Comparative Study of Motif Finding Algorithms and Efficient Motif

Search Using KMP

V. S. S. Ashish Babu, Daggubati Praneesha, Injeti Hemanth, P. Ravi Charan,

Rishu Jaiswal and Gayathri Ramasamy

Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru,

Karnataka, India

Keywords: Motif Search Algorithms, Bioinformatics, Brute Force Method, Greedy Algorithm, Randomized Motif

Search, Knuth‑Morris‑Pratt (KMP) Algorithm, Complexity Analysis.

Abstract: Different algorithms of finding motifs like Brute Force, Greedy, Randomized, and KMP (Knuth-Morris-Pratt)

to determine the most efficient one. So, in the implementation phase, these algorithms are going to be

implemented in order to provide the comparison in terms of time, best score, and complexity. This project is

going to implement the result of the comparison using graphical user interface components in an organized

table format, thus highlighting the time in microseconds it takes to carry out the task for each algorithm and

detailed analysis of the complexity. This result is then applied to searching for the best motif in a larger

dataset, thus showing the applicability in real terms of the KMP algorithm for efficient detection of motifs.

All this analysis will help in deciding the best algorithm to find the motifs and will, in turn, help provide

insight that will be adopted in bioinformatics research.

1 INTRODUCTION

The identification of recurring patterns, or motifs,

within DNA sequences is a fundamental problem in

bioinformatics, playing a crucial role in

understanding gene regulation, transcription factor

binding sites, and other biological mechanisms. Due

to the complexity and vast volume of genomic data,

robust computational approaches are necessary for

efficient and accurate motif discovery. This study

explores various motif-finding algorithms, each

leveraging distinct computational strategies to

identify biologically significant patterns within DNA

sequences.

The project investigates multiple motif discovery

techniques, including Brute Force Motif Search,

Greedy Motif Search, and Randomized Motif Search,

among others. The Brute Force approach

systematically examines all possible k-mers to

identify the optimal motif, albeit at a significant

computational cost. In contrast, the Greedy Motif

Search method incrementally constructs motifs by

selecting the most probable k-mer at each step,

offering a balance between efficiency and accuracy.

Additionally, the Knuth-Morris-Pratt (KMP)

algorithm a widely used string-matching technique is

integrated into this study to validate the identified

motifs by efficiently locating their occurrences within

larger DNA sequences. This approach not only

enhances motif discovery but also strengthens

biological interpretations by quantifying motif

frequency within genomic datasets.

To comprehensively evaluate the performance of

these algorithms, key metrics such as accuracy,

execution time, and computational complexity are

analysed. The study assesses algorithm efficiency

based on parameters including sequence length (n),

motif length (k), and the number of sequences (m). A

comparative analysis highlights the trade-offs

between computational cost and motif detection

accuracy, identifying the most effective algorithm

under various conditions.

Furthermore, a graphical user interface (GUI) is

developed to facilitate user-friendly visualization and

interpretation of results. This interface presents

algorithm performance metrics, motif positions,

execution times, and complexity analysis in an

intuitive format, enabling researchers to make

informed decisions regarding motif discovery. The

proposed framework is validated using both sample

DNA sequences and larger genomic datasets to assess

Babu, V. S. S. A., Praneesha, D., Hemanth, I., Charan, P. R., Jaiswal, R. and Ramasamy, G.

Comparative Study of Motif Finding Algorithms and Efﬁcient Motif Search Using KMP.

DOI: 10.5220/0013907700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

33-39

ISBN: 978-989-758-777-1

the scalability and robustness of the algorithms in

real-world applications.

This study underscores the significance of

integrating multiple motif-finding approaches with

validation techniques for comprehensive genetic

analysis. The combination of efficient motif search

algorithms with KMP-based validation offers a robust

framework for pattern recognition in DNA sequences.

The findings of this research contribute to the broader

field of bioinformatics by providing insights into

algorithmic trade-offs and facilitating enhanced

genetic analysis for researchers and professionals.

There is a list of related works in Section 2. In

Section 3, the recommended methods are presented.

The findings are presented in Section 4. The

discussion is presented in section 5. The conclusion

is presented in section 6.

