Name Mode Size
R 040000
inst 040000
man 040000
tests 040000
vignettes 040000
DESCRIPTION 100644 1 kb
NAMESPACE 100644 1 kb
NEWS 100644 0 kb 100644 2 kb
# m6Aboost Author: You Zhou, Kathi Zarnack --- ## Introduction N6-methyladenosine (m6A) is the most abundant internal modification in mRNA. It impacts many different aspects of an mRNA's life, e.g. nuclear export, translation, stability, etc. m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation (miCLIP) and the improved **miCLIP2** are m6A antibody-based methods that allow the transcriptome-wide mapping of m6A sites at a single-nucleotide resolution. In brief, UV crosslinking of the m6A antibody to the modified RNA leads to truncation of reverse transcription or C-to-T transitions in the case of readthrough. However, due to the limited specificity and high cross-reactivity of the m6A antibodies, the miCLIP data comprise a high background signal, which hampers the reliable identification of m6A sites from the data. For accurately detecting m6A sites, we implemented an AdaBoost-based machine learning model (**m6Aboost**) for classifying the miCLIP2 peaks into m6A sites and background signals. The model was trained on high-confidence m6A sites that were obtained by comparing wildtype and _Mettl3_ knockout mouse embryonic stem cells (mESC) lacking the major methyltransferase Mettl3. For classification, the m6Aboost model uses a series of features, including the experimental miCLIP2 signal (truncation events and C-to-T transitions) as well as the transcript region (5'UTR, CDS, 3'UTR) and the nucleotide sequence in a 21-nt window around the miCLIP2 peak. The package [m6Aboost]( includes the trained model and the functionalities to prepare the data, extract the required features and predict the m6A sites. --- ## Citing m6Aboost > Körtel, Nadine#, Cornelia Rückle#, You Zhou#, Anke Busch, Peter Hoch-Kraft, FX Reymond Sutandy, Jacob Haase, et al. 2021. “Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.” bioRxiv. --- ## How to use it Documentation (vignette and user manual) is available at the **m6Aboost's** Bioconductor landing page at ---