Identification Of Regulatory Motifs Research Articles

Comparison of different motif discovery algorithms

One of the most difficult challenges in molecular biology as well as computer science is finding patterns in DNA sequences. Identification of regulatory motifs is critical for understanding the gene expression. The essential concept in gene expression is that each a gene encodes the instructions for making a protein. The process of expression begins with the binding of several recognised protein factors. As transcription factors, they bind to enhancer and promoter sequences (Li and Li, 2019). Transcription is the first stage, which involves creating an RNA "copy" of a section of the DNA. This RNA sequence is read and interpreted to create a protein in the second stage of the process, known as translation. Gene expression is the combined result of these two actions. Numerous regulatory transcription factors (TFs), also known as Transcription Factor Binding Sites (TFBS), bind to certain DNA regions to control gene expression. In the past ten years, a significant new method for understanding transcription regulation networks has emerged: the computational identification of TFBS through the study of DNA sequence data (Ruzicka et al., 2017). Finding sequence motifs can be challenging since intergenic regions are extremely long and highly varied, while sequence motifs are small (approximately 6–12 bp). Sequence motifs are frequently repeated and conserved, and they have a fixed size. These patterns are critical for identifying Transcription Factor Binding Sites (TF-BSs), which aids in understanding the mechanisms governing gene expression 3. Motifs can be classified as planted, structured, gapped, sequence, network, and motifs (Hashim et al., 2019). An important issue in computational biology is the finding of weak motifs. It is challenging to solve because there are so many inconsistencies between the actual theme and its altered variants that false signals may mask the real ones. Further, it is challenging to identify and uncover regulatory elements using computer algorithms since they are typically brief and varied. The task of solving the theme finding problem is that of discovering overrepresented motifs as well as conserved motifs from the set of DNA sequences that are good candidates for becoming sites where transcription factors bind. Transcription factor is a protein that functions as a gene expression regulator, specifically regulating the start of the transcription process that produces mRNA using DNA as a template. The common sequence is called a motif. A "pattern" in a transcription factor's binding sites. Finding motifs will aid in the development of illness therapies and comprehension disease susceptibility (Mohanty and Mohanty, 2020). Many techniques for analysing gene function start with the finding of a DNA motif. Finding Transcription Factor Binding Sites (TFBSs), which aid in understanding the mechanisms for controlling gene expression, is a crucial part of motif discovery. The development of quick and precise motif discovery technologies has utilised a variety of algorithms over the years. These algorithms are typically categorised as probabilistic or consensus techniques, and many of them take a lot of time to run and are prone to get stuck in local optimums. Recently, solutions to these issues have been offered using both nature-inspired algorithms and a variety of combinatorial algorithms (Hashim et al., 2019).

Signatures of protein expression revealed by secretome analyses of cancer associated fibroblasts and melanoma cell lines

The imbalance of cellular homeostasis during oncogenesis together with the high heterogeneity of tumor-associated stromal cells have a marked effect on the repertoire of the proteins secreted by malignant cells (the secretome). Hence, the study of tumoral secretomes provides insights for understanding the cross-talk between cells within the tumor microenvironment as well as the key effectors for the establishment of the pre-metastatic niche in distant tumor sites. In this context, we performed a proteomic analysis of the secretomes derived from four cell lines: a paired set of fibroblasts - Hs 895. T, a cell line obtained from a lung node metastatic site from a patient who had melanoma and Hs 895.Sk, a skin fibroblast cell line (derived from the same patient); two malignant metastatic melanoma cell lines - A375, a malignant melanoma cell line from primary source and SH-4, a cell line derived from pleural effusion of a patient with metastatic melanoma. Clustering of expression profiles together with functional enrichment analysis resulted in patterns that mirrored each cell type. In addition, these patterns might be the result of cell-specific protein expression programs and reveal the emergence of trends in the co-expression of functionally related proteins in cellular melanoma models. SignificanceMelanoma is an aggressive skin cancer and a lethal melanocytic neoplasm with increasing annual number of cases, faster than any other solid tumor. In this context, the imbalance of cellular homeostasis during oncogenesis together with the high heterogeneity of tumor-associated stromal cells have a marked effect on the repertoire of the proteins secreted by malignant cells (the secretome). Therefore, the identification of protein expression patterns in malignant cells together with functional enrichment analysis provide insights into cell-specific protein expression programs and may reveal the emergence of trends in the co-expression of functionally related proteins regardless of cell type. Moreover, the identification of networks of protein interactions together with their expression profiles can be used for the targeted analysis of co-expressed proteins, allowing the identification of regulatory motifs in melanoma protein-protein interaction networks.

