TY  - JOUR
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
Y1  - 2024/11/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1976 
EP  - 1986 
DO  - 10.1101/gr.279095.124 
VL  - 34 
IS  - 11 
UR  - http://genome.cshlp.org/content/34/11/1976.abstract 
N2  - 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. 
ER  -