Inference and applications of ancestral recombination graphs
Nature Reviews Genetics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 30, 2024
Language: Английский
A general and efficient representation of ancestral recombination graphs
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Nov. 4, 2023
Abstract
As
a
result
of
recombination,
adjacent
nucleotides
can
have
different
paths
genetic
inheritance
and
therefore
the
genealogical
trees
for
sample
DNA
sequences
vary
along
genome.
The
structure
capturing
details
these
intricately
interwoven
is
referred
to
as
an
ancestral
recombination
graph
(ARG).
Classical
formalisms
focused
on
mapping
coalescence
events
nodes
in
ARG.
This
approach
out
step
with
modern
developments,
which
do
not
represent
terms
or
explicitly
infer
them.
We
present
simple
formalism
that
defines
ARG
specific
genomes
their
intervals
inheritance,
show
how
it
generalises
classical
treatments
encompasses
outputs
recent
methods.
discuss
nuances
arising
from
this
more
general
structure,
argue
forms
appropriate
basis
software
standard
rapidly
growing
field.
Language: Английский
A forest is more than its trees: haplotypes and inferred ARGs
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 2, 2024
Foreshadowing
haplotype-based
methods
of
the
genomics
era,
it
is
an
old
observation
that
"junction"
between
two
distinct
haplotypes
produced
by
recombination
inherited
as
a
Mendelian
marker.
In
genealogical
context,
this
recombination-mediated
information
reflects
persistence
ancestral
across
local
trees
in
which
they
do
not
represent
coalescences.
We
show
how
these
non-coalescing
("locally-unary
nodes")
may
be
inserted
into
graphs
(ARGs),
compact
but
information-rich
data
structure
describing
relationships
among
recombinant
sequences.
The
resulting
ARGs
are
smaller,
faster
to
compute
with,
and
additional
nearly
always
correct
where
initial
ARG
correct.
provide
efficient
algorithms
infer
locally-unary
nodes
within
existing
ARGs,
explore
some
consequences
for
inferred
from
real
data.
To
this,
we
introduce
new
metrics
agreement
disagreement
that,
unlike
previous
methods,
consider
rather
than
just
collection
trees.
Language: Английский