Identification of candidate transcription factor binding sites in the cattle genome.
Ontology highlight
ABSTRACT:
SUBMITTER: Bickhart DM
PROVIDER: S-EPMC4357788 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Despite almost 40 years of molecular genetics research in Escherichia coli a major fraction of its Transcription Start Sites (TSSs) are still unknown, limiting therefore our understanding of the regulatory circuits that control gene expression in this model organism. RegulonDB (http://regulondb.ccg.unam.mx/) is aimed at integrating the genetic regulatory network of E. coli K12 as an entirely bioinformatic project up till now. In this work, we extended its aims by generating experimental data at a genome scale on TSSs, promoters and regulatory regions. We implemented a modified 5' RACE protocol and an unbiased High Throughput Pyrosequencing Strategy (HTPS) that allowed us to map more than 1700 TSSs with high precision. From this collection, about 230 corresponded to previously reported TSSs, which helped us to benchmark both our methodologies and the accuracy of the previous mapping experiments. The other ca 1500 TSSs mapped belong to about 1000 different genes, many of them with no assigned function. We identified promoter sequences and type of sigma factors that control the expression of about 80% of these genes. As expected, the housekeeping sigma(70) was the most common type of promoter, followed by sigma(38). The majority of the putative TSSs were located between 20 to 40 nucleotides from the translational start site. Putative regulatory binding sites for transcription factors were detected upstream of many TSSs. For a few transcripts, riboswitches and small RNAs were found. Several genes also had additional TSSs within the coding region. Unexpectedly, the HTPS experiments revealed extensive antisense transcription, probably for regulatory functions. The new information in RegulonDB, now with more than 2400 experimentally determined TSSs, strengthens the accuracy of promoter prediction, operon structure, and regulatory networks and provides valuable new information that will facilitate the understanding from a global perspective the complex and intricate regulatory network that operates in E. coli.
| S-EPMC2760140 | biostudies-literature
Project description:Nannochloropsis spp. are a group of oleaginous microalgae that harbor an expanded array of lipid-synthesis related genes, yet how they are transcriptionally regulated remains unknown. Here a phylogenomic approach was employed to identify and functionally annotate the transcriptional factors (TFs) and TF binding-sites (TFBSs) in N. oceanica IMET1. Among 36 microalgae and higher plants genomes, a two-fold reduction in the number of TF families plus a seven-fold decrease of average family-size in Nannochloropsis, Rhodophyta and Chlorophyta were observed. The degree of similarity in TF-family profiles is indicative of the phylogenetic relationship among the species, suggesting co-evolution of TF-family profiles and species. Furthermore, comparative analysis of six Nannochloropsis genomes revealed 68 "most-conserved" TFBS motifs, with 11 of which predicted to be related to lipid accumulation or photosynthesis. Mapping the IMET1 TFs and TFBS motifs to the reference plant TF-"TFBS motif" relationships in TRANSFAC enabled the prediction of 78 TF-"TFBS motif" interaction pairs, which consisted of 34 TFs (with 11 TFs potentially involved in the TAG biosynthesis pathway), 30?TFBS motifs and 2,368 regulatory connections between TFs and target genes. Our results form the basis of further experiments to validate and engineer the regulatory network of Nannochloropsis spp. for enhanced biofuel production.
| S-EPMC5154493 | biostudies-literature
Project description:Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.
| S-EPMC6391789 | biostudies-literature
Project description:BackgroundGenome-wide maps of transcription factor binding sites in primary tissues can expand our understanding of genome function, transcriptional regulation, and genetic alterations that contribute to disease risk. However, almost all genome-wide studies of transcription factors have been in cell lines, and performing these experiments in tissues has been technically challenging and limited in throughput.ResultsHere we outline a simple strategy for mapping transcription factor binding sites in frozen tissues that utilizes dry pulverization of samples and is scalable for high-throughput analyses. We show that the method leads to accurate and reproducible chromatin immunoprecipitation next-generation sequencing (ChIP-seq) data, and is highly sensitive, identifying high-quality transcription factor binding sites from chromatin corresponding to only 5 mg of liver tissue.ConclusionsThe enhanced reproducibility, robustness, and sensitivity of the dry pulverization method, in addition to the ease of implementation and scalability, makes ChIP-seq in primary tissues a widely accessible assay.
