Identifying the origin of replication
(oriC) is fundamental to understanding bacterial
genome organization and replication mechanisms.
Traditional methods, such as GC-skew and
cumulative GC-skew (CGC) analyses, are
computationally simple but often imprecise.
Advanced approaches, including DnaA-box
detection and comparative genomics, can improve
accuracy but are frequently resource-intensive and
dependent on external biological information. This
paper presents ForiC, a novel method that bridges
this gap by combining the simplicity of GC- and
CGC-based analyses with improved oriC-region
localization performance. Beyond using CGC
inflection points, ForiC integrates three
complementary signals within a deterministic
selection rule: the CGC global maximum, the CGC
inflection point associated with that maximum, and
its alignment with the GC global minimum. The
resulting framework operates directly on the input
genome sequence, without DnaA-box motifs,
homology searches, or external databases. ForiC
was calibrated and evaluated on a curated set of 36
annotated bacterial genomes using five phylumstratified training/test splits. On one pre-defined
stratified test subset selected as a representative
balanced test subset, ForiC reached a hit accuracy
of 90.91% using a 1% window and a symmetrically
expanded two-window reporting interval, with
complementary localization metrics (centerdistance error, intersection-over-union)
and onewindow results reported in the main text. ForiC was
benchmarked against classical skew-based
methods (GC, CGC) and against a biologically
informed reference tool (OriFinder). OriFinder
achieves higher positional precision through
homology-based refinement but may report
multiple candidate oriC regions and depends on
external databases such as BLAST. In contrast,
ForiC consistently produces a single, directly
interpretable prediction per genome and runs
substantially faster without external resources,
making it complementary to reference-assisted
tools and particularly suitable for large-scale,
database-independent analyses. ForiC is available
at http://www.markostojicevic.com/foric.
algorithms, bioinformatics,
computational genomics, GC skew, oriC prediction
.
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