Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discovery of sequence motifs related to coexpression of genes using evolutionary computation.

ABSTRACT: Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging problem. Here, we demonstrate that evolutionary computation can be used to search for TFBSs in upstream regions of genes known to be coexpressed. Evolutionary computation was used to search for TFBSs of genes regulated by octamer-binding factor and nuclear factor kappa B. The discovered binding sites included experimentally determined known binding motifs as well as lists of putative, previously unknown TFBSs. We believe that this method to search nucleotide sequence information efficiently for similar motifs will be useful for discovering TFBSs that affect gene regulation.

SUBMITTER: Fogel GB

PROVIDER: S-EPMC506801 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Discovery of sequence motifs related to coexpression of genes using evolutionary computation.

Fogel Gary B GB Weekes Dana G DG Varga Gabor G Dow Ernst R ER Harlow Harry B HB Onyia Jude E JE Su Chen C

Nucleic acids research 20040720 13

Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging problem. Here, we demonstrate that evolutionary computation can be used to search for TFBSs in upstream regions of genes known to be coexpressed. Evolutionary c ...[more]

PMID: 15266008

Similar Datasets

Discovery of RNA structural elements using evolutionary computation.

Project description:RNA molecules fold into characteristic secondary and tertiary structures that account for their diverse functional activities. Many of these RNA structures, or certain structural motifs within them, are thought to recur in multiple genes within a single organism or across the same gene in several organisms and provide a common regulatory mechanism. Search algorithms, such as RNAMotif, can be used to mine nucleotide sequence databases for these repeating motifs. RNAMotif allows users to capture essential features of known structures in detailed descriptors and can be used to identify, with high specificity, other similar motifs within the nucleotide database. However, when the descriptor constraints are relaxed to provide more flexibility, or when there is very little a priori information about hypothesized RNA structures, the number of motif 'hits' may become very large. Exhaustive methods to search for similar RNA structures over these large search spaces are likely to be computationally intractable. Here we describe a powerful new algorithm based on evolutionary computation to solve this problem. A series of experiments using ferritin IRE and SRP RNA stem-loop motifs were used to verify the method. We demonstrate that even when searching extremely large search spaces, of the order of 10(23) potential solutions, we could find the correct solution in a fraction of the time it would have taken for exhaustive comparisons.

| S-EPMC137967 | biostudies-literature

Computation-based discovery of related transcriptional regulatory modules and motifs using an experimentally validated combinatorial model.

Project description:Gene expression is regulated by transcription factors that interact with cis-regulatory elements. Predicting these elements from sequence data has proven difficult. We describe here a successful computational search for elements that direct expression in a particular temporal-spatial pattern in the Drosophila embryo, based on a single well characterized enhancer model. The fly genome was searched to identify sequence elements containing the same combination of transcription factors as those found in the model. Experimental evaluation of the search results demonstrates that our method can correctly predict regulatory elements and highlights the importance of functional testing as a means of identifying false-positive results. We also show that the search results enable the identification of additional relevant sequence motifs whose functions can be empirically validated. This approach, combined with gene expression and phylogenetic sequence data, allows for genome-wide identification of related regulatory elements, an important step toward understanding the genetic regulatory networks involved in development.

| S-EPMC186630 | biostudies-literature

Evolutionary computation for discovery of composite transcription factor binding sites.

Project description:Previous research demonstrated the use of evolutionary computation for the discovery of transcription factor binding sites (TFBS) in promoter regions upstream of coexpressed genes. However, it remained unclear whether or not composite TFBS elements, commonly found in higher organisms where two or more TFBSs form functional complexes, could also be identified by using this approach. Here, we present an important refinement of our previous algorithm and test the identification of composite elements using NFAT/AP-1 as an example. We demonstrate that by using appropriate existing parameters such as window size, novel-scoring methods such as central bonusing and methods of self-adaptation to automatically adjust the variation operators during the evolutionary search, TFBSs of different sizes and complexity can be identified as top solutions. Some of these solutions have known experimental relationships with NFAT/AP-1. We also indicate that even after properly tuning the model parameters, the choice of the appropriate window size has a significant effect on algorithm performance. We believe that this improved algorithm will greatly augment TFBS discovery.

| S-EPMC2588514 | biostudies-literature

Using evolutionary computation on GPS position correction.

