As a newly emerged research area, RNA epigenetics has drawn increasing
attention recently for the participation of RNA methylation and other
modifications in a number of crucial biological processes. Thanks to high
throughput sequencing techniques, such as m6A-Seq, transcriptome-wide RNA
methylation profile is now available in the form of count-based data, with
which it is often of interests to study the dynamics in epitranscriptomic
layer. However, the sample size of RNA methylation experiment is usually
very small due to its costs; and additionally, there usually exist a large
number of genes whose methylation level cannot be accurately estimated due
to their low expression level, making differential RNA methylation analysis
a difficult task.
We present QNB, a statistical approach for differential RNA methylation
analysis with count-based small-sample sequencing data. The method is based
on 4 independent negative binomial distributions with their variances and
means linked by local regressions. QNB showed improved performance on
simulated and real m6A-Seq datasets when compared with competing algorithms.
And the QNB model is also applicable to other datasets related RNA
modifications, including but not limited to RNA bisulfite sequencing,
m1A-Seq, Par-CLIP, RIP-Seq, etc.Please don't hesitate to contact
|Author||Lian Liu <[email protected]>|
|Date of publication||2017-09-16 03:24:00 UTC|
|Maintainer||Lian Liu <[email protected]>|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.