DNB-based on-chip motif finding: A high-throughput method to profile different types of protein-DNA interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Li Z
PROVIDER: S-EPMC7399529 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.
| S-EPMC3326300 | biostudies-literature
Project description:ChIP-seq, which combines chromatin immunoprecipitation (ChIP) with next-generation parallel sequencing, allows for the genome-wide identification of protein-DNA interactions. This technology poses new challenges for the development of novel motif-finding algorithms and methods for determining exact protein-DNA binding sites from ChIP-enriched sequencing data. State-of-the-art heuristic, exhaustive search algorithms have limited application for the identification of short (l, d) motifs (l ≤ 10, d ≤ 2) contained in ChIP-enriched regions. In this work we have developed a more powerful exhaustive method (FMotif) for finding long (l, d) motifs in DNA sequences. In conjunction with our method, we have adopted a simple ChIP-enriched sampling strategy for finding these motifs in large-scale ChIP-enriched regions. Empirical studies on synthetic samples and applications using several ChIP data sets including 16 TF (transcription factor) ChIP-seq data sets and five TF ChIP-exo data sets have demonstrated that our proposed method is capable of finding these motifs with high efficiency and accuracy. The source code for FMotif is available at http://211.71.76.45/FMotif/.
| S-EPMC3901781 | biostudies-literature
Project description:ChIP-Seq (chromatin immunoprecipitation sequencing) has provided the advantage for finding motifs as ChIP-Seq experiments narrow down the motif finding to binding site locations. Recent motif finding tools facilitate the motif detection by providing user-friendly Web interface. In this work, we reviewed nine motif finding Web tools that are capable for detecting binding site motifs in ChIP-Seq data. We showed each motif finding Web tool has its own advantages for detecting motifs that other tools may not discover. We recommended the users to use multiple motif finding Web tools that implement different algorithms for obtaining significant motifs, overlapping resemble motifs, and non-overlapping motifs. Finally, we provided our suggestions for future development of motif finding Web tool that better assists researchers for finding motifs in ChIP-Seq data.
| S-EPMC4022013 | biostudies-literature
Project description:BackgroundSupervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.ResultsWe developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at http://virus.hpid.org/interactions.aspx. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at http://virus.hpid.org/PDB_IDs.html.ConclusionWhen the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.
| S-EPMC2648735 | biostudies-literature
Project description:BACKGROUND: The large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites. RESULTS: Here, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature. CONCLUSIONS: SeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.
| S-EPMC4223730 | biostudies-literature
Project description:An on-chip lectin microarray based glycomic approach is employed to identify glyco markers for different gastritis and gastric cancer. Changes in protein glycosylation have impact on biological function and carcinogenesis. These altered glycosylation patterns in serum proteins and membrane proteins of tumor cells can be unique markers of cancer progression and hence have been exploited to diagnose various stages of cancer through lectin microarray technology. In the present work, we aimed to study the alteration of glycan structure itself in different stages of gastritis and gastric cancer thoroughly. In order to perform the study from both serum and tissue glycoproteins in an efficient and high-throughput manner, we indigenously developed and employed lectin microarray integrated on a microfluidic lab-on-a-chip platform. We analyzed serum and gastric biopsy samples from 8 normal, 15 chronic Type-B gastritis, 10 chronic Type-C gastritis, and 6 gastric adenocarcinoma patients and found that the glycoprofile obtained from tissue samples was more distinctive than that of the sera samples. We were able to establish signature glycoprofile for the three disease groups, that were absent in healthy normal individuals. In addition, our findings elucidated certain novel signature glycan expression in chronic gastritis and gastric cancer. In silico analysis showed that glycoprofile of chronic gastritis and gastric adenocarcinoma formed close clusters, confirming the previously hypothesized linkage between them. This signature can be explored further as gastric cancer marker to develop novel analytical tools and obtain in-depth understanding of the disease prognosis.
