projection with pbdMPI
4. Experimental Study
4.1. Data Gathering
The data used in this study obtained from research in [17]. To download the data can
be through the site of University of Washington Computer Science and Engineering on page
http://bio.cs.washington.edu/research/download. In total, there are 52 data sets of DNA
sequences derived from four species, 6 of which are derived from the Drosophila melanogaster
sequence, 26 data derived from human sequences, 12 data derived from rat sequences and 8
other data derived from the Saccharomyces cerevisiae sequence. In each data file there are
several sequences that number between 1 to 35 sequences. Then, every sequence that resides
on the file has a variable length ranging from 500 to 3000 base pairs.
In this case, we only consider to use four datasets as follows: the dm01r.fasta and
dm05r.fasta files that are DNA sequences of Drosophila melanogaster, then hm01r.fasta
derived from the human sequence, and muso4r.fasta which is the rat DNA sequence as the
input data. The dm01r.fasta file contains 4 DNA sequences with the total length of sequence is
6000, while the dm05r.fasta file consists of 3 DNA sequence with the length of 7500.
The hm01r.fasta and mus04r.fasta files have the DNA sequence length of 36000 and
7000, respectively.
4.2. Experimental Design
In this study we conduct two simulations: standalone and parallel computing
(i.e., multicore) modes. Each group will use all data as mentioned previously: dm01r.fasta,
dm05r.fasta, hm01r.fasta, and muso4r.fasta. Furthermore, in accordance with the algorithm,
some parameters should be assigned, as follows: the length of motif and mismatches (l, d),

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threshold values (θ), and number of repetitions in each simulation (m). Specific variables (i.e.,
number of batches (b) and cores (c)) on parallel computing with pbdMPI are also assigned.
Total experiments conducted for both scenarios are 1560 experiments (i.e., 120 times for
standalone and 1440 times for parallel computing) by assigning all possibilities of the
combinations of the following parameter values: (l, d)={(6,2), (7,2), (8,3)}, θ={3,4}, m={1,2,3,4,5},
b={10,50}, and c={1,2,…,6}.
5. Results and Analysis
Since the limited space, in this section we illustrate the results and their analysis for a
particular dataset only. For example, on the standalone mode, a comparison of the number of
motifs found according to m, θ, and (l, d) on the dm01r dataset is shown in Figure 4. It can be
seen that the higher numbers of mismatch makes the higher of numbers of motifs.
Figure 4. The comparison of the numbers of motifs found on the dm01r dataset
Furthermore, on the standalone mode, we can compare the computational cost with
length of DNA sequence on the different (l, d) and θ as shown in Figure 5. It is obvious that the
longer length of DNA sequence takes the higher computation cost. It should be noted that these
lengths also represent the datasets used in the experiments, such as the dm01r has the length
of 6000.
Figure 5. The comparison between the computational cost and datasets/length of datasets

1357
On the parallel computing mode, Figure 6 shows that the comparison between the
computational costs and numbers of cores when we used the dm01r dataset on (l, d)=(6.2),
θ=3, and b=10. It can be seen that the proposed model has been successful since in general
speaking the computation cost can be reduced by adding the number of cores.
Figure 6. The comparison between computational cost and number of core on the
dm01r dataset
To ensure the analysis, Figure 7 explains a comparison between computational cost
and number of core on different (l, d) and m and the same θ (i.e., 3), and b (i.e., 10). It can be
seen that the computational time with stand alone mode (i.e., c=1) at (l, d)=(8.3) with m=5 took
26.98 seconds while on the number of core of 2 the computation only took 6.3 seconds.
It means that the computational time on stand alone needs four times longer than using 2 cores.
Moreover, the standalone mode took more than ten times compared with parallel computing
using 3 cores (i.e., 2.52 seconds). Using 6 cores, the computation can be faster around
34 times compared with the standalone mode. So, now it is obvious that the proposed model is
much faster than the standalone mode.
Figure 7. The comparison on dm01r with θ=3 and b=10
We also compared computational time gained from experimental results on the previous
research [1] even though there are different data on the file dm01r and mus04r. The number of
DNA sequences contained in the file dm01r is 4 with the length of 1500 for each sequence while
in the research [1] the dataset contains 5 DNA sequences. In the file mus04r the number of

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DNA sequences used in this experiment is 7 sequences with the length of each sequence is
1000 while only 6 sequences were used by the previous research. The comparison can be seen
in Table 1. It can be seen that all experiments conducted in this research are faster than the
study in [1]. It should be noted that the research conducted by [1] was performed in
standalone mode.
Table 1. Comparison with the Other Research [1]
File
Number of
sequence
Length of each
sequence
(l,d)
Time in
[1] (s)
Time using 6 cores in
this research (s)
dm01r 4 1500
(6,2) 1.81 0.32
(7,2) 5.804 0.45
(8,3) 39.55 0.42
dm05r 3 2500
(6,2) 2.574 0.48
(7,2) 7.268 0.51
(8,3) 52.537 0.52
hm01r 18 2000
(6,2) 2.73 1.75
(7,2) 7.708 1.65
(8,3) 50.135 1.71
mus04r 7 1000
(6,2) 1.4 0.52
(7,2) 5.08 0.5
(8,3) 35.45 0.52
6. Conclusion
The main contributions of this research are as follows (i) to propose the computational
model for modifying the random projection algorithm, called parallel random projection, for
dealing with planted motif search by utilizing R high performance computing (i.e., the pbdMPI
package) and (ii) to implement the proposed model and then validate it for finding motifs on
DNA sequences. According to the experiments, we can state that the proposed model are able
to reduce the computational cost significantly. Moreover, a comparison with the previous study
has been done, and it shown that the proposal produced better results in the term of
computational cost.
In the future, we have a plan to improve the model by using Big Data platform, such as
by using the programming model of MapReduce on Apache Hadoop [18] and Resilient
Distributed Datasets on Apache Spark [19]. Moreover, the different tools for utilizing parallel
computing, e.g., the foreach package [20], can be used as the study in [21]. Different tasks in
the related research to bioinformatics can be applied to test the proposed model as well, such
as prediction on cáncer [22], kidney disease [23], and sleep disorder [24]. Additionally, another
method that can be implemented for dealing with this research is Knuth Morris Pratt [25, 26].
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