De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing

By M. Stoiber, Joshua Quick, Rob Egan, Ji Eun Lee, S. Celniker, R. Neely, Nicholas J. Loman, Len A. Pennacchio, James B. Brown. bioRxiv, 2017

306 citations recorded by the source corpus.

Advances in nanopore sequencing technology have enabled investigation of the full catalogue of covalent DNA modifications. We present the first algorithm for the identification of modified nucleotides without the need for prior training data along with the open source software implementation, nanoraw. Nanoraw accurately assigns contiguous raw nanopore signal to genomic positions, enabling novel data visualization, and increasing power and accuracy for the discovery of covalently modified bases in native DNA. Ground truth case studies utilizing synthetically methylated DNA show the capacity to identify three distinct methylation marks, 4mC, 5mC, and 6mA, in seven distinct sequence contexts without any changes to the algorithm. We demonstrate quantitative reproducibility simultaneously identifying 5mC and 6mA in native E. coli across biological replicates processed in different labs. Finally we propose a pipeline for the comprehensive discovery of DNA modifications in any genome without a priori knowledge of their chemical identities.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e3af5da0dd75a9d7f57534d59758c774ef677800

Citations in the corpus

• Long-read human genome sequencing and its applications (2020, unknown): “Several computational tools have been developed to detect DNA and RNA modifications on the basis of these characteristic disruptions: Nanopolish 85,86 , signalAlign 127 , DeepSignal 153 , 154 , DeepMod 155 and Tombo 156 .”
• DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing (2023, unknown): “Based on the fact that unmethylated CpGs in vertebrates often range over long hypomethylated regions, Suzuki et al. proposed AgIn, which improved the con fi dence of 5mCpG detection by combining the IPD features of neighboring CpGs from SMRT data 22 .”