2 RELATED WORKS

The study by Duvvuri et al. 2022 stresses the

fundamental role of DNA sequences in biological

processes which includes protein synthesis and

metabolism functions. Research presented by

Duvvuri et al. 2022 evaluates several pattern-

matching algorithms intended to study the complexity

levels of genetic sequences. These algorithms

demonstrate capabilities and limitations when

detecting viral DNA in host genomes according to the

study which aids in early pathogen intervention.

Bioinformatics uses assist in understanding DNA

motifs and their function as regulators of gene

expression according to (Shanmukha Reddy et al.,

2024). The study evaluates greedy construction

algorithms as solutions to decrease the computational

complexity levels in DNA motif identification

processes. Research evidence validates how these

algorithms detect regulatory principles that govern

gene expression patterns and supports progress in

biomedical applications.

In their study Niraula et al., 2019 examine how

next-generation sequencing helps biomedical

research and explains why quick reliable read-

aligning algorithms are essential. The authors develop

a new randomized approach to improve DNA read

alignment accuracy which helps researchers better

understand genomes and supports patient-specific

medical care. The research proves that the algorithm

functions effectively under various sequencing

conditions which makes it useful for medical

diagnosis work. Fei Gu and team (Kisan Daule et al.,

2024) created a parallel DNA fragment assembly

method that applies the Knuth-Morris-Pratt (KMP)

algorithm to handle repeat masking problems. The

study performed many tests to show an improvement

in processing time without affecting sequence

reconstruction quality. This method boosts genome

assembly speed to give better knowledge about

genetic material and support biotechnology research.

Magallanes and associates tested DNA sequence

approximate motif detection in their research (Sri

Hanish Kumar et al., 2024). Their analysis tests

dedicated algorithms to find motifs and demonstrates

that MFA works best to find approximate patterns in

DNA. The algorithm proves effective in discovering

functional DNA motifs which helps scientists’ study

how genes control themselves and find new medicine

possibilities.

In their study Alvin et al., 2023 examines how

Brute Force and Knuth-Morris-Pratt (KMP)

algorithms handle keyword search tasks when

processing the Big Indonesian Dictionary (KBBI).

The research shows the Brute Force method achieves

better results for execution time accuracy and

precision compared to KMP. The research findings

guide effective practices for big data search

operations.

Fan et al., 2019 established AMDILM which

determines the right motif lengths for DNA sequence

analysis. The successful experimental testing of

AMDILM proves its ability to find motifs correctly

which helps scientists better understand genetic

functions. The findings enable better computational

and biomedical scientific progress.

Informatics experts S. M. and C. Nandini et al.,

2025 review standard motif discovery methods to

describe their approaches and show their strengths

and weaknesses. This research work provides helpful

information to bioinformatics and computational

biology scientists through a comparison of all known

motif detection methods.

These studies collectively underscore the

significance of motif discovery algorithms in

unraveling genetic complexities. The integration of

various computational approaches enhances the

accuracy and efficiency of motif identification,

facilitating breakthroughs in genomic research and

biomedical applications.

3 METHODOLOGY

This part explains our method for detecting DNA

motifs through different motif-finding programs. In

this study we used four different algorithms which are

Brute Force Motif Search, Greedy Motif Search,

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Randomized Motif Search, and Knuth-Morris-Pratt

(KMP) String Matching.

The system tests each algorithm using

performance measurements including how long it

runs; how accurate the results are and how much work

it requires. The evaluation between different

approaches for identifying motifs finds which method

runs best for DNA research.

3.1 Brute Force Motif Search

The Brute Force Motif Search algorithm

systematically evaluates all possible k-mers

(subsequences of length k) in a given DNA sequence.

Each k-mer is scored based on its similarity to motifs

within the dataset.

Since this approach checks every possible

combination, it guarantees the identification of the

best motif. However, this exhaustive search incurs a

high computational cost, making it impractical for

large datasets.

3.1.1 Steps of the Brute Force Algorithm

• Generate all possible k-length

subsequences from the input DNA

sequences.

• Compute a score for each k-mer by

evaluating its occurrences in all

sequences.

• Identify the k-mer with the highest

score as the best motif.

3.1.2 Computational Complexity

The time complexity of the Brute Force Motif Search

is O (nk * m), where:

• n is the length of the DNA sequence,

• k is the motif length,

• m is the number of DNA sequences.