| S-EPMC3848595 | biostudies-literature
Project description:BACKGROUND:When transcription factor binding sites are known for a particular transcription factor, it is possible to construct a motif model that can be used to scan sequences for additional sites. However, few statistically significant sites are revealed when a transcription factor binding site motif model is used to scan a genome-scale database. METHODS:We have developed a scanning algorithm, PhyloScan, which combines evidence from matching sites found in orthologous data from several related species with evidence from multiple sites within an intergenic region, to better detect regulons. The orthologous sequence data may be multiply aligned, unaligned, or a combination of aligned and unaligned. In aligned data, PhyloScan statistically accounts for the phylogenetic dependence of the species contributing data to the alignment and, in unaligned data, the evidence for sites is combined assuming phylogenetic independence of the species. The statistical significance of the gene predictions is calculated directly, without employing training sets. RESULTS:In a test of our methodology on synthetic data modeled on seven Enterobacteriales, four Vibrionales, and three Pasteurellales species, PhyloScan produces better sensitivity and specificity than MONKEY, an advanced scanning approach that also searches a genome for transcription factor binding sites using phylogenetic information. The application of the algorithm to real sequence data from seven Enterobacteriales species identifies novel Crp and PurR transcription factor binding sites, thus providing several new potential sites for these transcription factors. These sites enable targeted experimental validation and thus further delineation of the Crp and PurR regulons in E. coli. CONCLUSION:Better sensitivity and specificity can be achieved through a combination of (1) using mixed alignable and non-alignable sequence data and (2) combining evidence from multiple sites within an intergenic region.
| S-EPMC1794230 | biostudies-literature
Project description:A long-standing objective in modern biology is to characterize the molecular components that drive the development of an organism. At the heart of eukaryotic development lies gene regulation. On the molecular level, much of the research in this field has focused on the binding of transcription factors (TFs) to regulatory regions in the genome known as cis-regulatory modules (CRMs). However, relatively little is known about the sequence-specific binding preferences of many TFs, especially with respect to the possible interdependencies between the nucleotides that make up binding sites. A particular limitation of many existing algorithms that aim to predict binding site sequences is that they do not allow for dependencies between nonadjacent nucleotides. In this study, we use a recently developed computational algorithm, MARZ, to compare binding site sequences using 32 distinct models in a systematic and unbiased approach to explore nucleotide dependencies within binding sites for 15 distinct TFs known to be critical to Drosophila development. Our results indicate that many of these proteins have varying levels of nucleotide interdependencies within their DNA recognition sequences, and that, in some cases, models that account for these dependencies greatly outperform traditional models that are used to predict binding sites. We also directly compare the ability of different models to identify the known KRUPPEL TF binding sites in CRMs and demonstrate that a more complex model that accounts for nucleotide interdependencies performs better when compared with simple models. This ability to identify TFs with critical nucleotide interdependencies in their binding sites will lead to a deeper understanding of how these molecular characteristics contribute to the architecture of CRMs and the precise regulation of transcription during organismal development.
| S-EPMC4907338 | biostudies-literature
Project description:To represent the sequence specificity of transcription factors, the position weight matrix (PWM) is widely used. In most cases, each element is defined as a log likelihood ratio of a base appearing at a certain position, which is estimated from a finite number of known binding sites. To avoid bias due to this small sample size, a certain numeric value, called a pseudocount, is usually allocated for each position, and its fraction according to the background base composition is added to each element. So far, there has been no consensus on the optimal pseudocount value. In this study, we simulated the sampling process by artificially generating binding sites based on observed nucleotide frequencies in a public PWM database, and then the generated matrix with an added pseudocount value was compared to the original frequency matrix using various measures. Although the results were somewhat different between measures, in many cases, we could find an optimal pseudocount value for each matrix. These optimal values are independent of the sample size and are clearly correlated with the entropy of the original matrices, meaning that larger pseudocount vales are preferable for less conserved binding sites. As a simple representative, we suggest the value of 0.8 for practical uses.
