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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: RepliCNN
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Dominik
    family-names: Stroh
    email: dominik.stroh@uni-wuerzburg.de
    affiliation: >-
      Department of Bioinformatics,
      Theodor-Boveri-Institute, Biocenter, University of
      Würzburg, 97074 Würzburg, Germany
    orcid: 'https://orcid.org/0000-0003-3285-8629'
  - given-names: Kathi
    family-names: Zarnack
    email: kathi.zarnack@uni-wuerzburg.de
    affiliation: >-
      Department of Bioinformatics,
      Theodor-Boveri-Institute, Biocenter, University of
      Würzburg, 97074 Würzburg, Germany
    orcid: 'https://orcid.org/0000-0003-3527-3378'
identifiers:
  - type: doi
    value: 10.64898/2026.03.12.710907
    description: bioRxiv preprint
repository-code: 'https://github.com/ZarnackGroup/RepliCNN'
url: 'https://github.com/ZarnackGroup/RepliCNN'
repository-artifact: >-
  https://github.com/ZarnackGroup/RepliCNN/pkgs/container/replicnn
abstract: >-
  During S phase, the genome is replicated in a tightly
  regulated spatiotemporal order described as DNA
  replication timing (RT). Discontinuous lagging-strand
  synthesis produces Okazaki fragments whose strand-specific
  distribution reflects replication dynamics. Here, we
  present RepliCNN, a deep learning framework based on
  one-dimensional convolutional neural networks to predict
  RT from Okazaki fragment distributions obtained from
  strand-specific 3′ DNA end sequencing methods such as
  GLOE-Seq, TrAEL-seq, or OK-Seq. RepliCNN also
  automatically annotates replication origins, termination
  zones, replication fork directionality, and origin
  efficiency genome-wide from a single dataset. Benchmarking
  on public and in-house human and yeast datasets using
  leave-one-chromosome-out cross-validation demonstrates
  high predictive accuracy in both wild-type and
  perturbation experiments, enabling comprehensive analyses
  of replication dynamics from strand-specific DNA 3′ end
  sequencing data.
keywords:
  - bioinformatics
  - replication-timing
  - convolutional-neural-network
  - origin-of-replication
  - python
license: GPL-3.0