This method ensures high accuracy but becomes

computationally expensive as sequence length

increases.

3.2 Greedy Motif Search

The Greedy Motif Search algorithm iteratively

constructs motifs by making locally optimal choices

at each step. Instead of evaluating all possible k-mers,

it refines motif selection based on profile matrices.

This method balances accuracy and efficiency by

selecting motifs that provide the best score in each

iteration.

3.2.1 Steps of the Greedy Algorithm

• Select a random k-mer from the first

sequence as an initial motif.

• Construct a profile matrix from the selected

motif.

• For each subsequent sequence, select the k-

mer that best matches the profile matrix.

• Update the profile matrix after adding each

new k-mer.

• Repeat the process until convergence is

reached.

3.2.2 Computational Complexity

The time complexity of the Greedy Motif Search is O

(n * m), making it significantly faster than the Brute

Force approach while still providing near-optimal

motif identification.

3.3 Randomized Motif Search

The Randomized Motif Search algorithm introduces

randomness to motif selection, allowing it to escape

local optima and find better motifs over multiple

iterations. This method is well-suited for large

datasets where exhaustive search is infeasible.

3.3.1 Steps of the Randomized Algorithm

• Initialize by selecting random k-mers

from each DNA sequence.

• Construct a profile matrix from the

selected k-mers.

• Identify the most probable k-mer from

each sequence based on the profile

matrix.

• If the new set of k-mers has a higher

score, update the motif set.

• Repeat the process for a predefined

number of iterations to refine the

motifs.

3.3.2 Computational Complexity

The time complexity of Randomized Motif Search is

also O (n * m), similar to the Greedy approach.

However, the stochastic nature of this method enables

it to find better motifs over multiple trials.

Comparative Study of Motif Finding Algorithms and Efﬁcient Motif Search Using KMP

3.4 Knuth-Morris-Pratt (KMP)

Algorithm for Motif Search

Once the best motif is found the KMP search

discovers all its occurrences rapidly in lengthy DNA

sequences. The KMP method extracts search

efficiency from Longest Prefix Suffix arrays by

creating these arrays during preprocessing of the

motif.

3.4.1 Steps of the KMP Algorithm

The Knuth-Morris-Pratt (KMP) algorithm is used for

efficient motif searching by preprocessing the motif

to optimize pattern matching. It constructs a longest

prefix suffix (LPS) array, which helps in skipping

unnecessary character comparisons during the search

process. By leveraging the LPS array, the algorithm

scans the DNA sequence efficiently and identifies

occurrences of the motif while reducing redundant

computations. The identified motif positions are then

recorded for further analysis.

3.4.2 Computational Complexity

The time complexity of KMP is O (n + m), where n

is the length of the DNA sequence and m is the length

of the motif.

This makes it significantly more efficient than

traditional search methods, ensuring rapid motif

detection in large-scale bioinformatics datasets.

3.5 Comparison of Algorithms

Table 1: Comparative analysis.

Algorithm

Best Case

Complexit

Average

Case

Complexit

Worst

Case

Comple

xity

Brute

Force

O(nk * m) O(nk * m)

O(nk *

Greedy O(n * m) O(n * m)

O(n *

Randomize

O(n * m) O(n * m)

O(n *

KMP

O(n + m) O(n + m)

O(n +

To evaluate the efficiency of each algorithm, we

compare them based on accuracy, execution time, and

computational complexity. Accuracy determines how

well an algorithm identifies the correct motifs within

a dataset. Execution time refers to the duration taken

by the algorithm to process and return results, which

is crucial for handling large datasets efficiently.

Computational complexity measures the algorithm’s

performance in terms of resource utilization, such as

time and memory requirements, under varying input

conditions. The comparison is summarized in Table

• Brute Force provides the most accurate results

but is computationally expensive due to its

exhaustive search approach.

• Greedy Motif Search is faster but may

converge to suboptimal motifs as it makes

locally optimal choices at each step.

• Randomized Motif Search balances efficiency

and accuracy, making it a preferred approach

for large datasets as it avoids exhaustive

searching.

• KMP significantly optimizes motif detection

in larger DNA sequences by reducing

redundant computations and leveraging

efficient string-matching techniques.