Project description:More and more devices are equipped with global positioning system (GPS). However, those handheld devices with consumer-grade GPS receivers usually have low accuracy in positioning. A position correction algorithm is therefore useful in this case. In this paper, we proposed an evolutionary computation based technique to generate a correction function by two GPS receivers and a known reference location. Locating one GPS receiver on the known location and combining its longitude and latitude information and exact poisoning information, the proposed technique is capable of evolving a correction function by such. The proposed technique can be implemented and executed on handheld devices without hardware reconfiguration. Experiments are conducted to demonstrate performance of the proposed technique. Positioning error could be significantly reduced from the order of 10 m to the order of 1 m.

| S-EPMC3918392 | biostudies-other

Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis.

Project description:Major advances in RNA secondary structural motif prediction have been achieved in the last few years; however, few methods harness the predictive power of multiple approaches to deliver in-depth characterizations of local RNA motifs and their potential functionality. Additionally, most available methods do not predict RNA pseudoknots. This work combines complementary bioinformatic systems into one robust discovery pipeline where: •RNA sequences are folded to search for thermodynamically favorable motifs utilizing ScanFold.•Motifs are expanded and refolded into alternate pseudoknot conformations by Knotty/Iterative HFold.•All conformations are evaluated for covariance via the cm-builder pipeline (Infernal and R-scape).

| S-EPMC10336498 | biostudies-literature

Phosphatidylinositol transfer proteins: sequence motifs in structural and evolutionary analyses.

Project description:Phosphatidylinositol transfer proteins (PITP) are a family of monomeric proteins that bind and transfer phosphatidylinositol and phosphatidylcholine between membrane compartments. They are required for production of inositol and diacylglycerol second messengers, and are found in most metazoan organisms. While PITPs are known to carry out crucial cell-signaling roles in many organisms, the structure, function and evolution of the majority of family members remains unexplored; primarily because the ubiquity and diversity of the family thwarts traditional methods of global alignment. To surmount this obstacle, we instead took a novel approach, using MEME and a parsimony-based analysis to create a cladogram of conserved sequence motifs in 56 PITP family proteins from 26 species. In keeping with previous functional annotations, three clades were supported within our evolutionary analysis; two classes of soluble proteins and a class of membrane-associated proteins. By, focusing on conserved regions, the analysis allowed for in depth queries regarding possible functional roles of PITP proteins in both intra- and extra- cellular signaling.

| S-EPMC4943071 | biostudies-literature

High-quality Japanese flounder genome aids in identifying stress-related genes using gene coexpression network.

Project description:The Japanese flounder is one of the most economically important marine flatfish. However, due to the increased frequency of extreme weather events and high-density industrial farming, an increasing number of environmental stresses have become severe threats to the healthy development of the Japanese flounder culture industry. Herein, we produced a high-quality chromosome-scale Japanese flounder genome using PacBio Circular Consensus Sequencing technologies. The assembled Japanese flounder genome spanned 588.22 Mb with a contig N50 size of 24.35 Mb. In total, 105.89 Mb of repetitive sequences and 22,565 protein-coding genes were identified by genome annotation. In addition, 67 candidate genes responding to distinct stresses were identified by gene coexpression network analysis based on 16 published stress-related RNA-seq datasets encompassing 198 samples. A high-quality chromosome-scale Japanese flounder genome and candidate stress-related gene set will not only serve as key resources for genomics studies and further research on the underlying stress responsive molecular mechanisms in Japanese flounder but will also advance the progress of genetic improvement and comprehensive stress-resistant molecular breeding of Japanese flounder.

| S-EPMC9668919 | biostudies-literature

Finding regulatory DNA motifs using alignment-free evolutionary conservation information.

