Motif Search Algorithm 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).

Graph-Theoretical Analysis of Biological Networks: A Survey

Biological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound effect on our understanding the origins of life, health, and the disease states of organisms, and it allows for the diagnosis of diseases to aid in the search for remedial processes. In this review, we describe the main analysis methods of biological networks using graph theory, by first defining the main parameters, such as clustering coefficient, modularity, and centrality. We then survey fundamental graph clustering methods and algorithms, followed by the network motif search algorithms, with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function that is carried out by that small network, and discovering such a function provides an insight into the overall function of the organism. Lastly, we review network alignment algorithms that find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor, and finding such a relationship may help researchers to derive ancestral relationships and to predict the future evolution of organisms to enable the design of new drugs. We provide a review of the research studies in all of these methods, and conclude using the current challenging areas of biological network analysis, and by using graph theory and parallel processing for high performance analysis.

Abstract 1616: Dissecting the biology and therapeutic vulnerabilities of RB1-mutant osteosarcoma using hereditary retinoblastoma iPSCs

Abstract Patients with hereditary retinoblastoma (RB), an inherited autosomal dominant cancer disordercaused by germline mutations/deletions in the RB1 gene, have a >400 fold increased incidenceof osteosarcoma (OS), suggesting a strong mechanistic link between RB1 loss andosteosarcomagenesis. Although mice offer many advantages when conducting cancer research,unlike humans, Rb1 knockout mice do not develop OS, suggesting the urgent requirement foralternative disease models to understand how RB1 mutation leads to osteosarcomagenesis.Here, we generated patient-derived induced pluripotent stem cells (iPSCs) by reprogramming RBand healthy control fibroblasts and then created isogenic controls by correcting RB1 mutationusing CRISPR/Cas9. Performing in vitro soft-agar assay and in vivo xenografts, we foundpotential tumorigenic ability in RB osteoblasts (OBs) but not control OBs. In order to gain insightsinto RB1 loss associated osteosarcomagenesis, we compared global transcripts among RB andcontrol OBs. GSEA analysis indicated that OS associated genes are specifically enriched in RBOBs in comparison with control OBs, demonstrating that RB OBs acquire OS characteristics inthe absence of additional gene alterations. GO analysis revealed that genes involved in the mitoticcell cycle are enriched in RB OBs. Although RB1 is known to controlling G1/S related geneexpression by inhibiting E2F transcription factors, it is still nebulous if RB1 plays a role inregulating mitotic genes.To comprehensively identify preferential binding of RB1 to specific promoters in OBs, weconducted RB1 ChIP-seq to map genome-wide RB1-binding sites in iPSC-derived OBs. SinceE2F3a, a RB1 repressed transcription factor, is enriched expressed in OBs, we also performedE2F3a ChIP-seq to define whether these RB1-repressed targets are E2F3a-activated targets.Using the nonbiased de novo motif search algorithm DME, we further identified the most highlyenriched RB1-binding motif in all conditions and cross-compare with the E2F3a motifs. Weconfirmed that RB1 and E2F3a co-occupy the mitotic regulator promoters by ChIP-PCR. IHCstudies of clinical OS specimens supported the clinical correlation between RB1/E2F3a andmitotic regulators. High mitotic regulator genes expression is correlated with poor prognosis. Ourresults demonstrated that the mitotic regulators are co-regulated by both RB1 and E2F3a andthey play roles in osteosarcomagenesis.Taken together, our findings demonstrated multiple OS-related phenotypes in human RB iPSC-derived OBs and RB1/E2F3a-regulating mitotic regulators can be a therapeutic vulnerability inRB1-mutant OS. Citation Format: Dandan Zhu, Jian Tu, Zijun Huo, An Xu, Mo-Fan Huang, Ying Liu, Yi-Hung Chen, Ruiying Zhao, Dung-Fang Lee. Dissecting the biology and therapeutic vulnerabilities of RB1-mutant osteosarcoma using hereditary retinoblastoma iPSCs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1616.

SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery

A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals for multi-variate motif discovery. Unfortunately, motif search among multiple variates is an expensive process, as the potential number of sub-spaces in which a pattern can occur increases exponentially with the number of variates. Consequently, many multi-variate motif search algorithms make simplifying assumptions, such as searching for motifs across all variates individually, assuming that the motifs are of the same length, or that they occur on a fixed subset of variates. In this paper, we are interested in addressing a relatively broad form of multi-variate motif detection, which seeks frequently occurring patterns (of possibly differing lengths) in sub-spaces of a multi-variate time series. In particular, we aim to leverage contextual information to help select contextually salient patterns and identify the most frequent patterns among all. Based on these goals, we first introduce the contextually salient multi-variate motif (CS-motif) discovery problem and then propose a salient multi-variate motif (SMM) algorithm that, unlike existing methods, is able to seek a broad range of patterns in multi-variate time series.

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 Algorithms for Discovering Planted (l, d) Motif

In computational biology, motifs are short, recurring patterns of biological sequences that possess the principal character for the analysis and interpretation of various biological issues like human disease, gene function, drug design, etc. The major objectives of the motif search problem are the management, analysis, and interpretation of huge biological sequences using computational techniques from computer science and mathematics. However, detection of the motif leads to computational problems whose solutions require a substantial amount of time in one uniprocessor machine and thus, remains as one challenging problem. In this chapter, two parallel algorithms are proposed, along with its implementation detail which crucially enhances the performance of the PMSP motif search algorithm. The first approach enhances the existing algorithm by eliminating the redundant process of the computation and also, minimizes the execution time by the use of both process-level and thread-level parallelism in the implementation. The second approach is the improvement over the first one, where not only the time of computation is reduced further but also the best space utilization is achieved..

Optimized cloud-based scheduling for protein secondary structure analysis

In the domain of proteomics, an in-depth analysis of the 3D structure of a protein is of paramount importance for many biological studies and applications. At the secondary level, protein structure can be described in terms of motifs, recurrent patterns of smaller biological structures called secondary structure elements. In this paper, the focus is on the identification of geometrical motifs in different proteins using the Cross Motif Search (CMS) algorithm. Such task, due to the high computational cost of CMS with respect to traditional alignment algorithms, is very demanding, and thus, parallel processing is mandatory. In previous papers, CMS parallelization has been already studied from the HPC standpoint. Since cloud computing is emerging as an alternative to on-premise HPC systems, it is worthwhile examining the feasibility and possible advantages in terms of both performance and costs, of migrating to a cloud implementation. This paper is an extension of a preliminary work carried out on the cloud parallelization of CMS. The paper has two main contributions. First of all, an analytic model of the communication pattern of CMS is described, in order to get insights on the performance of the application when executed on a cloud infrastructure. Secondly, an optimized “location-aware” scheduling policy to assign workload to the application workers is introduced, in order to minimize internode communication in a cloud setting. Experiments are presented in order to validate the newly introduced scheduling policy and assess the performance of the cloud implementation of CMS. The results presented in this paper are general, in the sense that they can be applied to any other algorithm with a communication pattern similar to the one of the target applications.

Bioinformatics Approaches to Gain Insights into cis-Regulatory Motifs Involved in mRNA Localization.

Messenger RNA (mRNA) is a fundamental intermediate in the expression of proteins. As an integral part of this important process, protein production can be localized by the targeting of mRNA to a specific subcellular compartment. The subcellular destination of mRNA is suggested to be governed by a region of its primary sequence or secondary structure, which consequently dictates the recruitment of trans-acting factors, such as RNA-binding proteins or regulatory RNAs, to form a messenger ribonucleoprotein particle. This molecular ensemble is requisite for precise and spatiotemporal control of gene expression. In the context of RNA localization, the description of the binding preferences of an RNA-binding protein defines a motif, and one, or more, instance of a given motif is defined as a localization element (zip code). In this chapter, we first discuss the cis-regulatory motifs previously identified as mRNA localization elements. We then describe motif representation in terms of entropy and information content and offer an overview of motif databases and search algorithms. Finally, we provide an outline of the motif topology of asymmetrically localized mRNA molecules.

