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Cross-Linking and Immunoprecipitation Followed by Deep Sequencing

Cross-linking and immunoprecipitation followed by deep sequencing (CLIP-seq) is a powerful technique used to study protein-RNA interactions in living cells.
This method combines UV-induced cross-linking, immunoprecipitation, and high-throughput sequencing to identify the specific RNA binding sites of RNA-binding proteins (RBPs) with high resolution.
CLIP-seq allows researchers to map the precise binding locations of RBPs on their target RNAs, providing insights into the regulatory mechanisms that govern gene expression and post-transcriptional processing.
This technique has been widely applied to investigate the roles of RBPs in various biological processes, such as mRNA splicing, stability, localization, and translation.
PubCompare.ai, an AI-driven platform, can help optimize CLIP-seq workflows by assisting researchers in identifying the most reliable and effective protocols from the literature, preprints, and patents, thus enhancing reproducibility and research accurracy.

Most cited protocols related to «Cross-Linking and Immunoprecipitation Followed by Deep Sequencing»

1
Comprehensive miRNA Functional Analysis with DIANA-miRPath v3.0
DIANA-miRPath v3.0 database has been extended to support features such as microRNA nomenclature history (18 ), a novel miRNA/gene name suggestion mechanism, as well as analysis support for seven species (H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, G. gallus and D. rerio). The new database schema incorporates KEGG pathways, as well as GO and GOSlim annotations, enabling functional annotation of miRNAs and miRNA combinations using all datasets, or their subsets (GO cellular component, biological processes or molecular function). Gene and miRNA annotations are derived from Ensembl (19 (link)) and miRBase (20 (link)), respectively. Single nucleotide polymorphism locations and pathogenicity are derived from dbSNP (21 (link)).
miRNA:gene interactions are derived from the in silico miRNA target prediction algorithms: DIANA-microT-CDS and TargetScan 6.2, the latter in both Context+ and Conservation modes. DIANA-microT-CDS is the fifth version of the microT algorithm (3 (link)). It is a highly accurate target prediction algorithm trained against CLIP-Seq datasets, enabling target prediction in 3′ UTR and CDS mRNA regions. The user of DIANA-miRPath v3.0 can also utilize experimentally supported interactions from DIANA-TarBase v.7.0. TarBase v7.0 incorporates more than half a million experimentally supported miRNA:gene interactions derived from hundreds of publications and more than 150 CLIP-Seq libraries (17 (link)). The number of indexed interactions is 9–250-fold higher compared to any other manually curated database. The user of miRPath v3.0 can harness this wealth of information and substitute or combine in silico predicted targets with high quality experimentally validated interactions. Currently, this functionality is supported for H. sapiens and M. musculus and C. elegans, since most relevant wet-lab experiments correspond to these species. As more experimental data become available for other organisms in DIANA-TarBase, the experimentally supported functional analysis module will be further extended.
Vlachos I.S., Zagganas K., Paraskevopoulou M.D., Georgakilas G., Karagkouni D., Vergoulis T., Dalamagas T, & Hatzigeorgiou A.G. (2015). DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Research, 43(Web Server issue), W460-W466.
Publication 2015
Biological Processes Caenorhabditis elegans Cellular Structures Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Drosophila melanogaster Genes MicroRNAs Muscle Tissue Pathogenicity RNA, Messenger Single Nucleotide Polymorphism Zebrafish
2
Quantitative miRNA Target Site Identification and Repression Modeling
AGO2–miRNA complexes were generated by adding synthetic miRNA duplexes to lysate from cells that over-expressed recombinant AGO2, and then these complexes were purified on the basis of affinity to the miRNA seed. RNA libraries were generated by in vitro transcription of synthetic DNA templates. For AGO-RBNS, purified AGO2–miRNA complex was incubated with a large excess of library molecules, and after reaching binding equilibrium, library molecules bound to AGO2–miRNA complex were isolated and prepared for high-throughput sequencing. Examination of k-mers enriched within the bound library sequences identified miRNA target sites, and relative KD values for each of these sites were simultaneously determined by maximum likelihood estimation, fitting to AGO-RBNS results obtained over a 100-fold range in AGO2–miRNA concentration.