| S-EPMC2647310 | biostudies-literature
Project description:UnlabelledThe expressions of proteins in the cell are carefully regulated by transcription factors that interact with their downstream targets in specific signal transduction cascades. Our understanding of the regulation of functional genes responsive to stress signals is still nascent. Plants like Arabidopsis thaliana, are convenient model systems to study fundamental questions related to regulation of the stress transcriptome in response to stress challenges. Microarray results of the Arabidopsis transcriptome indicate that several genes could be upregulated during multiple stresses, such as cold, salinity, drought etc. Experimental biochemical validations have proved the involvement of several transcription factors could be involved in the upregulation of these stress responsive genes. In order to follow the intricate and complicated networks of transcription factors and genes that respond to stress situations in plants, we have developed a computer algorithm that can identify key transcription factor binding sites upstream of a gene of interest. Hidden Markov models of the transcription factor binding sites enable the identification of predicted sites upstream of plant stress genes. The search algorithm, STIF, performs very well, with more than 90% sensitivity, when tested on experimentally validated positions of transcription factor binding sites on a dataset of 60 stress upregulated genes.AvailabilitySupplementary data is available at http://caps.ncbs.res.in/download/stif.
| S-EPMC2561162 | biostudies-literature
Project description:Nanobodies® are single-domain antibody fragments derived from camelid heavy-chain antibodies. Because of their small size, straightforward production in Escherichia coli, easy tailoring, high affinity, specificity, stability and solubility, nanobodies® have been exploited in various biotechnological applications. A major challenge in the post-genomics and post-proteomics era is the identification of regulatory networks involving nucleic acid-protein and protein-protein interactions. Here, we apply a nanobody® in chromatin immunoprecipitation followed by DNA microarray hybridization (ChIP-chip) for genome-wide identification of DNA-protein interactions. The Lrp-like regulator Ss-LrpB, arguably one of the best-studied specific transcription factors of the hyperthermophilic archaeon Sulfolobus solfataricus, was chosen for this proof-of-principle nanobody®-assisted ChIP. Three distinct Ss-LrpB-specific nanobodies®, each interacting with a different epitope, were generated for ChIP. Genome-wide ChIP-chip with one of these nanobodies® identified the well-established Ss-LrpB binding sites and revealed several unknown target sequences. Furthermore, these ChIP-chip profiles revealed auxiliary operator sites in the open reading frame of Ss-lrpB. Our work introduces nanobodies® as a novel class of affinity reagents for ChIP. Taking into account the unique characteristics of nanobodies®, in particular, their short generation time, nanobody®-based ChIP is expected to further streamline ChIP-chip and ChIP-Seq experiments, especially in organisms with no (or limited) possibility of genetic manipulation.
| S-EPMC3597646 | biostudies-literature
Project description:For decades, it has been hypothesized that gene regulation has had a central role in human evolution, yet much remains unknown about the genome-wide impact of regulatory mutations. Here we use whole-genome sequences and genome-wide chromatin immunoprecipitation and sequencing data to demonstrate that natural selection has profoundly influenced human transcription factor binding sites since the divergence of humans from chimpanzees 4-6 million years ago. Our analysis uses a new probabilistic method, called INSIGHT, for measuring the influence of selection on collections of short, interspersed noncoding elements. We find that, on average, transcription factor binding sites have experienced somewhat weaker selection than protein-coding genes. However, the binding sites of several transcription factors show clear evidence of adaptation. Several measures of selection are strongly correlated with predicted binding affinity. Overall, regulatory elements seem to contribute substantially to both adaptive substitutions and deleterious polymorphisms with key implications for human evolution and disease.
| S-EPMC3932982 | biostudies-literature