Project description:As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at detecting regulatory elements in intergenic sequences are becoming more prevalent. Most comparative methods for transcription factor (TF) binding site discovery make use of global or local alignments of orthologous regulatory regions to assess whether a particular DNA site is conserved across related organisms, and thus more likely to be functional. Since binding sites are usually short, sometimes degenerate, and often independent of orientation, alignment algorithms may not align them correctly. Here, we present a novel, alignment-free approach for using conservation information for TF binding site discovery. We relax the definition of conserved sites: we consider a DNA site within a regulatory region to be conserved in an orthologous sequence if it occurs anywhere in that sequence, irrespective of orientation. We use this definition to derive informative priors over DNA sequence positions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery. Our approach is simple and fast. It requires neither sequence alignments nor the phylogenetic relationships between the orthologous sequences, yet it is more effective on real biological data than methods that do.

| S-EPMC2847231 | biostudies-other

Positionally cloned human disease genes: patterns of evolutionary conservation and functional motifs.

Project description:Positional cloning has already produced the sequences of more than 70 human genes associated with specific diseases. In addition to their medical importance, these genes are of interest as a set of human genes isolated solely on the basis of the phenotypic effect of the respective mutations. We analyzed the protein sequences encoded by the positionally cloned disease genes using an iterative strategy combining several sensitive computer methods. Comparisons to complete sequence databases and to separate databases of nematode, yeast, and bacterial proteins showed that for most of the disease gene products, statistically significant sequence similarities are detectable in each of the model organisms. Only the nematode genome encodes apparent orthologs with conserved domain architecture for the majority of the disease genes. In yeast and bacterial homologs, domain organization is typically not conserved, and sequence similarity is limited to individual domains. Generally, human genes complement mutations only in orthologous yeast genes. Most of the positionally cloned genes encode large proteins with several globular and nonglobular domains, the functions of some or all of which are not known. We detected conserved domains and motifs not described previously in a number of proteins encoded by disease genes and predicted functions for some of them. These predictions include an ATP-binding domain in the product of hereditary nonpolyposis colon cancer gene (a MutL homolog), which is conserved in the HS90 family of chaperone proteins, type II DNA topoisomerases, and histidine kinases, and a nuclease domain homologous to bacterial RNase D and the 3'-5' exonuclease domain of DNA polymerase I in the Werner syndrome gene product.

| S-EPMC20866 | biostudies-literature

Estimation and efficient computation of the true probability of recurrence of short linear protein sequence motifs in unrelated proteins.

Project description:BackgroundLarge datasets of protein interactions provide a rich resource for the discovery of Short Linear Motifs (SLiMs) that recur in unrelated proteins. However, existing methods for estimating the probability of motif recurrence may be biased by the size and composition of the search dataset, such that p-value estimates from different datasets, or from motifs containing different numbers of non-wildcard positions, are not strictly comparable. Here, we develop more exact methods and explore the potential biases of computationally efficient approximations.ResultsA widely used heuristic for the calculation of motif over-representation approximates motif probability by assuming that all proteins have the same length and composition. We introduce pv, which calculates the probability exactly. Secondly, the recently introduced SLiMFinder statistic Sig, accounts for multiple testing (across all possible motifs) in motif discovery. However, it approximates the probability of all other possible motifs, occurring with a score of p or less, as being equal to p. Here, we show that the exhaustive calculation of the probability of all possible motif occurrences that are as rare or rarer than the motif of interest, Sig', may be carried out efficiently by grouping motifs of a common probability (i.e. those which have permuted orders of the same residues). Sig'v, which corrects both approximations, is shown to be uniformly distributed in a random dataset when searching for non-ambiguous motifs, indicating that it is a robust significance measure.ConclusionsA method is presented to compute exactly the true probability of a non-ambiguous short protein sequence motif, and the utility of an approximate approach for novel motif discovery across a large number of datasets is demonstrated.

| S-EPMC2819990 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data