Parallel network motif search using message passing approach for biological complex networks

Study of complex biological networks is essential for understanding their functional characteristics. Network motifs have functional significance in biological networks as they represent building blocks of these networks. This study evaluates master-worker parallelization approach on sequential PATRICIA trie based fast network motif search algorithm for distributed memory model based High Performance Clusters (HPCs). Proposed algorithm uses PATRICIA trie for data compression during census of subgraphs based upon ESU algorithm. Parallel implementation was done using MPI and C language. We applied proposed parallel algorithm to three real networks viz. networks of metabolic pathway of E.coli, electronic and social networks. PATRICIA based parallel approach was able to achieve speedup of 50.75, 49.37, 38.07 as analysed on 101 cores on networks of metabolic pathway of E.coli, electronic and social networks respectively for large motifs of size 9 for E.coli, social and 10 for electronic networks over the PATRICIA trie based sequential algorithm.

Randomised sequential and parallel algorithms for efficient quorum planted motif search

Motifs are crucial patterns in biological sequences that have numerous applications. Motif search is an important step in obtaining meaningful patterns from biological data. However, most of the existing algorithms are deterministic and the role of randomisation in this area is still unexploited. This paper focuses on (l,d)-motif model, which is also known as Planted Motif Search (PMS) and proposes an efficient randomised algorithm, named qPMS10, to solve PMS. We utilise the most efficient PMS solver until now, named qPMS9, as a subroutine. We analyse the time complexity of both algorithms and provide a performance comparison of qPMS10 with qPMS9 on standard benchmark datasets. In addition, we offer a parallel implementation of qPMS10 and run tests using up to four processors. Both theoretical and empirical analyses demonstrate that our randomised algorithm outperforms the existing algorithms for solving PMS.

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.

MPI‐CMS: a hybrid parallel approach to geometrical motif search in proteins

SummaryThis paper describes the message passing parallel implementation of the Cross Motif Search algorithm (MPI‐CMS). It is an extension and specifically improves on the results obtained in a conference paper presented at PBIO 2014. CMS is a bioinformatics algorithm whose goal is to search for geometrical motifs in proteins. For the purpose of a complete characterization of protein similarities, it would be important to run CMS on the largest possible dataset. Unfortunately, due to its precision, CMS is inherently slow; thus, it was originally implemented using a shared memory parallel paradigm. In the original conference paper, we proved that the OpenMP implementation of Cross Motif Search (MP‐CMS) is extremely inefficient and cannot scale adequately. To solve the problem, we designed a new parallel implementation of CMS (MPI‐CMS) based on a hybrid shared memory and message passing paradigm. This paper reconsiders MPI‐CMS with the target to port it on a supercomputing machine. The focus is on the dependence of performance in the hybrid approach on the workload unbalance. Using a simple statistical analysis of the workload we discuss several strategies through which we can improve the design of MPI‐CMS. We conclude the paper describing a revised implementation of MPI‐CMS, which takes into account the size of the protein pairs to fine‐tune the parallelization strategy. Copyright © 2015 John Wiley & Sons, Ltd.

An adiabatic quantum algorithm and its application to DNA motif model discovery

Site-specific DNA–protein interactions can be studied by using experimental and computational methods. Understanding the relationship between these two perspectives and finding ways to improve both is a major challenge of modern molecular biology. Computational approaches for finding DNA motifs are well recognized as useful tools to biologists, which greatly help in saving experimental time and cost in wet laboratories. Over these years, many motif search algorithms and Web-based tools have been developed based on computational intelligence systems and data mining techniques in light of classical electronic computers. In order to use quantum computers in DNA motif model discovering, a related quantum algorithm is necessary. In this paper, based on the quantum adiabatic theorem, an adiabatic quantum algorithm and its application to DNA motif model discovery are obtained in terms of the MISCORE-based motif score. The proposed method can be used to deal with discovering of DNA motif models with big data by a quantum computer, e.g. D-Wave.

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.

New strategies for evaluation and analysis of SELEX experiments.

