Login / Signup

Genome-wide systematic identification of methyltransferase recognition and modification patterns.

Torbjørn Ølshøj JensenChristian Tellgren-RothStephanie RedlJérôme MaurySimo Abdessamad Baallal JacobsenLasse Ebdrup PedersenAlex Toftgaard Nielsen
Published in: Nature communications (2019)
Genome-wide analysis of DNA methylation patterns using single molecule real-time DNA sequencing has boosted the number of publicly available methylomes. However, there is a lack of tools coupling methylation patterns and the corresponding methyltransferase genes. Here we demonstrate a high-throughput method for coupling methyltransferases with their respective motifs, using automated cloning and analysing the methyltransferases in vectors carrying a strain-specific cassette containing all potential target sites. To validate the method, we analyse the genomes of the thermophile Moorella thermoacetica and the mesophile Acetobacterium woodii, two acetogenic bacteria having substantially modified genomes with 12 methylation motifs and a total of 23 methyltransferase genes. Using our method, we characterize the 23 methyltransferases, assign motifs to the respective enzymes and verify activity for 11 of the 12 motifs.
Keyphrases
  • genome wide
  • dna methylation
  • single molecule
  • high throughput
  • genome wide analysis
  • copy number
  • gene expression
  • single cell
  • living cells
  • room temperature
  • machine learning
  • deep learning
  • transcription factor