Planted Motif Search Problem Research Articles

Trie-PMS8: A trie-tree based robust solution for planted motif search problem

Finding patterns in biological sequences is a crucial and intriguing task. This paper explores the (Ɩ, d) motif search problem, also known as Planted Motif Search (PMS), and discusses its challenging nature as an NP-hard problem. PMS and (Ɩ, d) motif search algorithms are believed to represent the next generation of tools for motif discovery. In this context, PMS deals with n biological sequences and two parameters, Ɩ and d, to identify sequences of Ɩ length that occur in all input strings with, at most, d mismatches. Many existing exact PMS algorithms exhibit exponential time complexity in worst-case scenarios. This paper introduces an innovative algorithm that focuses on improving the efficiency of the sample-driven portion of the process. Specifically, dynamic programming techniques are employed to avoid redundant calculations in frequently used subtrees. Furthermore, this paper presents novel approaches to enhance algorithm performance, such as utilizing a trie tree that significantly reduces the time for the “sort rows by size” step. It has also reduced the spaces that take linked lists on LL-PMS8 (Hasan et al., Jun., 2022) or reduced the number of l-mers. Using trie tree as the main way to speed things up gives a much better result than older versions of PMS methods like LL-PMS8 (Hasan et al., Jun., 2022). Overall time complexity reduced than the previous method is 26.17 % and 16.48 % for real-world and generated datasets (Hasan et al., 2020).

LL-PMS8: A time efficient approach to solve planted motif search problem

Among other motif finding algorithms Planted Motif Search (PMS) or (ℓ, d) motif search algorithms are considered as next-generation motif finding algorithms. Here, n strings and two integer ℓ and d are provided as input in PMS. It gives outputs of all sequences of length M in each input string, where each occurrence varies from Min at most d points. This paper proposes a new algorithm, for the (ℓ, d) motif discovery problem in which we find all strings of length ℓ that seems in every string of a provided set of strings with at most d mismatches has been. After proper investigation of the PMS problem, it has been shown that this is an NP-hard exact algorithm. Usually, in the worst-case scenarios, all the known exact algorithms for PMS need exponential time in some of the underlying parameters. In this paper, we proposed a faster approach that reduces the searching time in the sample-driven part of the algorithm. In particular, we used dynamic programming techniques to eliminate the recalculation of the values of some common subtree and introduced some new speedup techniques like using a linked list, reduced ℓ-mers for making the algorithm faster. Due to the use of Linked List as a main speedup technique, we named our proposed algorithm as Linked List Implementation technique of PMS8 (LL-PMS8).

Parallel implementation of quorum planted (ℓ, d) motif search on multi-core/many-core platforms

Multi-core and many-core architectures are widely adopted by researchers in applied sciences and engineering, owing to their reasonable cost, and ease of access. Moreover, their painless hardware set-up process and rather simple programming paradigm attract more researchers to acquire them and implement their time-expensive computations on these platforms. Planted Motif Search problem is one of the most challenging problems in bioinformatics whose goal is to enumerate all strings of length ℓ that are commonly planted in a given set of DNA sequences. In this paper, we propose an efficient method of thread parallelization to accelerate the latest Quorum Planted Motif Search algorithm (qPMS9) on multi-core and many-core systems. Our contribution towards dynamic scheduling of threads and parallelization of loops in the proposed method outperforms previous sequential and parallel algorithms.

A New Idea for Improving the Running Time of PMS Algorithm

Motif finding problem is a major challenge in biology with significant applications in the detection of transcription factor binding sites and transcriptional regulatory elements that are crucial in understanding gene expression and function, human disease, drug design, etc. Two type of motif finding problems have been investigated. Planted Motif Search Problem (PMSP) which is defined as finding motifs that appear in all sequences and a restricted version of it “Planted Motif Search Problem-Sample Driven” (PMSP-SD) where the motifs themselves are found in the input. The first version is NP-Complete and the second version can be trivially solved in polynomial time. In this paper, a new idea is used to speed up the PMS-SD algorithm. Although PMS-SD is a polynomial time algorithm and the new idea does not improve its asymptotic runtime, but since most of the motif search algorithms combine a sample driven approach with a pattern driven approach, the speed up of PMS-SD running time would result in speed up of PMS algorithm. To verify the performance of the modified algorithms which are called PMS-two step and PMS-SD-two step, these algorithms are tested on simulated data. The experimental results approve the improvements.

