RT Journal
A1 Jha, Anupama
A1 Bohaczuk, Stephanie C.
A1 Mao, Yizi
A1 Ranchalis, Jane
A1 Mallory, Benjamin J.
A1 Min, Alan T.
A1 Hamm, Morgan O.
A1 Swanson, Elliott
A1 Dubocanin, Danilo
A1 Finkbeiner, Connor
A1 Li, Tony
A1 Whittington, Dale
A1 Noble, William Stafford
A1 Stergachis, Andrew B.
A1 Vollger, Mitchell R.
T1 DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools
JF Genome Research 
JO Genome Research 
YR 2024 
FD November 01 
VO 34 
IS 11 
SP 1976 
OP 1986 
DO 10.1101/gr.279095.124 
UL http://genome.cshlp.org/content/34/11/1976.abstract 
AB Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation and the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using Pacific Biosciences (PacBio) single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either the PacBio or Oxford Nanopore Technologies (ONT) sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kb long DNA molecules with an ∼1000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.