Intracellular miRNA-mediated repression was measured by performing RNA-seq on HeLa cells that had been transfected with a synthetic miRNA duplex. For sites that were sufficiently abundant in endogenous 3′ UTRs, efficacy was measured on the basis of their influence on levels of endogenous mRNAs of HeLa cells. Site efficacy was also evaluated using massively parallel reporter assays, which provided information for the rare sites as well as the more abundant ones. The biochemical and biochemical+ models of miRNA-mediated repression were constructed and fit using the measured KD values and the repression of endogenous mRNAs was observed after transfecting miRNAs into HeLa cells. The CNN was built using TensorFlow, trained using the measured KD values and the repression observed in the HeLa transfection experiments, and tested on the repression of endogenous mRNAs observed after transfecting miRNAs into HEK293T cells. Results were also tested on external datasets examining either intracellular binding of miRNAs by CLIP-seq or repression of endogenous mRNAs after miRNAs had been transfected, knocked down, or knocked out. The details of each of these methods are described in the supplementary materials.
McGeary S.E., Lin K.S., Shi C.Y., Pham T.M., Bisaria N., Kelley G.M, & Bartel D.P. (2019). The biochemical basis of microRNA targeting efficacy. Science (New York, N.Y.), 366(6472), eaav1741.
Publication 2019
Biological Assay cDNA Library Cells Cross-Linking and Immunoprecipitation Followed by Deep Sequencing EIF2C2 protein, human GPER protein, human HeLa Cells MicroRNAs Protoplasm Repression, Psychology RNA, Messenger RNA-Seq Transcription, Genetic Transfection Untranslated Regions
3
Curated CLIP-Seq Data Analysis
In cases where raw high-throughput data were available, curators obtained the relevant data sets, commented each library with extensive metadata and marked them for reanalysis in order to maintain optimal quality standards for all identified interactions. Specifically, data available from online repositories or supplemental materials of 1 CLASH, 31 PAR-CLIP and 122 HITS-CLIP libraries were analyzed and included in the database.
The CLIP-Seq analysis has been performed using an in-house developed pipeline. Regions formed by at least five overlapping reads were included to the analysis. For PAR-CLIP data, peaks containing adequate T-to-C (sense strand) or A-to-G (antisense strand) incorporation were selected. At least two transitions in the same position for peaks with less than 50 reads were required, while for the remaining regions we applied the threshold of >5%, as indicated by Hafner et al. (11 (link)). For all CLIP-Seq data sets having replicates, a peak had to be present in at least two replicates in order to be considered as valid. Where available, top expressed miRNAs were retrieved from the original publication. In all other instances, we analyzed publically available small-RNA-Seq libraries derived from the relevant cell lines. miRNA:gene interactions were inferred using a CLIP-peak-guided MRE search algorithm considering the miRNA:mRNA binding type, binding free energy, MRE conservation and AU flanking content.
Changes over 50% were utilized as a threshold for microarray and biotin pull-down experiments. In cases where replicates were available, an interaction had to be present in at least two replicates, in order to be included to the database.
Vlachos I.S., Paraskevopoulou M.D., Karagkouni D., Georgakilas G., Vergoulis T., Kanellos I., Anastasopoulos I.L., Maniou S., Karathanou K., Kalfakakou D., Fevgas A., Dalamagas T, & Hatzigeorgiou A.G. (2014). DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Research, 43(Database issue), D153-D159.
