Predicting AID off-targets: A step forward.

Published in: The Journal of experimental medicine (2018)

In this issue of JEM, Álvarez-Prado et al. (https://doi.org/10.1084/jem.20171738) designed a DNA capture library allowing them to identify 275 genes targeted by AID in mouse germinal center B cells. Using the molecular features of these genes to feed a machine-learning algorithm, they determined that high-density RNA PolII and Spt5 binding-found in 2.3% of the genes-are the best predictors of AID specificity.

Keyphrases

machine learning
high density
genome wide
bioinformatics analysis
genome wide identification
deep learning
genome wide analysis
dna methylation
cancer therapy
gene expression
dna binding
single molecule