Efficient sequential and parallel algorithms for planted motif search.

BackgroundMotif searching is an important step in the detection of rare events occurring in a set of DNA or protein sequences. One formulation of the problem is known as (l,d)-motif search or Planted Motif Search (PMS). In PMS we are given two integers l and d and n biological sequences. We want to find all sequences of length l that appear in each of the input sequences with at most d mismatches. The PMS problem is NP-complete. PMS algorithms are typically evaluated on certain instances considered challenging. Despite ample research in the area, a considerable performance gap exists because many state of the art algorithms have large runtimes even for moderately challenging instances.ResultsThis paper presents a fast exact parallel PMS algorithm called PMS8. PMS8 is the first algorithm to solve the challenging (l,d) instances (25,10) and (26,11). PMS8 is also efficient on instances with larger l and d such as (50,21). We include a comparison of PMS8 with several state of the art algorithms on multiple problem instances. This paper also presents necessary and sufficient conditions for 3 l-mers to have a common d-neighbor. The program is freely available at http://engr.uconn.edu/~man09004/PMS8/.ConclusionsWe present PMS8, an efficient exact algorithm for Planted Motif Search. PMS8 introduces novel ideas for generating common neighborhoods. We have also implemented a parallel version for this algorithm. PMS8 can solve instances not solved by any previous algorithms.

A HEURISTIC CLUSTER-BASED EM ALGORITHM FOR THE PLANTED (l, d) PROBLEM

The planted motif search problem arises from locating the transcription factor binding sites (TFBSs) which are crucial for understanding the gene regulatory relationship. Many attempts in using expectation maximization for TFBSs discovery are successful in past. However, identifying highly degenerate motifs and reducing the effect of local optima are still an arduous task. To alleviate the vulnerability of EM to local optima trapping, we present a heuristic cluster-based EM algorithm, CEM, which refines the cluster subsets in EM method to explore the best local optimal solution. Based on experiments using both synthetic and real datasets, our algorithm demonstrates significant improvements in identifying the motif instances and performs better than current widely used algorithms. CEM is a novel planted motif finding algorithm, which is able to solve the challenging instances and easy to parallel since the process of solving each cluster subset is independent.

An improved voting algorithm for planted (l, d) motif search

The planted motif search problem is a classical problem in bioinformatics that seeks to identify meaningful patterns in biological sequences. As an NP-complete problem, current algorithms focus on improving the average time complexity and solving challenging instances within an acceptable time. In this paper, we propose a new exact algorithm CVoting that improves the state-of-the-art Voting algorithm. CVoting uses a new hash technique to reduce the space complexity to O(mn+N(l,d)) and a new pruning technique to reduce the average time complexity to Om2nN(l,d)14+3ll. Experimental results show that CVoting outperforms competing algorithms, including PMS1, RISOTTO, Voting and Pmsprune, in both space and time: up to an order of magnitude faster and using less memory in solving challenging instances. The software of the proposed algorithm is publicly available at http://staff.ustc.edu.cn/xuyun/motif.

QPMS7: a fast algorithm for finding (ℓ, d)-motifs in DNA and protein sequences.

