Relevant research integration on MeRIP-Seq

With the development of new sequencing technologies, it is now possible to use methylated RNA immunoprecipitation sequencing technology (MeRIP-Seq) to perform unbiased analysis of the entire N6-methyl-adenosine (m(6)A) RNA methylation group. It is possible to detect differential methylation between two conditions of RNA status, for example, between normal tissue and cancer tissue. However, as an affinity-based method, MeRIP-Seq does not yet provide base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can actually contain multiple RNA methylation residues, Some of them can be regulated by different enzymes, thus differentially methylated under the two conditions.

Since the existing peak-based methods cannot effectively distinguish multiple methylated residues located within a single methylation site, the researchers proposed a hidden Markov model (HMM)-based method to solve this problem. Specifically, the detected RNA methylation sites are further divided into multiple adjacent cells. Then, a hidden Markov model is used to scan at a higher resolution to simulate the dependence between adjacent intervals in space to improve accuracy. The researchers tested the proposed algorithm on simulated data and real data. The results show that the proposed algorithm is significantly better than the existing peak-based methods on the simulation system, and the differentially methylated regions with higher statistical significance are detected on the real data set.

Although there has been widespread research on DNA/chromatin-related epigenetics, such as histone modification and DNA methylation, however, before the development of a new affinity-based sequencing method MeRIP-Seq and its application to the investigation of global mRNA N6-methyladenosine, RNA epigenetics did not attract the attention it deserves (m6A) in mammalian cells. As a combination of ChIP-Seq and RNA-Seq, MeRIP-Seq has the potential to study the distribution of various post-transcriptional RNA modifications within the transcriptome.

The research on MeRIP-Seq data is still at a very early stage, and the existing protocol is not optimized for the inherent characteristics of processing MeRIP-Seq data. The researchers provide a detailed and easy-to-use protocol that uses the exomePeak R/Bioconductor package and other software programs to analyze MeRIP-Seq data, including raw read alignment, RNA methylation site detection, motif discovery, and differential RNA Methylation analysis and functional analysis.

Post-transcriptional RNA modification occurs on all types of RNA and plays a vital role in regulating all aspects of RNA function. Due to the development of high-throughput sequencing technology, transcriptome-wide analysis of RNA modifications has become possible. With the accumulation of a large number of high-throughput data sets, bioinformatics methods are becoming more and more important for revealing the epitranscriptome.

IntegrateRNA uses methylated RNA immunoprecipitation sequencing (MeRIP-seq) to quickly, efficiently and accurately identify m6A modification sites in mRNA transcripts and other RNAs. The combination of co-immunoprecipitation and high-throughput sequencing can systematically study m6A peaks and m6A motifs in the transcriptome. IntegrateRNA uses highly m6A-specific antibodies to immunoprecipitate methylated RNA fragments from randomly fragmented transcriptomes, and then performs NGS. First, the purified RNA is chemically fragmented into oligonucleotides of approximately 100 nt length, and immunoprecipitation is performed using m6A-specific antibodies. Use random hexamer primers to convert the eluted m6A-containing fragments (IP) and unprocessed input control fragments into cDNA, and then perform adapter ligation and high-throughput sequencing.

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