Publication 2014
Biotin cDNA Library Cell Lines Clip Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Genes High-Throughput Sequencing of RNA Isolated by Crosslinking Immunoprecipitation Lanugo Microarray Analysis MicroRNAs RNA, Messenger RNA-Seq
4
Comprehensive miRNA-Target Interaction Database
DIANA-TarBase v8.0 caters more than one million entries, corresponding to the largest compilation of experimentally supported miRNA targets. This collection of miRNA–gene interactions has been derived from experiments employing >33 distinct low-yield and high-throughput techniques, spanning 85 tissues, 516 cell types and ∼451 experimental conditions from 18 species (Figure 1A). Approximately 1200 publications were manually curated and >350 high-throughput datasets have been analyzed. The new database version incorporates an assortment of positive and negative direct miRNA interactions. It comprises >10 000 interactions derived from specific techniques. Approximately 5100 of these miRNA targets are verified by reporter gene assays, extracted from ∼950 publications, providing a 1.6-fold increase compared to relevant entries in TarBase v7.0. More than 14 000 direct miRNA–mRNA chimeric fragments defined from CLASH and CLEAR-CLIP experiments, as well as from a previous meta-analysis of published AGO-CLIP datasets (25 (link)), have been integrated to the repository. Approximately 90 000 new entries were generated from the analysis of additional AGO CLIP-seq libraries from three studies. More than 233 000 interactions have been extracted from miRNA-specific transfection/knockdown microarray, RPF-seq, RIP-seq and RNA-seq experiments which were performed in 28 tissues and 82 cell types under 206 experimental conditions. Updated entries derived from the aforementioned methodologies are summarized in Figure 1B.
Karagkouni D., Paraskevopoulou M.D., Chatzopoulos S., Vlachos I.S., Tastsoglou S., Kanellos I., Papadimitriou D., Kavakiotis I., Maniou S., Skoufos G., Vergoulis T., Dalamagas T, & Hatzigeorgiou A.G. (2017). DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Research, 46(Database issue), D239-D245.
Publication 2017
Biological Assay Cells Chimera Clip Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Genes Genes, Reporter Microarray Analysis MicroRNAs RNA, Messenger RNA-Seq Tissues Training Programs Transfection
5
Comparative miRNA Target Prediction
To compare predictions from different miRNA target prediction tools, we collected the following freely downloadable predictions: AnTar (predictions from either miRNA-transfection or CLIP-seq models) (Wen et al., 2011 (link)), DIANA-microT-CDS (September 2013) (Reczko et al., 2012 (link)), ElMMo v5 (January 2011) (Gaidatzis et al., 2007 (link)), MBSTAR (all predictions) (Bandyopadhyay et al., 2015 (link)), miRanda-MicroCosm v5 (Griffiths-Jones et al., 2008 (link)), miRmap v1.1 (September 2013) (Vejnar and Zdobnov, 2012 (link)), mirSVR (August 2010) (Betel et al., 2010 (link)), miRTarget2 (from miRDB v4.0, January 2012) (Wang, 2008 (link); Wang and El Naqa, 2008 (link)), MIRZA-G (sets predicted either with or without conservation features and either with or without more stringent seed-match requirements, March 2015) (Gumienny and Zavolan, 2015 (link)), PACCMIT-CDS (sets predicted either with or without conservation features) (Marin et al., 2013 (link)), PicTar2 (from the doRiNA web resource; sets conserved to either fish, chicken, or mammals) (Krek et al., 2005 (link); Anders et al., 2012 (link)), PITA Catalog v6 (3/15 flank for either ‘All’ or ‘Top’ predictions, August 2008) (Kertesz et al., 2007 (link)), RNA22 (May 2011) (Miranda et al., 2006 (link)), SVMicrO (February 2011) (Liu et al., 2010 (link)), TargetRank (all scores from web server) (Nielsen et al., 2007 (link)), TargetSpy (all predictions) (Sturm et al., 2010 ), TargetScan v5.2 (either conserved or all predictions, June 2011) (Grimson et al., 2007 (link)), and TargetScan v6.2 (either conserved predictions ranked by the context+ model or all predictions ranked by either the context+ model or PCT scores, June 2012) (Friedman et al., 2009 (link); Garcia et al., 2011 (link)). For algorithms providing site-level predictions (i.e., ElMMo, MBSTAR, miRSVR, PITA, and RNA22), scores were summed within genes or transcripts (if available) to acquire an aggregate score. For algorithms providing multiple transcript-level predictions (i.e., miRanda-MicroCosm, PACCMIT-CDS, and TargetSpy), the transcript with the best score was selected as the representative transcript isoform. In all cases, predictions with gene symbol or Ensembl ID formats were translated into RefSeq format. When computing r2 to the test sets, mRNAs that were not predicted by the algorithm to be a target were assigned the worst score in the range of all scores generated by the algorithm.