The research discusses motif discovery and search

approaches using Brute Force, Greedy and

Randomized Motif Search algorithms in addition to

practical testing on designated DNA sample

sequences to identify motifs. Large datasets received

motif location help from the application of the Knuth-

Morris-Pratt (KMP) algorithm. The combination of

Randomized Motif Search with KMP achieves ideal

efficiency and accuracy performance levels which

make them appropriate for massive bioinformatics

applications.

4 RESULTS AND EVALUATION

The experimental evaluation was conducted to

compare the efficiency of motif-finding algorithms,

including Brute Force, Greedy, and Randomized

Motif Search, in identifying DNA motifs.

Additionally, the Knuth-Morris-Pratt (KMP)

algorithm was applied to efficiently locate motifs

within larger DNA sequences. The analysis focused

on execution time, accuracy, and computational

complexity, providing insights into the practical

usability of these algorithms for large-scale

bioinformatics applications. Figure 1 shows the

KMP.

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Figure 1: KMP.

4.1 Performance Analysis of

Algorithms

The Brute Force algorithm systematically evaluates

all possible k-mers, ensuring the identification of the

most accurate motif. However, its computational cost

is significantly high, making it impractical for large

datasets. The execution time increases exponentially

with sequence length, limiting its scalability.

The Greedy algorithm provides a balance between

accuracy and efficiency by iteratively selecting k-

mers based on a profile matrix. While it is faster than

the Brute Force approach, it does not always

guarantee the globally optimal motif due to its locally

optimal selections.

The Randomized Motif Search algorithm

iteratively refines motif selection, incorporating

randomness to escape local optima. It achieves a good

trade-off between accuracy and speed, making it

suitable for large datasets. Multiple runs of the

algorithm result in more reliable motif detection, even

though some variability remains due to its stochastic

nature.

To further optimize motif detection, the KMP

algorithm was applied to locate motifs efficiently

within extended DNA sequences. By utilizing the

Longest Prefix Suffix (LPS) array, KMP reduces

redundant computations, leading to a significant

reduction in execution time compared to naive string-

matching techniques.

4.2 Comparative Analysis

The algorithms were evaluated based on two primary

criteria:

• Score: The Brute Force algorithm

consistently achieved the highest scores,

followed closely by the Greedy and

Randomized algorithms.

• Execution Time: The Randomized

algorithm was the fastest, followed by the

Greedy algorithm, while the Brute Force

method was significantly slower.

In scenarios where two algorithms achieved similar

scores, execution time served as the deciding factor.

Overall, the Randomized algorithm emerged as the

most practical choice, balancing accuracy and speed.

Its iterative nature allowed it to approach the

performance of Brute Force while maintaining

computational efficiency.

4.3 Application of the Knuth-Morris-

Pratt (KMP) Algorithm

Once the best motif was identified, the Knuth-Morris-

Pratt (KMP) algorithm was employed to efficiently

locate all occurrences of this motif within extended

DNA sequences. The efficiency of KMP stems from

its ability to preprocess the motif and optimize the

search process.

The KMP algorithm operates through the following

key steps:

• Preprocessing: Constructs a Longest Prefix

Suffix (LPS) array for the motif, allowing

for rapid skipping of mismatches during the

search process.

• Efficient Search: Utilizes the LPS array to

minimize redundant comparisons, enabling

rapid and accurate identification of all motif

occurrences.

The effectiveness of the KMP algorithm in analysing

large genomic datasets makes it a crucial tool for

bioinformatics applications. It enables:

• Identification of regulatory elements such as

transcription factor binding sites.

• Mapping of motif occurrences across

genomes, contributing to the study of gene

expression patterns and evolutionary

relationships.

• Facilitation of research into disease

mechanisms and potential drug targets by

efficiently detecting biologically significant

motifs.

5 DISCUSSIONS

The comparison of motif-finding algorithms reveals

performance trade-offs between results precision and

processing speed and memory usage. Brute Force

authentication proves to be the most accurate at motif

discovery by checking every possible match from the

database. The computational time of the approach

increases exponentially which makes it inappropriate

for processing large genomic datasets. The Greedy

and Randomized Motif Search algorithms function as

Comparative Study of Motif Finding Algorithms and Efﬁcient Motif Search Using KMP

optimized solutions because they reduce the

frequency of motif assessments. The Greedy

approach performs locally effective moves which

sometimes leads to unsatisfactory global solution

results. The Randomized approach generates

unpredictable results through its methods yet runs

multiple trials to establish performance consistency.