| S-EPMC4048441 | biostudies-other
Project description:In plants, sex determination is a comprehensive process of correlated events, which involves genes that are differentially and/or specifically expressed in distinct developmental phases. Exploring gene expression profiles from different sex types will contribute to fully understanding sex determination in plants. In this study, we conducted RNA-sequencing of female and male buds (FB and MB) as well as ovulate strobilus and staminate strobilus (OS and SS) of Ginkgo biloba to gain insights into the genes potentially related to sex determination in this species. Approximately 60 Gb of clean reads were obtained from eight cDNA libraries. De novo assembly of the clean reads generated 108,307 unigenes with an average length of 796 bp. Among these unigenes, 51,953 (47.97%) had at least one significant match with a gene sequence in the public databases searched. A total of 4709 and 9802 differentially expressed genes (DEGs) were identified in MB vs. FB and SS vs. OS, respectively. Genes involved in plant hormone signal and transduction as well as those encoding DNA methyltransferase were found to be differentially expressed between different sex types. Their potential roles in sex determination of G. biloba were discussed. Pistil-related genes were expressed in male buds while anther-specific genes were identified in female buds, suggesting that dioecism in G. biloba was resulted from the selective arrest of reproductive primordia. High correlation of expression level was found between the RNA-Seq and quantitative real-time PCR results. The transcriptome resources that we generated allowed us to characterize gene expression profiles and examine differential expression profiles, which provided foundations for identifying functional genes associated with sex determination in G. biloba.
| S-EPMC4910463 | biostudies-literature
Project description:Analysis of table olives microbiome using next-generation sequencing has enriched the available information about the microbial community composition of this popular fermented food. In this study, 16S and 18S rRNA sequencing was performed on table olives of five Greek popular cultivars, Halkidikis, Thassou, Kalamon, Amfissis, and Konservolia, fermented either by Greek style (in brine or salt-drying) or by Spanish style, in order to evaluate their microbial communities. Moreover, analytical methods were used to evaluate their biochemical properties. The prevailing bacterial species of all olives belonged to Lactobacillaceae, Leuconostocaceae, and Erwiniaceae families, while the most abundant yeasts were of the Pichiaceae family. Principal coordinates analysis showed a clustering of samples cured by salt-drying and of samples stored in brine, regardless of their cultivar. The biochemical evaluation of total phenol content, antioxidant activity, hydroxytyrosol, oleuropein, oleocanthal, and oleacein showed that salt-dried olives had low amounts of hydroxytyrosol, while Spanish-style green olives had the highest amounts of oleocanthal. All the other values exhibited various patterns, implying that more than one factor affects the biochemical identity of the final product. The protocols applied in this study can provide useful insights for the final product, both for the producers and the consumers.
| S-EPMC10094447 | biostudies-literature
Project description:The interactions of biological macromolecules with nanoparticles underlie a wide variety of current and future applications in the fields of biotechnology, medicine and bioremediation. The same interactions are also responsible for mediating potential biohazards of nanomaterials. Some applications require that proteins adsorb to the nanomaterial and that the protein resists or undergoes structural rearrangements. This article presents a screening method for detecting nanoparticle-protein partners and conformational changes on time scales ranging from milliseconds to days. Mobile fluorophores are used as reporters to study the interaction between proteins and nanoparticles in a high-throughput manner in multi-well format. Furthermore, the screening method may reveal changes in colloidal stability of nanomaterials depending on the physicochemical conditions.
| S-EPMC4551901 | biostudies-literature
Project description:'ReMoDiscovery' is an intuitive algorithm to correlate regulatory programs with regulators and corresponding motifs to a set of co-expressed genes. It exploits in a concurrent way three independent data sources: ChIP-chip data, motif information and gene expression profiles. When compared to published module discovery algorithms, ReMoDiscovery is fast and easily tunable. We evaluated our method on yeast data, where it was shown to generate biologically meaningful findings and allowed the prediction of potential novel roles of transcriptional regulators.
| S-EPMC1779513 | biostudies-literature