Agarwal V., Bell G.W., Nam J.W, & Bartel D.P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4, e05005.
Publication 2015
Chickens Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Fishes Genes Mammals MicroRNAs Mirza Protein Isoforms RNA, Messenger Transfection

Most recents protocols related to «Cross-Linking and Immunoprecipitation Followed by Deep Sequencing»

1
Regulatory Network Analysis of lncRNA-miRNA-mRNA
DELs (targeted studies) and DEMs (miRNAs associated with hub molecules among the top pathway and the targets reported at both mRNA and protein levels) were used to screen the experimentally validated interaction between them by DIANA-LncBase v3 (https://diana.e-ce.uth.gr/lncbasev3) (Karagkouni et al., 2020 (link)). Both the subunits of miRNA, i.e., “-3p” and “-5p” were considered for finding associated lncRNAs in those cases where the subunits were not specified. Then, lncRNA-miRNA and miRNA-mRNA co-expression pairs (positive relation) were then used to construct ceRNA interaction networks (lncRNA-miRNA-mRNA). The networks were visualized using Cytoscape. Another database, mirTarBase (mirtarbase.cuhk.edu.cn), containing more than three hundred and sixty thousand miRNA-mRNA interactions was then used to categorize the miRNA-mRNA interactions with strong evidence (Reporter assay/Western blot/qPCR) or less strong evidence (Microarray, NGS, pSILAC, CLIP-Seq and others).
Saklani N., Chauhan V., Akhtar J., Upadhyay S.K., Sirdeshmukh R, & Gautam P. (2023). In silico analysis to identify novel ceRNA regulatory axes associated with gallbladder cancer. Frontiers in Genetics, 14, 1107614.
Publication 2023
Biological Assay Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Microarray Analysis MicroRNAs Proteins Protein Subunits RNA, Long Untranslated RNA, Messenger Western Blotting
2
Regulatory Motifs and Binding Sites in Vav3 mRNA
The identification of the AREs motifs in the 3′UTR region of Vav3 mRNA was retrieved from the public database AREsite2 (31 (link)) (http://rna.tbi.univie.ac.at/AREsite). The RBP binding sites on the 3′UTR region of Vav3 mRNA were identified by CLIP-Seq technology analyzed by the peak calling method Piranha and were collected from the POSTAR3 database (33 (link)) (http://postar.ncrnalab.org).
Badaoui M., Sobolewski C., Luscher A., Bacchetta M., Köhler T., van Delden C., Foti M, & Chanson M. (2023). Targeting HuR-Vav3 mRNA interaction prevents Pseudomonas aeruginosa adhesion to the cystic fibrosis airway epithelium. JCI Insight, 8(3), e161961.
Publication 2023
Binding Sites Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Piranhas RNA, Messenger VAV3 protein, human
3
Identifying 7SK Binding Proteins via CLIP-seq
We filtered the GENCODE transcript annotation data set for all 7SK annotated transcripts to enable the identification of known and novel 7SK binding proteins via observed CLIP-seq signals (eCLIP-seq or POSTAR-derived binding sites) on corresponding transcripts and assess their predictive value in the context of transcriptional pausing. In particular, 7SK transcripts which were labeled as pseudo versions were included if they were expressed at least at the median expression level of all expressed non-coding transcripts. Their inclusion was motivated by the idea that factors that also bind these pseudo 7SK transcripts may compete (55 (link)) for respective binding sites with factors that bind the non-pseudo version. The set of 7SK binding factors was defined for each cell line as all factors with at least one CLIP binding site on any of the 7SK transcripts (see Supplementary Tables S12 - S14).
Akcan T.S., Vilov S, & Heinig M. (2023). Predictive model of transcriptional elongation control identifies trans regulatory factors from chromatin signatures. Nucleic Acids Research, 51(4), 1608-1624.
Publication 2023
Binding Proteins Binding Sites Cell Lines Clip Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Transcription, Genetic
4
MiR-155-5p Targets Cell Cycle Genes
Eight miRNAs (miR-9-5p, miR-125b-5p, miR-34a-5p, miR-184, miR-155-5p, miR-3131, miR-4497, and miR-4491) were screened for verification [9 (link)–11 (link)]. Analysis using multiple tools (miRPathDB, https://mpd.bioinf.uni-sb.de/mirna.html?mirna=hsa-miR-155-5p&organism=hsa, hg19_CLIP-seq_miRNA, and miRTarBase) revealed that miR-155-5p could target the following cell cycle-related and proliferation-related genes: CDK2, CDK4, CCND1, and CCND2.
