Exome-based Analysis for RNA Epigenome Sequencing Data

 

ExomePeak was originally a MATLAB toolbox distributed under the free GNU GPL license for affinity-based RNA epigenome sequencing data obtained from MeRIP-seq [1] or m6A-seq [2] . It is now also available as an R-package to achieve the complete open source capacity. The MATLAB-based exomePeak package [3] is no longer actively maintained.

The salient features of exomePeak R-package include:

  1. Exome-based peak detection, which is more suitable for RNA epigenetic regulation analysis. (Please see the figure on the right for a comparison between genome-based and transcriptome-based peak calling.)
  2. Statistically supports biological replicates, and reports consistent peaks which must appear on all biological replicates
  3. Internally removes PCR artifacts and multimapping reads, takes directly the output of spliced aligner, and pre-processing is no longer necessary.
  4. The binding sites are reported in BED format, which is compatible with most genome browser for easy visualization and manipulation
  5. Comparing two experimental conditions to unveil the dynamics in RNA epigenetic regulations

 

http://upload.wikimedia.org/wikipedia/commons/thumb/f/fc/ExomePeak_peak_calling.tiff/lossy-page1-500px-ExomePeak_peak_calling.tiff.jpg

http://bits.wikimedia.org/static-1.22wmf12/skins/common/images/magnify-clip.png

ExomePeak detects a single peak that spans 2 junctions, compared with the genome-based method MACS2 that reports 3 non-adjacent peaks, all of which cover some intronic regions.

 

Availability

The exomePeak R-package is now available from bioconductor and can be installed with the following R commands:

# Install Rsamtools and exomePeak package

source("http://bioconductor.org/biocLite.R")

biocLite("exomePeak")

 

Examples

The usage of exomePeak package is very straightforward. It takes directly the ouput of spliced aligner such as Tophat, and outputs exome-based peaks or binding sites in BED format.

 

# For peak detection:

exomepeak(GENE_ANNO_GTF = gtf, IP_BAM = ip, INPUT_BAM = input)

where, gtf, ip and input are the gene annotation GTF file, IP bam files and input control bam files, respectively.

 

# for peak calling and comparing two experimental conditions

exomepeak(GENE_ANNO_GTF = gtf, IP_BAM = ip, INPUT_BAM = input,

        TREATED_IP_BAM = treated_ip, TREATED_INPUT_BAM = treated_input)

# where, gtf, ip and input are the gene annotation GTF file, IP bam files and input control bam files, respectively. And treated_ip and treated_input are the IP and control bam files from a differential condition.

 

User support

User support is available from Google group: "RNA epigenetics and Post-transcriptional RNA modifications"

The exome R-package is under active development, we welcome any suggestions or questions. Please feel free to contact Dr. Jia Meng (Email: jia.meng@hotmail.com) for any questions.

 

References

1.      Meyer, Kate D.; Saletore, Yogesh; Zumbo, Paul; Elemento, Olivier; Mason, Christopher E.; Jaffrey, Samie R. (31 May 2012). "Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3 UTRs and near Stop Codons". Cell 149 (7): 1635 1646.doi:10.1016/j.cell.2012.05.003.

2.      Dominissini, Dan; Moshitch-Moshkovitz, Sharon; Schwartz, Schraga; Salmon-Divon, Mali; Ungar, Lior; Osenberg, Sivan; Cesarkas, Karen; Jacob-Hirsch, Jasmine; Amariglio, Ninette; Kupiec, Martin; Sorek, Rotem; Rechavi, Gideon (28 April 2012). "Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq". Nature 485 (7397): 201 206. doi:10.1038/nature11112.

3.     Meng, J.; Cui, X.; Rao, M. K.; Chen, Y.; Huang, Y. (14 April 2013). "Exome-based analysis for RNA epigenome sequencing data". Bioinformatics 29 (12): 1565 1567. doi:10.1093/bioinformatics/btt171