Identification of regulatory motifs in the CHO genome for stable monoclonal antibody production.

Chinese hamster ovary (CHO) cell lines are widely used for therapeutic protein production. When a transgene is integrated into the genome of a CHO cell, the expression level is highly dependent on the site of integration because of positional effects such as gene silencing. To overcome negative positional effects and establish stable CHO cell lines with high productivity, several regulatory DNA elements are used in vector construction. Previously, we established the CHO DR1000L-4N cell line, a stable and high copy number Dhfr gene-amplified cell line. It was hypothesized that the chromosomal location of the exogenous gene-amplified region in the CHO DR1000L-4N genome contains regulatory motifs for stable protein production. Therefore, we isolated DNA regulatory motifs from the CHO DR1000L-4N cell line and determined whether these motifs act as an insulator. Our results suggest that stable expression of a transgene can be promoted by the CHO genome sequence, and it would be a powerful tool for therapeutic protein manufacturing.

In silico identification of regulatory motifs in co-expressed genes under osmotic stress representing their co-regulation

Osmotic stress is one of the abiotic conditions for plants responsible for osmotic imbalance. The genes have the capability to show differential expression in response to such conditions. In order to understand the role of genes towards multiple stresses simultaneously, a coexpression study is required. In our analysis, the coexpressed genes of Solanum tuberosum showed a positive correlation (0.91) in salt and drought stresses. The genes showing similar expression were grouped into five sub groups. Sub group 2 revealed the highest number of genes and formed a network. The genes of this network were found to be coding for different stress related proteins. The largest portion (25%) of genes was found to be coding for lipoxygenase revealing its role in jasmonic acid pathway responsible for abiotic stresses. The coexpressed genes were further analyzed for their regulation by the same regulatory factor. The results suggest that the coexpressed genes were regulated due to presence of similar binding sites for EREBP/AP2. The gene expression, their coexpression, functional annotation and coregulation were integrated to form a network. This approach could yield that the coexpressed genes are under the control of the same regulatory system thus are coregulated and form a network.

RMOD: A Tool for Regulatory Motif Detection in Signaling Network

Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs

BackgroundFunctional RNA molecules participate in numerous biological processes, ranging from gene regulation to protein synthesis. Analysis of functional RNA motifs and elements in RNA sequences can obtain useful information for deciphering RNA regulatory mechanisms. Our previous work, RegRNA, is widely used in the identification of regulatory motifs, and this work extends it by incorporating more comprehensive and updated data sources and analytical approaches into a new platform.Methods and resultsAn integrated web-based system, RegRNA 2.0, has been developed for comprehensively identifying the functional RNA motifs and sites in an input RNA sequence. Numerous data sources and analytical approaches are integrated, and several types of functional RNA motifs and sites can be identified by RegRNA 2.0: (i) splicing donor/acceptor sites; (ii) splicing regulatory motifs; (iii) polyadenylation sites; (iv) ribosome binding sites; (v) rho-independent terminator; (vi) motifs in mRNA 5'-untranslated region (5'UTR) and 3'UTR; (vii) AU-rich elements; (viii) C-to-U editing sites; (ix) riboswitches; (x) RNA cis-regulatory elements; (xi) transcriptional regulatory motifs; (xii) user-defined motifs; (xiii) similar functional RNA sequences; (xiv) microRNA target sites; (xv) non-coding RNA hybridization sites; (xvi) long stems; (xvii) open reading frames; (xviii) related information of an RNA sequence. User can submit an RNA sequence and obtain the predictive results through RegRNA 2.0 web page.ConclusionsRegRNA 2.0 is an easy to use web server for identifying regulatory RNA motifs and functional sites. Through its integrated user-friendly interface, user is capable of using various analytical approaches and observing results with graphical visualization conveniently. RegRNA 2.0 is now available at http://regrna2.mbc.nctu.edu.tw.

