VirusWarn: A Mutation-Based Early Warning System to Prioritize Concerning SARS-CoV-2 and Influenza Virus Variants from Sequencing Data
Computational and Structural Biotechnology Journal,
Год журнала:
2025,
Номер
27, С. 1081 - 1088
Опубликована: Янв. 1, 2025
The
rapid
evolution
of
respiratory
viruses
is
characterized
by
the
emergence
variants
with
concerning
phenotypes
that
are
efficient
in
antibody
escape
or
show
high
transmissibility.
This
necessitates
timely
identification
such
surveillance
networks
to
assist
public
health
interventions.
Here,
we
introduce
VirusWarn,
a
comprehensive
system
designed
for
detecting,
prioritizing,
and
warning
emerging
virus
from
large
genomic
datasets.
VirusWarn
uses
both
manually-curated
rules
machine-learning
(ML)
classifiers
generate
rank
pathogen
sequences
based
on
mutations
concern
regions
interest.
Validation
results
SARS-CoV-2
showed
successfully
identifies
assessments,
manual-
ML-derived
criteria
positive
selection
analyses.
Although
initially
developed
SARS-CoV-2,
was
adapted
Influenza
their
dynamics,
provides
robust
performance,
integrating
scheme
accounts
fixed
past
seasons.
HTML
reports
provide
detailed
searchable
tables
visualizations,
including
mutation
plots
heatmaps.
Because
written
Nextflow,
it
can
be
easily
other
pathogens,
demonstrating
its
flexibility
scalability
efforts.
Язык: Английский
RCoV19: A One-Stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-Warning
Genomics Proteomics & Bioinformatics,
Год журнала:
2023,
Номер
21(5), С. 1066 - 1079
Опубликована: Окт. 1, 2023
The
Resource
for
Coronavirus
2019
(RCoV19)
is
an
open-access
information
resource
dedicated
to
providing
valuable
data
on
the
genomes,
mutations,
and
variants
of
severe
acute
respiratory
syndrome
coronavirus
2
(SARS-CoV-2).
In
this
updated
implementation
RCoV19,
we
have
made
significant
improvements
advancements
over
previous
version.
Firstly,
implemented
a
highly
refined
genome
curation
model.
This
model
now
features
automated
integration
pipeline
optimized
rules,
enabling
efficient
daily
updates
in
RCoV19.
Secondly,
developed
global
regional
lineage
evolution
monitoring
platform,
alongside
outbreak
risk
pre-warning
system.
These
additions
provide
comprehensive
understanding
SARS-CoV-2
transmission
patterns,
better
preparedness
response
strategies.
Thirdly,
powerful
interactive
mutation
spectrum
comparison
module.
module
allows
users
compare
analyze
assisting
detection
potential
new
lineages.
Furthermore,
incorporated
knowledgebase
effects.
serves
as
retrieving
functional
implications
specific
mutations.
summary,
RCoV19
vital
scientific
resource,
access
data,
relevant
information,
technical
support
fight
against
COVID-19.
complete
contents
are
available
public
at
https://ngdc.cncb.ac.cn/ncov/.
Язык: Английский