m5C Profiling

5-methylcytosine (m5C) profiling is a next generation sequencing (NGS)-based method to comprehensively detect m5C, one type of the most common and abundant methylation modifications in transcriptome. Our experienced technical team provides you with multiple m5C detection services, including hMeRIP-seq, m5C-RIP-seq, miCLIP-m5C-seq and RNA BS-seq. Meantime, we provide professional in-depth data analysis to meet customer needs.

Overview

Interest in RNA modification has grown rapidly in recent years, including m5C. 5mC is one of the most common and abundant methylation modifications in the transcriptome, and is highly prevalent in mRNAs as well as in some non-coding RNAs. It is concentrated in CG-rich regions and regions immediately downstream of translation initiation sites and has conversed, and has the characteristics of conservation, tissue-specific and dynamic features across mammalian transcriptomes. Our provides multiple m5C detection services, including hMeRIP-seq, m5C-RIP-seq, miCLIP-m5C-seq and RNA BS-seq. hMeRIP -seq is a method that combines immunoprecipitation with NGS technology to quickly and efficiently detect genomic m5C methylation. m5C-RIP-seq is a method that uses m5C-specific antibodies to enrich m5C-modified mRNAs from total mRNAs. But neither method can achieve single-base resolution. miCLIP-m5C is a technology that cross-links mRNA with m5C antibody and uses single-nucleotide resolution to accurately locate m5C in the transcriptome. RNA BS-seq is a technology that uses sodium bisulfite to convert un-methylated cytosine into uracil. The method can also achieve single-base resolution and has become the mainstream method to detect methylated cytosine. We provide multiple analysis strategies for customers to choose, which can comprehensively analyze the distribution and changes of m5C in the transcriptome, help researchers deeply understand the role of RNA modification in life and disease, and promote accurate diagnosis and treatment of diseases.

Features

Application High Efficiency Bioinformatics Analysis Flexibility
This method can profile and quantify m5C in small-RNA species and hm5C. Adopts carefully optimized experimental procedure, achieving high efficiency and specificity. Our integrated bioinformatics pipeline can be tailored to suit your project. One-stop solution from sample QC, library construction, to sequencing and data analysis.

Project Workflow

Sample Preparation

1. Sample Preparation

RNA purification; quality assessment and quantification.

Library Preparation

2. Library Preparation

Poly(A) tail RNA selection; m5C library preparation.

Sequencing

3. Sequencing

Illumina HiSeq; PE 50/75/100/150.

Data Analysis

4. Data Analysis

Visualize and preprocess results, and perform custom bioinformatics analysis.

Bioinformatics Analysis Pipeline

m5C Profiling

In-depth data analysis:

  • Statistics of m5C distribution
  • mC calling on whole genome
  • Peaks annotation
  • Transcriptome-wide profiling of m5C methylation
  • Differential binding analysis
  • Motif identification
  • GO&KEGG enrichment analysis of different peaks
  • Identify m5C and hm5C in small RNA

Sample Requirements

Please make sure that the RNA is not significantly degraded.

Sample storage: RNA can be dissolved in ethanol or RNA-free ultra-pure water and stored at -80°C. RNA should avoid repeated freezing and thawing.

Shipping Method: When shipping RNA samples, the RNA sample is stored in a 1.5 mL Eppendorf tube, sealed with sealing film. Shipments are generally recommended to contain 5-10 pounds of dry ice per 24 hours.

Deliverable: FastQ, BAM, coverage summary, QC report, custom bioinformatics analysis.

References:

  1. Seki M, Nishimura R, Yoshida K, et al. Integrated genetic and epigenetic analysis defines novel molecular subgroups in rhabdomyosarcoma. Nat Commun, 2015, 6: 7557.
  2. Lee H Y, An J H, Jung S E, et al. Genome-wide methylation profiling and a multiplex construction for the identification of body fluids using epigenetic markers. Forensic Sci Int Genet, 2015, 17: 17-24.
  3. Lin I H, Chen D T, Chang Y F, et al. Hierarchical clustering of breast cancer methylomes revealed differentially methylated and expressed breast cancer genes. PLoS One, 2015, 10: e0118453.
  4. Coit P, Yalavarthi S, Ognenovski M, et al. Epigenome profiling reveals significant DNA demethylation of interferon signature genes in lupus neutrophils. J Autoimmun, 2015, 58: 59-66.
* For Research Use Only. Not for use in diagnostic procedures.


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