Aptamers are an interesting alternative to antibodies in pharmaceutics and biosensorics, because they are able to bind to a multitude of possible target molecules with high affinity. Therefore the process of finding such aptamers, which is commonly a SELEX screening process, becomes crucial. The standard SELEX procedure schedules the validation of certain found aptamers via binding experiments, which is not leading to any detailed specification of the aptamer enrichment during the screening. For the purpose of advanced analysis of the accrued enrichment within the SELEX library we used sequence information gathered by next generation sequencing techniques in addition to the standard SELEX procedure. As sequence motifs are one possibility of enrichment description, the need of finding those recurring sequence motifs corresponding to substructures within the aptamers, which are characteristically fitted to specific binding sites of the target, arises. In this paper a motif search algorithm is presented, which helps to describe the aptamers enrichment in more detail. The extensive characterization of target and binding aptamers may later reveal a functional connection between these molecules, which can be modeled and used to optimize future SELEX runs in case of the generation of target-specific starting libraries.

PMS6MC: A Multicore Algorithm for Motif Discovery.

We develop an efficient multicore algorithm, PMS6MC, for the (l, d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. PMS6MC is based on PMS6, which is currently the fastest single-core algorithm for motif discovery in large instances. The speedup, relative to PMS6, attained by our multicore algorithm ranges from a high of 6.62 for the (17,6) challenging instances to a low of 2.75 for the (13,4) challenging instances on an Intel 6-core system. We estimate that PMS6MC is 2 to 4 times faster than other parallel algorithms for motif search on large instances.

Network motif detection: Algorithms, parallel and cloud computing, and related tools

Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amounts of repeated processes for statistical evaluation. Although many efficient algorithms have been introduced, exhaustive search methods are still infeasible and feasible approximation methods are yet implausible. Additionally, the fast and continual growth of biological networks makes the problem more challenging. As a consequence, parallel algorithms have been developed and distributed computing has been tested in the cloud computing environment as well. In this paper, we survey current algorithms for network motif detection and existing software tools. Then, we show that some methods have been utilized for parallel network motif search algorithms with static or dynamic load balancing techniques. With the advent of cloud computing services, network motif search has been implemented with MapReduce in Hadoop Distributed File System (HDFS), and with Storm, but without statistical testing. In this paper, we survey network motif search algorithms in general, including existing parallel methods as well as cloud computing based search, and show the promising potentials for the cloud computing based motif search methods.

Efficient algorithms for biological stems search.

BackgroundMotifs are significant patterns in DNA, RNA, and protein sequences, which play an important role in biological processes and functions, like identification of open reading frames, RNA transcription, protein binding, etc. Several versions of the motif search problem have been studied in the literature. One such version is called the Planted Motif Search (PMS)or (l, d)-motif Search. PMS is known to be NP complete. The time complexities of most of the planted motif search algorithms depend exponentially on the alphabet size. Recently a new version of the motif search problem has been introduced by Kuksa and Pavlovic. We call this version as the Motif Stems Search (MSS) problem. A motif stem is an l-mer (for some relevant value of l)with some wildcard characters and hence corresponds to a set of l-mers (without wildcards), some of which are (l, d)-motifs. Kuksa and Pavlovic have presented an efficient algorithm to find motif stems for inputs from large alphabets. Ideally, the number of stems output should be as small as possible since the stems form a superset of the motifs.ResultsIn this paper we propose an efficient algorithm for MSS and evaluate it on both synthetic and real data. This evaluation reveals that our algorithm is much faster than Kuksa and Pavlovic’s algorithm.ConclusionsOur MSS algorithm outperforms the algorithm of Kuksa and Pavlovic in terms of the run time as well as the number of stems output. Specifically, the stems output by our algorithm form a proper (and much smaller)subset of the stems output by Kuksa and Pavlovic’s algorithm.

STEME: efficient EM to find motifs in large data sets

MEME and many other popular motif finders use the expectation–maximization (EM) algorithm to optimize their parameters. Unfortunately, the running time of EM is linear in the length of the input sequences. This can prohibit its application to data sets of the size commonly generated by high-throughput biological techniques. A suffix tree is a data structure that can efficiently index a set of sequences. We describe an algorithm, Suffix Tree EM for Motif Elicitation (STEME), that approximates EM using suffix trees. To the best of our knowledge, this is the first application of suffix trees to EM. We provide an analysis of the expected running time of the algorithm and demonstrate that STEME runs an order of magnitude more quickly than the implementation of EM used by MEME. We give theoretical bounds for the quality of the approximation and show that, in practice, the approximation has a negligible effect on the outcome. We provide an open source implementation of the algorithm that we hope will be used to speed up existing and future motif search algorithms.