Detection of rare events happening in a set of DNA/protein sequences could lead to new biological discoveries. One kind of such rare events is the presence of patterns called motifs in DNA/protein sequences. Finding motifs is a challenging problem since the general version of motif search has been proven to be intractable. Motifs discovery is an important problem in biology. For example, it is useful in the detection of transcription factor binding sites and transcriptional regulatory elements that are very crucial in understanding gene function, human disease, drug design, etc. Many versions of the motif search problem have been proposed in the literature. One such is the -motif search (or Planted Motif Search (PMS)). A generalized version of the PMS problem, namely, Quorum Planted Motif Search (qPMS), is shown to accurately model motifs in real data. However, solving the qPMS problem is an extremely difficult task because a special case of it, the PMS Problem, is already NP-hard, which means that any algorithm solving it can be expected to take exponential time in the worse case scenario. In this paper, we propose a novel algorithm named qPMS7 that tackles the qPMS problem on real data as well as challenging instances. Experimental results show that our Algorithm qPMS7 is on an average 5 times faster than the state-of-art algorithm. The executable program of Algorithm qPMS7 is freely available on the web at http://pms.engr.uconn.edu/downloads/qPMS7.zip. Our online motif discovery tools that use Algorithm qPMS7 are freely available at http://pms.engr.uconn.edu or http://motifsearch.com.

PMS5: an efficient exact algorithm for the (ℓ, d)-motif finding problem.

BackgroundMotifs are patterns found in biological sequences that are vital for understanding gene function, human disease, drug design, etc. They are helpful in finding transcriptional regulatory elements, transcription factor binding sites, and so on. As a result, the problem of identifying motifs is very crucial in biology.ResultsMany facets of the motif search problem have been identified in the literature. One of them is (ℓ, d)-motif search (or Planted Motif Search (PMS)). The PMS problem has been well investigated and shown to be NP-hard. Any algorithm for PMS that always finds all the (ℓ, d)-motifs on a given input set is called an exact algorithm. In this paper we focus on exact algorithms only. All the known exact algorithms for PMS take exponential time in some of the underlying parameters in the worst case scenario. But it does not mean that we cannot design exact algorithms for solving practical instances within a reasonable amount of time. In this paper, we propose a fast algorithm that can solve the well-known challenging instances of PMS: (21, 8) and (23, 9). No prior exact algorithm could solve these instances. In particular, our proposed algorithm takes about 10 hours on the challenging instance (21, 8) and about 54 hours on the challenging instance (23, 9). The algorithm has been run on a single 2.4GHz PC with 3GB RAM. The implementation of PMS5 is freely available on the web at http://www.pms.engr.uconn.edu/downloads/PMS5.zip.ConclusionsWe present an efficient algorithm PMS5 that uses some novel ideas and combines them with well-known algorithm PMS1 and PMSPrune. PMS5 can tackle the large challenging instances (21, 8) and (23, 9). Therefore, we hope that PMS5 will help biologists discover longer motifs in the futures.

A speedup technique for (l, d)-motif finding algorithms.

BackgroundThe discovery of patterns in DNA, RNA, and protein sequences has led to the solution of many vital biological problems. For instance, the identification of patterns in nucleic acid sequences has resulted in the determination of open reading frames, identification of promoter elements of genes, identification of intron/exon splicing sites, identification of SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have proven to be extremely helpful in domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, etc. Motifs are important patterns that are helpful in finding transcriptional regulatory elements, transcription factor binding sites, functional genomics, drug design, etc. As a result, numerous papers have been written to solve the motif search problem.ResultsThree versions of the motif search problem have been proposed in the literature: Simple Motif Search (SMS), (l, d)-motif search (or Planted Motif Search (PMS)), and Edit-distance-based Motif Search (EMS). In this paper we focus on PMS. Two kinds of algorithms can be found in the literature for solving the PMS problem: exact and approximate. An exact algorithm identifies the motifs always and an approximate algorithm may fail to identify some or all of the motifs. The exact version of PMS problem has been shown to be NP-hard. Exact algorithms proposed in the literature for PMS take time that is exponential in some of the underlying parameters. In this paper we propose a generic technique that can be used to speedup PMS algorithms.ConclusionsWe present a speedup technique that can be used on any PMS algorithm. We have tested our speedup technique on a number of algorithms. These experimental results show that our speedup technique is indeed very effective. The implementation of algorithms is freely available on the web at http://www.engr.uconn.edu/rajasek/PMS4.zip