
Abstract RNA modifications, especially methylation of the N6 position of adenosine (A)—m6A, represent an emerging research frontier in RNA biology. With the rapid development of high-throughput sequencing technology, in-depth study of m6A distribution and function relevance becomes feasible. However, a robust method to effectively identify m6A-modified regions has not been available yet. Here, we present a novel high-efficiency and user-friendly analysis pipeline called MeRIP-PF for the signal identification of MeRIP-Seq data in reference to controls. MeRIP-PF provides a statistical P-value for each identified m6A region based on the difference of read distribution when compared to the controls and also calculates false discovery rate (FDR) as a cut off to differentiate reliable m6A regions from the background. Furthermore, MeRIP-PF also achieves gene annotation of m6A signals or peaks and produce outputs in both XLS and graphical format, which are useful for further study. MeRIP-PF is implemented in Perl and is freely available at http://software.big.ac.cn/MeRIP-PF.html.
Adenosine, Sequence Analysis, RNA, MeRIP-Seq, Molecular Sequence Annotation, m6A, RNA modification, Biochemistry, Methylation, Computational Mathematics, Mice, Application Note, Genetics, Animals, RNA, RNA, Messenger, Peak finding, Molecular Biology, Software
Adenosine, Sequence Analysis, RNA, MeRIP-Seq, Molecular Sequence Annotation, m6A, RNA modification, Biochemistry, Methylation, Computational Mathematics, Mice, Application Note, Genetics, Animals, RNA, RNA, Messenger, Peak finding, Molecular Biology, Software
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 19 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
