Motif Discovery Tools Research Articles

Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation.

DNA motifs are crucial patterns in gene regulation. DNA-binding proteins (DBPs), including transcription factors, can bind to specific DNA motifs to regulate gene expression and other cellular activities. Past studies suggest that DNA shape features could be subtly involved in DNA-DBP interactions. Therefore, the shape motif annotations based on intrinsic DNA topology can deepen theunderstanding of DNA-DBP binding. Nevertheless, high-throughput tools for DNA shape motif discovery that incorporate multiple features altogether remain insufficient. To address it, we propose a series of methods to discover non-redundant DNA shape motifs with the generalization to multiple motifs inmultiple shape features. Specifically, an existingGibbs sampling methodis generalized to multiple DNA motif discovery with multiple shape features. Meanwhile, an expectation-maximization (EM) method and a hybrid method coupling EM with Gibbs sampling are proposed anddeveloped with promisingperformance, convergence capability, and efficiency. The discovered DNA shape motif instances reveal insights intolow-signal ChIP-seq peak summits, complementing the existing sequence motif discovery works. Additionally, our modelling captures the potential interplays across multiple DNA shape features. We provide a valuable platform of tools for DNA shape motif discovery. An R package is built for open accessibility and long-lasting impact: https://zenodo.org/doi/10.5281/zenodo.10558980.

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).

Comparative analysis of computational based motif refinement methods

Finding patterns in DNA sequences, a challenging task in molecular biology and computer science, plays a crucial role in understanding gene expression and transcription regulation. This article explores various computational methods for identifying transcription factor binding sites TFBS and regulatory motifs in DNA sequences. We discuss the significance of these motifs in gene expression and disease susceptibility research. The article categorizes motif discovery algorithms into enumerative, probabilistic, combinatorial, and nature-inspired approaches, highlighting their strengths and limitations. It emphasizes the need for a comprehensive motif discovery tool that can handle diverse motif types, perform global searches efficiently, and reduce user-defined parameters.

Thermodynamic and structural characterization of an EBV infected B-cell lymphoma transcriptome.

Epstein–Barr virus (EBV) is a widely prevalent human herpes virus infecting over 95% of all adults and is associated with a variety of B-cell cancers and induction of multiple sclerosis. EBV accomplishes this in part by expression of coding and noncoding RNAs and alteration of the host cell transcriptome. To better understand the structures which are forming in the viral and host transcriptomes of infected cells, the RNA structure probing technique Structure-seq2 was applied to the BJAB-B1 cell line (an EBV infected B-cell lymphoma). This resulted in reactivity profiles and secondary structural analyses for over 10000 human mRNAs and lncRNAs, along with 19 lytic and latent EBV transcripts. We report in-depth structural analyses for the human MYC mRNA and the human lncRNA CYTOR. Additionally, we provide a new model for the EBV noncoding RNA EBER2 and provide the first reported model for the EBV tandem terminal repeat RNA. In-depth thermodynamic and structural analyses were carried out with the motif discovery tool ScanFold and RNAfold prediction tool; subsequent covariation analyses were performed on resulting models finding various levels of support. ScanFold results for all analyzed transcripts are made available for viewing and download on the user-friendly RNAStructuromeDB.

Transcriptomic Response of the Diazotrophic Bacteria Gluconacetobacter diazotrophicus Strain PAL5 to Iron Limitation and Characterization of the fur Regulatory Network.

Gluconacetobacter diazotrophicus has been the focus of several studies aiming to understand the mechanisms behind this endophytic diazotrophic bacterium. The present study is the first global analysis of the early transcriptional response of exponentially growing G. diazotrophicus to iron, an essential cofactor for many enzymes involved in various metabolic pathways. RNA-seq, targeted gene mutagenesis and computational motif discovery tools were used to define the G. diazotrophicus fur regulon. The data analysis showed that genes encoding functions related to iron homeostasis were significantly upregulated in response to iron limitations. Certain genes involved in secondary metabolism were overexpressed under iron-limited conditions. In contrast, it was observed that the expression of genes involved in Fe-S cluster biosynthesis, flagellar biosynthesis and type IV secretion systems were downregulated in an iron-depleted culture medium. Our results support a model that controls transcription in G. diazotrophicus by fur function. The G. diazotrophicus fur protein was able to complement an E. coli fur mutant. These results provide new insights into the effects of iron on the metabolism of G. diazotrophicus, as well as demonstrate the essentiality of this micronutrient for the main characteristics of plant growth promotion by G. diazotrophicus.

