Lakhasly

Conclusion and perspectiv Identifying the process underlying replication origins and regulatory mechanisms has

been one of a focal point in replication origin research.With the development of experimental technology, we are expected to add more replication-related data, such as replication timing and epigenetic modification sites, and provide online service for data analysis in the next update.On the one hand, with the explosive growth of origin-related

datasets in cukaryotes, it has been a great impetus for researchers to explore the origin

characterization, firing and regulatory mechanisms of replication origins through

computational analysis and prediction.Furthermore, DeOri is expected to play a crucial role in experimental and computational studies of eukaryotic replication origins.

Conclusion and perspectiv Identifying the process underlying replication origins and regulatory mechanisms has

been one of a focal point in replication origin research. The proposal and validation of

pre-RC and CMG mechanisms have been reported [39].

remain unanswered. On the one hand, with the explosive growth of origin-related

datasets in cukaryotes, it has been a great impetus for researchers to explore the origin

characterization, firing and regulatory mechanisms of replication origins through

computational analysis and prediction. However, there have been few database studies

on cukaryotic origins, such as OriDB [40], which summarizes the replication origins of
In practical application, researchers can compare the replication origins of different

features can be extracted by machine learning to predict and distinguish the replication.

yeast. To fill this gap, we constructed and developed DeOri, which not only provided a

large amount of experimental data, but also presented the rORIs and statistical results.

species or different cell lines based on data of DeOri, and explore their differences in

sequence characteristics, chromatin distribution. For computational science, sequence

origins. For the experimenters, they can design new experimental methods according

to the experimental methods showed in DeOri. Additionally, based on the present

analysis, there are highly concentrated distribution of gene around replication origins
in human, which suggests a new analytical perspective for the researchers. Subsequent

analysis could focus on the specific types and functions of genes enriched in replication
origins. A deeper understanding of the human replication origins may reveal differences
between normal and cancer cells, potentially leading to novel treatment methods and
directions,
With the rapid development of artificial intelligence and machine learning in recent
years, the scientific research paradigm has been significantly promoted and changed [41]. In machine learning, many studies on replication origins have been based on the
available experimental data [42-45]. For example, replication origins in bacterial
genomes can be predicted using a Z-curve [46], and deep learning can be employed to
identify replication origins in humans [47]. Large amounts of high-quality data are urgently required for high-quality computational science studies. We believe that DeOri
will contribute to computational data mining and further understanding of the DNA
replication process in eukaryotes. Furthermore, DeOri is expected to play a crucial role
in experimental and computational studies of eukaryotic replication origins. In the
future, we will continue to maintain and update DeOri timely, and further integrate
latest experimental data to facilitate a comprehensive study of cukaryotic replication
origins. With the development of experimental technology, we are expected to add more
replication-related data, such as replication timing and epigenetic modification sites,
and provide online service for data analysis in the next update.