It provides efficient accuracy solutions.

KMP algorithm outperformed other methods by

showing better efficiency for motif search operations.

The KMP algorithm speeds up motif search by

treating the motif through preprocessing and

implementing the Longest Prefix Suffix (LPS) array

for optimized comparison operations. The algorithm

shows excellent potential for big DNA sequence

scanning because it maintains high efficiency

alongside its suitability. The Randomized Motif

Search system together with KMP brings together

powerful motif discovery with swift pattern matching

capabilities which makes it an ideal solution for large-

scale bioinformatics applications.

The results indicate that the choice of an

appropriate algorithm depends on the dataset size and

computational constraints. While the Brute Force

method remains valuable for small datasets requiring

exhaustive analysis, Greedy and Randomized

methods are better suited for large-scale applications

where speed is a priority. The KMP algorithm, when

combined with motif detection techniques, ensures

robust performance in practical genomic studies.

Future research could focus on hybrid approaches that

integrate multiple motif-finding strategies to further

enhance efficiency and accuracy in large-scale

genetic analysis.

6 CONCLUSIONS

This study performed a comparative analysis of

motif-finding algorithms, including Brute Force,

Greedy, and Randomized Motif Search, to evaluate

their effectiveness in identifying DNA motifs. The

results demonstrate that while Brute Force guarantees

the most accurate results, its computational expense

makes it infeasible for large datasets. The Greedy

approach provides a faster alternative but may

converge to suboptimal motifs. The Randomized

algorithm achieves a balance between accuracy and

efficiency, making it a more practical choice for

large-scale bioinformatics applications.

To further enhance motif search efficiency, the

Knuth-Morris-Pratt (KMP) algorithm was employed

for locating identified motifs within extended DNA

sequences. By leveraging its preprocessing step and

LPS array, KMP significantly optimizes execution

time compared to traditional search methods. The

combination of Randomized Motif Search with KMP

proves to be an effective approach, ensuring both high

accuracy and computational feasibility.

The findings underscore the importance of

selecting appropriate motif detection algorithms

based on dataset size and computational constraints.

Future research could explore hybrid approaches that

integrate multiple motif-search strategies to improve

performance further. Additionally, extending these

methods to protein sequence analysis and real-time

genomic applications could enhance their

applicability in bioinformatics and computational

biology.

REFERENCES

Ajala, O. (2021). Efficient String Algorithms for Data

Security and Privacy. PhD dissertation, King’s College

London.

Alvin, A., Ramadhany, D.G., Rabbani, R.I., Suryaningrum,

K.M., & Saputri, H.A. (2023). Efficiency Analysis of

Brute Force and Knuth Morris Pratt Algorithms for

Indonesian Keyword Search on KBBI. 5th International

Conference on Cybernetics and Intelligent System

(ICORIS), Pangkalpinang, Indonesia.

Bhagat, K., Kumar Das, A., Kumar Agrahari, S., Aanand

Shah, S., & Ramasamy, G. (2024). Cross-Language

Comparative Study and Performance Benchmarking of

Sorting Algorithms. SSRN Preprint 5088751.

Daule, V.K., Santh V, S., Padmakumar, K., Mohandas, G.,

& Ramasamy, G. (2024). Optimized System for Crowd

Management Using Encryption and Decryption

Techniques. SSRN Preprint 5089076.

Duvvuri, K., Reddy, P.N., Kanisettypalli, H., Reddy, R.D.,

& T. V., N.P. (2022). Comparative Analysis of Pattern

Matching Algorithms Using DNA Sequences. IEEE

Mysore Sub Section International Conference

(MysuruCon), Mysuru, India.

Fan, Y., Wu, W., Yang, J., Yang, W., & Liu, R. (2019). An

Algorithm for Motif Discovery with Iteration on

Lengths of Motifs. IEEE/ACM Transactions on

Computational Biology and Bioinformatics, 12(1),

136-141.

Fernau, H., Manea, F., Mercaş, R., & Schmid, M.L. (2020).