Chen Q., Lin H., Li S., Deng X, & Zhang J. (2023). Mini-αA Upregulates the miR-155-5p Target Gene CDK2 and Plays an Antiapoptotic Role in Retinal Pigment Epithelial Cells during Oxidative Stress. Journal of Ophthalmology, 2023, 6713094.
Publication 2023
CCND1 protein, human CCND2 protein, human CDK2 protein, human Cell Cycle Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Genes MicroRNAs MIRN9 microRNA, human
5
RNA-Binding Protein Classification Dataset
For each RBP, a classification dataset of bound (positive) and unbound (negative) RNA sequences was constructed. Positive samples were obtained by taking corresponding 400nt peak-region windows from the previous step, while two distinct sets of negative samples were generated. First, 400nt long regions which did not overlap with CLIP peaks of the given RBP were sampled from transcripts harboring at least one CLIP peak. This constraint ensures that the transcript is expressed in the experimental cell type and would not be observed as RBP-binding in other cell types. The second set of negative samples was generated by randomly sampling CLIP peaks of other RBPs. This ensures that any CLIP-seq biases (such as U-bias during UV-C cross-linking (38 (link),39 )) are present in both positive and negative samples and prevents the model from performing a biases-based sample discrimination during the training. Together, this yields a three-class training set, where class 1 corresponds to positive samples and class 2 and 3 correspond to negative samples. Samples of class 2 and 3 were sampled at a 3:1 ratio with respect to class 1. Finally, generated samples were randomly split into train, validation and test sets at a ratio of 70:15:15, respectively.
Horlacher M., Oleshko S., Hu Y., Ghanbari M., Cantini G., Schinke P., Vergara E.E., Bittner F., Mueller N.S., Ohler U., Moyon L, & Marsico A. (2023). A computational map of the human-SARS-CoV-2 protein–RNA interactome predicted at single-nucleotide resolution. NAR Genomics and Bioinformatics, 5(1), lqad010.
Publication 2023
Cells Clip Cross-Linking and Immunoprecipitation Followed by Deep Sequencing Discrimination, Psychology RNA Sequence

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More about "Cross-Linking and Immunoprecipitation Followed by Deep Sequencing"

Cross-Linking and Immunoprecipitation Followed by Deep Sequencing (CLIP-seq) is a powerful technique used to study protein-RNA interactions in living cells.
This method combines UV-induced cross-linking, immunoprecipitation, and high-throughput sequencing to identify the specific RNA binding sites of RNA-binding proteins (RBPs) with high resolution.
CLIP-seq allows researchers to map the precise binding locations of RBPs on their target RNAs, providing insights into the regulatory mechanisms that govern gene expression and post-transcriptional processing.
This technique has been widely applied to investigate the roles of RBPs in various biological processes, such as mRNA splicing, stability, localization, and translation.
CLIP-seq has become an increasingly popular tool in the field of transcriptomics, complementing other techniques like NextSeq, Dual-Luciferase Reporter Assay System, and M6A-seq.
To optimize CLIP-seq workflows, researchers can utilize AI-driven platforms like PubCompare.ai.
This tool assists in identifying the most reliable and effective protocols from the literature, preprints, and patents, enhancing reproducibility and research accuracy.
By incorporating insights from tools like the BD Accuri C6 instrument, Lipofectamine 3000, Ab5176, and Pd2EGFP-N1 vector, researchers can further refine their CLIP-seq experiments and gain deeper understanding of RNA-protein interactions.
Additionally, specialized techniques like M6A-CLIP-seq, which combines CLIP-seq with N6-methyladenosine (m6A) detection, have emerged to study the epitranscriptomic regulation of RBPs.
These advancements, along with the use of enzymes like T4 PNK, continue to expand the capabilities of CLIP-seq and its applications in various fields of biology.