An alternative approach to multiple genome comparison

Genome comparison is now a crucial step for genome annotation and identification of regulatory motifs. Genome comparison aims for instance at finding genomic regions either specific to or in one-to-one correspondance between individuals/strains/species. It serves e.g. to pre-annotate a new genome by automatically transfering annotations from a known one. However, efficiency, flexibility and objectives of current methods do not suit the whole spectrum of applications, genome sizes and organizations. Innovative approaches are still needed. Hence, we propose an alternative way of comparing multiple genomes based on segmentation by similarity. In this framework, rather than being formulated as a complex optimization problem, genome comparison is seen as a segmentation question for which a single optimal solution can be found in almost linear time. We apply our method to analyse three strains of a virulent pathogenic bacteria, Ehrlichia ruminantium, and identify 92 new genes. We also find out that a substantial number of genes thought to be strain specific have potential orthologs in the other strains. Our solution is implemented in an efficient program, qod, equipped with a user-friendly interface, and enables the automatic transfer of annotations betwen compared genomes or contigs (Video in Supplementary Data). Because it somehow disregards the relative order of genomic blocks, qod can handle unfinished genomes, which due to the difficulty of sequencing completion may become an interesting characteristic for the future. Availabilty: http://www.atgc-montpellier.fr/qod.

Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

BackgroundExisting clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.ResultsWe applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.ConclusionsThese results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.

Functions of Bifans in Context of Multiple Regulatory Motifs in Signaling Networks

Representation of intracellular signaling networks as directed graphs allows for the identification of regulatory motifs. Regulatory motifs are groups of nodes with the same connectivity structure, capable of processing information. The bifan motif, made of two source nodes directly crossregulating two target nodes, is an overrepresented motif in a mammalian cell signaling network and in transcriptional networks. One example of a bifan is the two MAP-kinases, p38, and JNK that phosphorylate and activate the two transcription factors ATF2 and Elk-1. We have used a system of coupled ordinary differential equations to analyze the regulatory capability of this bifan motif by itself, and when it interacts with other motifs such as positive and negative feedback loops. Our results indicate that bifans provide temporal regulation of signal propagation and act as signal sorters, filters, and synchronizers. Bifans that have OR gate configurations show rapid responses whereas AND gate bifans can introduce delays and allow prolongation of signal outputs. Bifans that have AND gates can filter noisy signal inputs. The p38/JNK-ATF2/Elk-1bifan synchronizes the output of activated transcription factors. Synchronization is a robust property of bifans and is exhibited even when the bifan is adjacent to a positive feedback loop. The presence of the bifan promotes the transcription and translation of the dual specificity protein phosphatase MKP-1 that inhibits p38 and JNK thus enabling a negative feedback loop. These results indicate that bifan motifs in cell signaling networks can contribute to signal processing capability both intrinsically and by enabling the functions of other regulatory motifs.

Gene ordering in partitive clustering using microarray expressions

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarray gene expressions.Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fibroblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution.

PEAKS: identification of regulatory motifs by their position in DNA sequences

Many DNA functional motifs tend to accumulate or cluster at specific gene locations. These locations can be detected, in a group of gene sequences, as high frequency 'peaks' with respect to a reference position, such as the transcription start site (TSS). We have developed a web tool for the identification of regions containing significant motif peaks. We show, by using different yeast gene datasets, that peak regions are strongly enriched in experimentally-validated motifs and contain potentially important novel motifs. http://genomics.imim.es/peaks

Bayesian Variable Selection for Gene Expression Modeling With Regulatory Motif Binding Sites in Neuroinflammatory Events

Multiple transcription factors (TFs) coordinately control transcriptional regulation of genes in eukaryotes. Although numerous computational methods focus on the identification of individual TF-binding sites (TFBSs), very few consider the interdependence among these sites. In this article, we studied the relationship between TFBSs and microarray gene expression levels using both family-wise and memberspecific motifs, under various combination of regression models with Bayesian variable selection, as well as motif scoring and sharing conditions, in order to account for the coordination complexity of transcription regulation. We proposed a three-step approach to model the relationship. In the first step, we preprocessed microarray data and used p-values and expression ratios to preselect upregulated and downregulated genes. The second step aimed to identify and score individual TFBSs within DNA sequence of each gene. A method based on the degree of similarity and the number of TFBSs was employed to calculate the score of each TFBS in each gene sequence. In the last step, linear regression and probit regression were used to build a predictive model of gene expression outcomes using these TFBSs as predictors. Given a certain number of predictors to be used, a full search of all possible predictor sets is usually combinatorially prohibitive. Therefore, this article considered the Bayesian variable selection for prediction using either of the regression models. The Bayesian variable selection has been applied in the context of gene selection, missing value estimation, and regulatory motif identification. In our modeling, the regressor was approximated as a linear combination of the TFBSs and a Gibbs sampler was employed to find the strongest TFBSs. We applied these regression models with the Bayesian variable selection on spinal cord injury gene expression data set. These TFs demonstrated intricate regulatory roles either as a family or as individual members in neuroinflammatory events. Our analysis can be applied to create plausible hypotheses for combinatorial regulation by TFBSs and avoiding false-positive candidates in the modeling process at the same time. Such a systematic approach provides the possibility to dissect transcription regulation, from a more comprehensive perspective, through which phenotypical events at cellular and tissue levels are moved forward by molecular events at gene transcription and translation levels.