Pattern Matching with Variables: Efficient Algorithms

and Complexity Results. ACM Transactions on

Computation Theory (TOCT), 12(1).

Gargano, M.L., Quintas, L.V., & Vaughn, G.A. (2021).

Improving A Greedy DNA Motif Search Using A

Multiple Genomic Self-Adapting Genetic Algorithm.

Congressus Numerantium, 185, 23.

Huebener, Z., & Van Houten, K. (2012). Three Approaches

to Solving the Motif-Finding Problem. Midwest

Instruction and Computing Symposium.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

Iliopoulos, C.S., Kundu, R., & Pissis, S.P. (2021). Efficient

Pattern Matching in Elastic-Degenerate Strings.

Information and Computation, 279, 104616.

Khaled, H. (2019). Enhancing recursive brute force

algorithm with static memory allocation: Solving motif

finding problem as a case study. 14th International

Conference on Computer Engineering and Systems

(ICCES), IEEE, 66-70.

Kisan Daule, V., Santh V, S., Padmakumar, K., Mohandas,

G., & Ramasamy, G. (2024). Optimized System for

Crowd Management Using Encryption and Decryption

Techniques. Available at SSRN 5089076.

Klaib, A.F., & Osborne, H. (2019). RSMA Matching

Algorithm for Searching Biological Sequences.

International Conference on Innovations in Information

Technology (IIT), IEEE.

Klaib, A., & Osborne, H. (2019). OE Matching Algorithm

for Searching Biological Sequences. ISRST.

Kumar Agrahari, S., Arjun Kumar Das, A., Yadav, A., &

Ramasamy, G. (2024). Next-Gen Routing and

Scalability Enhancements in Mobile Ad Hoc Networks.

SSRN Preprint 5089037.

Lee, N.K., Li, X., & Wang, D. (2018). A Comprehensive

Survey on Genetic Algorithms for DNA Motif

Prediction. Information Sciences, 466, 25-43.

Niraula, N., Vo, N.S., Tran, Q., & Phan, V. (2019). A

randomized algorithm for aligning DNA sequences to

reference genomes. IEEE 3rd International Conference

on Computational Advances in Bio and Medical

Sciences (ICCABS), New Orleans, LA, USA.

Ramasamy, G., Shaik, B. A., Kancharla, Y., & Manikanta,

A. R. (2025). A Bash-based approach to simulating

multi-process file systems: Design and implementation.

In Challenges in Information, Communication and

Computing Technology (pp. 200-206). CRC Press.

Ramasamy, G., Shaik, B.A., Kancharla, Y., & Manikanta,

A.R. (2025). A Bash-based approach to simulating

multi-process file systems: Design and implementation.

Challenges in Information, Communication and

Computing Technology, CRC Press, 200-206.

Septem, R.L., Rachmat, A.B., Munir, T.H., & Nazir, S.

(2019). Genomic repeat detection using the Knuth-

Morris-Pratt algorithm on R high-performance

computing package. International Journal of Advance

Soft Computing Applications, 11(1), 94-111.

Shanmukha Reddy, A., Charan, K.S., Ramasamy, G., &

Naga Sai, N. (2024). Quantitative Analysis of

Scheduling Efficiency: A Case Study of FCFS and

Round Robin Algorithms. SSRN Preprint 5088751.

Shanmukha Reddy, A., Charan, K. S., Ramasamy, G., &

Naga Sai, N. (2024). Quantitative Analysis of

Scheduling Efficiency: A Case Study of Fcfs and

Round Robin Algorithms. Kolla Sriram and

Ramasamy, Gayathri and Naga Sai, Nisankarrao,

Quantitative Analysis of Scheduling Efficiency: A Case

Study of Fcfs and Round Robin Algorithms (November

15, 2024).

Sri Hanish Kumar, M. S., BG, S., Mahithi Reddy, T., Bindu

Sree, M., & Ramasamy, G. (2024). Optimizing Job

Shop Scheduling: A Comparative Study of

Metaheuristic Algorithms.

Yu, Q., Zhao, X., & Huo, H. (2019). A New Algorithm for

DNA Motif Discovery Using Multiple Sample

Sequence Sets. Journal of Bioinformatics and

Computational Biology, 17(4).

Comparative Study of Motif Finding Algorithms and Efﬁcient Motif Search Using KMP