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.

BackgroundPhenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario.ObjectiveIn this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data.MethodsThe core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction.ResultsIn total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method.ConclusionsWe developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.

Prediction and Experimental Validation of a New Salinity-Responsive Cis-Regulatory Element (CRE) in a Tilapia Cell Line.

Transcriptional regulation is a major mechanism by which organisms integrate gene x environment interactions. It can be achieved by coordinated interplay between cis-regulatory elements (CREs) and transcription factors (TFs). Euryhaline tilapia (Oreochromis mossambicus) tolerate a wide range of salinity and thus are an appropriate model to examine transcriptional regulatory mechanisms during salinity stress in fish. Quantitative proteomics in combination with the transcription inhibitor actinomycin D revealed 19 proteins that are transcriptionally upregulated by hyperosmolality in tilapia brain (OmB) cells. We searched the extended proximal promoter up to intron1 of each corresponding gene for common motifs using motif discovery tools. The top-ranked motif identified (STREME1) represents a binding site for the Forkhead box TF L1 (FoxL1). STREME1 function during hyperosmolality was experimentally validated by choosing two of the 19 genes, chloride intracellular channel 2 (clic2) and uridine phosphorylase 1 (upp1), that are enriched in STREME1 in their extended promoters. Transcriptional induction of these genes during hyperosmolality requires STREME1, as evidenced by motif mutagenesis. We conclude that STREME1 represents a new functional CRE that contributes to gene x environment interactions during salinity stress in tilapia. Moreover, our results indicate that FoxL1 family TFs are contribute to hyperosmotic induction of genes in euryhaline fish.

Bayesian Markov models improve the prediction of binding motifs beyond first order.

Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved predictions for most TFs on in vivo data. However, they are more prone to overfit the data and to learn patterns merely correlated with rather than directly involved in TF binding. We present an improved, faster version of our Bayesian Markov model software, BaMMmotif2. We tested it with state-of-the-art motif discovery tools on a large collection of ChIP-seq and HT-SELEX datasets. BaMMmotif2 models of fifth-order achieved a median false-discovery-rate-averaged recall 13.6% and 12.2% higher than the next best tool on 427 ChIP-seq datasets and 164 HT-SELEX datasets, respectively, while being 8 to 1000 times faster. BaMMmotif2 models showed no signs of overtraining in cross-cell line and cross-platform tests, with similar improvements on the next-best tool. These results demonstrate that dependencies beyond first order clearly improve binding models for most TFs.

Locating transcription factor binding sites by fully convolutional neural network.

Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learning (DL) based methods have been proposed for predicting TF binding sites (TFBSs) and achieved impressive prediction performance. However, these methods mainly focus on predicting the sequence specificity of TF-DNA binding, which is equivalent to a sequence-level binary classification task, and fail to identify motifs and TFBSs accurately. In this paper, we developed a fully convolutional network coupled with global average pooling (FCNA), which by contrast is equivalent to a nucleotide-level binary classification task, to roughly locate TFBSs and accurately identify motifs. Experimental results on human ChIP-seq datasets show that FCNA outperforms other competing methods significantly. Besides, we find that the regions located by FCNA can be used by motif discovery tools to further refine the prediction performance. Furthermore, we observe that FCNA can accurately identify TF-DNA binding motifs across different cell lines and infer indirect TF-DNA bindings.

CEMD: A Cluster-based Ensemble Motif Discovery Tool

Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ensemble methods. A review and classification of current ensemble motif discovery tools is presented. We then propose our Cluster-based Ensemble Motif Discovery Tool (CEMD) which is based on k-medoids clustering of state-of-art stand-alone motif finding tools. We evaluate the performance of CEMD on benchmark datasets and compare the results to both stand-alone and similar ensemble tools. Experimental results indicate that CEMD has better sensitivity than state-of-art stand-alone tools when dealing with human datasets. CEMD also obtains better values of sensitivity when motifs are implanted in real promoter sequences. As for the comparison of CEMD with ensemble motif discovery tools, results indicate that CEMD achieves better results than MEME-ChIP on all evaluation measures. CEMD shows comparable performance to RSAT peak-motifs and MODSIDE.

A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs.