A novel approach to identifying regulatory motifs in distantly related genomes

A two-step procedure for identifying regulatory motifs in distantly related organisms is described that combines the advantages of sequence alignment and motif detection approaches.

A suite of web-based programs to search for transcriptional regulatory motifs.

The identification of regulatory motifs is important for the study of gene expression. Here we present a suite of programs that we have developed to search for regulatory sequence motifs: (i) BioProspector, a Gibbs-sampling-based program for predicting regulatory motifs from co-regulated genes in prokaryotes or lower eukaryotes; (ii) CompareProspector, an extension to BioProspector which incorporates comparative genomics features to be used for higher eukaryotes; (iii) MDscan, a program for finding protein-DNA interaction sites from ChIP-on-chip targets. All three programs examine a group of sequences that may share common regulatory motifs and output a list of putative motifs as position-specific probability matrices, the individual sites used to construct the motifs and the location of each site on the input sequences. The web servers and executables can be accessed at http://seqmotifs.stanford.edu.

Methods in comparative genomics: genome correspondence, gene identification and regulatory motif discovery.

In Kellis et al. (2003), we reported the genome sequences of S. paradoxus, S. mikatae, and S. bayanus and compared these three yeast species to their close relative, S. cerevisiae. Genomewide comparative analysis allowed the identification of functionally important sequences, both coding and noncoding. In this companion paper we describe the mathematical and algorithmic results underpinning the analysis of these genomes. (1) We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change. (2) We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. (3) We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on genomewide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs. Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast and will be invaluable in the study of complex genomes like that of the human.

Genome wide identification of regulatory motifs in Bacillus subtilis

BackgroundTo explain the vastly different phenotypes exhibited by the same organism under different conditions, it is essential that we understand how the organism's genes are coordinately regulated. While there are many excellent tools for predicting sequences encoding proteins or RNA genes, few algorithms exist to predict regulatory sequences on a genome wide scale with no prior information.ResultsTo identify motifs involved in the control of transcription, an algorithm was developed that searches upstream of operons for improbably frequent dimers. The algorithm was applied to the B. subtilis genome, which is predicted to encode for approximately 200 DNA binding proteins. The dimers found to be over-represented could be clustered into 317 distinct groups, each thought to represent a class of motifs uniquely recognized by some transcription factor. For each cluster of dimers, a representative weight matrix was derived and scored over the regions upstream of the operons to predict the sites recognized by the cluster's factor, and a putative regulon of the operons immediately downstream of the sites was inferred. The distribution in number of operons per predicted regulon is comparable to that for well characterized transcription factors. The most highly over-represented dimers matched σA, the T-box, and σW sites. We have evidence to suggest that at least 52 of our clusters of dimers represent actual regulatory motifs, based on the groups' weight matrix matches to experimentally characterized sites, the functional similarity of the component operons of the groups' regulons, and the positional biases of the weight matrix matches. All predictions are assigned a significance value, and thresholds are set to avoid false positives. Where possible, we examine our false negatives, drawing examples from known regulatory motifs and regulons inferred from RNA expression data.ConclusionsWe have demonstrated that in the case of B. subtilis our algorithm allows for the genome wide identification of regulatory sites. As well as recovering known sites, we predict new sites of yet uncharacterized factors. Results can be viewed at .

Oligo-capped cDNAs for promoter identification and annotation

In order to obtain precise information of 5′ end of mRNAs, we have developed the oligo-capping method that replaces the cap of mRNA with synthetic oligonucleotides. Using the data from oligo-capped cDNAs, we constructed database of transcriptional start sites (DBTSS; http://elmo.ims.u-tokyo.ac.jp/dbtss/). This database shows the information on distribution of transcriptional start sites and can be used to identify the core promoter region. Here we show the summery and the update of DBTSS and its future use on identification of regulatory motifs within the promoter.