Introduction to RIP-Sequencing
Protein binding is involved in every step of the transcript life cycle, from synthesis (polymerases) to degradation (nucleases). A subset of these transcripts is involved in other critical processes such as epigenetic regulation and genome protection through transposon silencing, in addition to protein synthesis. The majority of research has focused on transcriptomics profiling. However, it is thought that mRNA levels do not always necessarily equate with steady-state protein levels. As the importance of RNA processing and translational events that occur post-transcriptionally becomes more apparent, interest in recognizing the RNAs linked with RNA binding protein (RBP) in a cellular context is growing.
Within RNA-protein complexes, RIP-Seq maps the sites where proteins are obligated to the RNA. The purification of RNA–protein interactions in native situations utilizing a protein-specific antibody to plot the RBP of concern is referred to as RNA immunoprecipitation (RIP With the advent of sequencing innovations and multiple RIP chemistries, thousands of bound transcripts (mRNAs, non-coding RNAs, or viral RNAs) can now be detected simultaneously in a single research.
Instead of using a microarray to analyze the RNAs that were taken down, RIP-Seq uses high-throughput sequencing to sequence the RNAs that were taken down. The RNA immunoprecipitation procedure was integrated with RNA sequencing in the studies. They immunoprecipitated nuclear RNA separated from mouse ES cells using specific antibodies (-Ezh2), and then sequenced the taken-down RNA using the Illumina next-generation sequencing framework. To recognize RNAs bound by an RBP of concern, RIP can be used in conjunction with microarray (RIP-chip) or sequencing (RIP-seq The specificity of the RBP-RNA interaction in cell extract is used in both RIP-chip and RIP-seq to maintain the interaction long enough to segregate via immunoprecipitation.
Applications of RIP-Seq
RIP-Seq assessment can be utilized to:
- Define the RNA-RNA binding protein interaction network across the entire transcriptome.
- Study of the non-coding RNA's functional mechanism.
- Research into the interaction of RBP and noncoding RNAs like miRNA and lncRNA.
- Find new binding sites for proteins.
RIP-Seq Data Analysis
Overview: Peak calling in the transcriptome range, annotating the genome's peak location data and the sequence information of the peak area, and obtaining the appropriate genes of the peak based on the annotation data are all required steps in RIP-Seq data analysis. Finally, the appropriate genes are subjected to differential assessment.
Detailed Steps: The following are the six main stages in the RIP-Sequencing data assessment: (1) data pre-processing, (2) genome mapping, (3) peak calling and annotation, (4) motif analysis, (5) differential peak analysis, and (6) result visualization.
Data quality control, data statistics, and the removal of adapter and low-quality reads are all part of the data pre-processing Mapping to the reference genome, on the other hand, takes into account the comparison rate, unique comparison rate, sequencing depth, and chromosome distribution reads. Peak-related gene annotation, peak distribution on gene functional elements, and peak-related gene function enrichment analysis are all stages in the peak calling and annotation process. The motif analysis is done on the entire transcript of genes that are related to the peak. Difference peak analysis, difference peak gene annotation, GO enrichment analysis of related genes, and KEGG pathway enrichment analysis of related genes are all part of the differential peak analysis. The visual display of the analysis findings is the final process.
References
- Zeng Y, Wang S, Gao S, et al. Refined RIP-seq protocol for epitranscriptome analysis with low input materials. PLoS biology. 2018, 16(9).
- Li Y, Zhao DY, Greenblatt JF, Zhang Z. RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments. Nucleic acids research. 2013, 41(8).
- Kucukural A, Özadam H, Singh G, et al. ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq. Bioinformatics. 2013, 29(19).