There are nine herpesviruses known to infect humans, of which Epstein–Barr virus (EBV) is the most widely distributed (>90% of adults infected). This ubiquitous virus is implicated in a variety of cancers and autoimmune diseases. Previous analyses of the EBV genome revealed numerous regions with evidence of generating unusually stable and conserved RNA secondary structures and led to the discovery of a novel class of EBV non-coding (nc)RNAs: the stable intronic sequence (sis)RNAs. To gain a better understanding of the roles of RNA structure in EBV biology and pathogenicity, we revisit EBV using recently developed tools for genome-wide motif discovery and RNA structural characterization. This corroborated previous results and revealed novel motifs with potential functionality; one of which has been experimentally validated. Additionally, since many herpesviruses increasingly rival the seroprevalence of EBV (VZV, HHV-6 and HHV-7 being the most notable), analyses were expanded to include all sequenced human Herpesvirus RefSeq genomes, allowing for genomic comparisons. In total 10 genomes were analyzed, for EBV (types 1 and 2), HCMV, HHV-6A, HHV-6B, HHV-7, HSV-1, HSV-2, KSHV, and VZV. All resulting data were archived in the RNAStructuromeDB (https://structurome.bb.iastate.edu/herpesvirus) to make them available to a wide array of researchers.

Matrix profile goes MAD: variable-length motif and discord discovery in data series

In the last 15 years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, including robotics, entomology, seismology, medicine, and climatology. Nevertheless, the state-of-the-art motif and discord discovery tools still require the user to provide the relative length. Yet, in several cases, the choice of length is critical and unforgiving. Unfortunately, the obvious brute-force solution, which tests all lengths within a given range, is computationally untenable. In this work, we introduce a new framework, which provides an exact and scalable motif and discord discovery algorithm that efficiently finds all motifs and discords in a given range of lengths. We evaluate our approach with five diverse real datasets, and demonstrate that it is up to 20 times faster than the state-of-the-art. Our results also show that removing the unrealistic assumption that the user knows the correct length, can often produce more intuitive and actionable results, which could have otherwise been missed.

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments

BackgroundMotif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool.MethodsWe present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems.ResultsWe demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity.ConclusionsRegmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex).

MCAT: Motif Combining and Association Tool.

De novo motif discovery in biological sequences is an important and computationally challenging problem. A myriad of algorithms have been developed to solve this problem with varying success, but it can be difficult for even a small number of these tools to reach a consensus. Because individual tools can be better suited for specific scenarios, an ensemble tool that combines the results of many algorithms can yield a more confident and complete result. We present a novel and fast tool ensemble MCAT (Motif Combining and Association Tool) for de novo motif discovery by combining six state-of-the-art motif discovery tools (MEME, BioProspector, DECOD, XXmotif, Weeder, and CMF). We apply MCAT to data sets with DNA sequences that come from various species and compare our results with two well-established ensemble motif-finding tools, EMD and DynaMIT. The experimental results show that MCAT is able to identify exact match motifs in DNA sequences efficiently, and it has a significantly better performance in practice.

Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, but inappropriate initialization of EM has yielded poor performance or running time scalability under a given data set. In this study, we used a sequential integration approach that defined the final solution as the set of solutions acquired from solving objectives in a cascade manner to integrate the fuzzy C-means and the EM approaches to DNA motif discovery. The new method is explained in detail and tested on the chromatin immunoprecipitation sequencing (ChIP-seq) data sets for different transcription factors (TFs) with various motif patterns. The proposed algorithm also suggests an efficient process for analyzing motif similarity to known motifs as well as finding a target motif. A comparison of results with those of the well-known motif-finding tool, MEME-ChIP, shows the advantages of our proposed framework over this existing tool. Experimental results show that we were able to find the true motifs for all TFs, and that the motifs found by our proposed algorithm were more similar to JASPAR-known motifs for the STAT1, GATA1, and JUN TFs than those found by MEME-ChIP.

TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets

BackgroundA strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57–74, 2012; Nat 507:462–70, 2014; Nat 507:455–61, 2014; Nat 518:317–30, 2015). Whole-genome approaches based on next-generation sequencing (NGS) have provided insight into the genomic location of regulatory elements throughout different cell types, organs and organisms. These technologies are now widespread and commonly used in laboratories from various fields of research. This highlights the need for fast and user-friendly software tools dedicated to extracting cis-regulatory information contained in these regulatory regions; for instance transcription factor binding site (TFBS) composition. Ideally, such tools should not require prior programming knowledge to ensure they are accessible for all users.ResultsWe present TrawlerWeb, a web-based version of the Trawler_standalone tool (Nat Methods 4:563–5, 2007; Nat Protoc 5:323–34, 2010), to allow for the identification of enriched motifs in DNA sequences obtained from next-generation sequencing experiments in order to predict their TFBS composition. TrawlerWeb is designed for online queries with standard options common to web-based motif discovery tools. In addition, TrawlerWeb provides three unique new features: 1) TrawlerWeb allows the input of BED files directly generated from NGS experiments, 2) it automatically generates an input-matched biologically relevant background, and 3) it displays resulting conservation scores for each instance of the motif found in the input sequences, which assists the researcher in prioritising the motifs to validate experimentally. Finally, to date, this web-based version of Trawler_standalone remains the fastest online de novo motif discovery tool compared to other popular web-based software, while generating predictions with high accuracy.ConclusionsTrawlerWeb provides users with a fast, simple and easy-to-use web interface for de novo motif discovery. This will assist in rapidly analysing NGS datasets that are now being routinely generated. TrawlerWeb is freely available and accessible at: http://trawler.erc.monash.edu.au.

An efficient method for significant motifs discovery from multiple DNA sequences.

Identification of transcription factor binding sites or biological motifs is an important step in deciphering the mechanisms of gene regulation. It is a classic problem that has eluded a satisfactory and efficient solution. In this paper, we devise a three-phase algorithm to mine for biologically significant motifs. In the first phase, we generate all the possible string motifs, this phase is followed by a filtering process where we discard all motifs that do not meet the constraints. And in the final phase, motifs are scored and ranked using a combination of stochastic techniques and [Formula: see text]-value. We show that our method outperforms some very well-known motif discovery tools, e.g. MEME and Weeder on well-established benchmark data suites. We also apply the algorithm on the non-coding regions of M.tuberculosis and report significant motifs of size 10 with excellent [Formula: see text]-values in a fraction of the time MEME and MoSDi did. In fact, among the best 10 motifs ([Formula: see text]-value wise) in the non-coding regions of M.tuberculosis reported by the tools MEME, MoSDi and ours, five were discovered by our approach which included the third and the fourth best ones. All this in 1/17 and 1/6 the time which MEME and MoSDi (respectively) took.

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.

Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines.

KpLogo: positional k-mer analysis reveals hidden specificity in biological sequences.

Motifs of only 1–4 letters can play important roles when present at key locations within macromolecules. Because existing motif-discovery tools typically miss these position-specific short motifs, we developed kpLogo, a probability-based logo tool for integrated detection and visualization of position-specific ultra-short motifs from a set of aligned sequences. kpLogo also overcomes the limitations of conventional motif-visualization tools in handling positional interdependencies and utilizing ranked or weighted sequences increasingly available from high-throughput assays. kpLogo can be found at http://kplogo.wi.mit.edu/.

SLiMSearch: a framework for proteome-wide discovery and annotation of functional modules in intrinsically disordered regions.

The extensive intrinsically disordered regions of higher eukaryotic proteomes contain vast numbers of functional interaction modules known as short linear motifs (SLiMs). Here, we present SLiMSearch, a motif discovery tool that scans a motif consensus, representing the specificity determinants of a motif-binding domain, against a proteome to discover putative novel motif instances. SLiMSearch applies several distinct and complementary approaches exploiting the common properties of SLiMs to predict novel motifs. Consensus matches are annotated with overlapping sequence annotation, including feature information describing protein modular architecture, post-translational modification, structure, sequence variation and experimental characterisation of functional regions. Discriminatory motif attributes such as conservation and accessibility are also calculated. In addition, SLiMSearch provides functional enrichment and evolutionary analysis tools. The enrichment tool analyses GO terms, keywords and interacting partner enrichment to indicate possible motif function. The evolutionary tool evaluates motif taxonomic range and the conservation of motif sequence context. Consensus matches can be filtered based on motif attributes such as accessibility and taxonomic range; or by the localisation, interacting partners or ontology annotation of the peptide-containing protein. SLiMSearch supports a range of species of experimental and therapeutic relevance and is available online at http://slim.ucd.ie/slimsearch/.