OPTIMALITY
CRITERIA
for
De
Novo
Transcriptome
Assemb(lers|ly)
What
Number
of
raw
reads
used
Identification
of
poor
reads
Trimming
of
reads
Number
of
contigs
Number
of
singletons
N50
of
contigs
Description
Optimality
criteria
How
many
of
the
raw
reads
given
to
the
assembler
does
it
use
for
assembly?
[NOTE:
This
is
conflated
with
the
'number
of
singletons'
issue,
as
some
assemblers
count
single‐sequence
clusters
as
clusters
and
others
do
not.]
Does
the
assembler
identify
and
remove
from
consideration
low
quality
reads?
[NOTE:
Some
assemblers
may
classify
a
read
as
low
quality
if
it
appears
to
be
part
of
a
repeat
element.]
Does
the
assembler
trim
reads
for
quality
or
for
known
'contaminant'
sequence
(adapters,
poly(A/T))
during
assembly
to
avoid
illegal
joins?
How
many
contigs
does
the
assembler
produce?
[NOTE:
For
some
assemblers
there
is
a
distinction
between
clusters
(=reads
that
likely
come
from
the
same
transcription
unit)
and
contigs
(=assembled
reads,
that
may
represent
different
splicing
isoforms
or
allelic
forms
of
a
transcript.]
How
many
singletons
are
identified?
more
reads
used
=
better
either
use
mapping
specified
by
assembler,
or
map
reads
back
to
the
assembly
using
a
standard
tool
to
identify
verifiable
criteria
for
low
quality
read
removal
How
long
is
the
median
contig
size?
This
can
be
done
for
both
all
contigs
and
also
for
contigs
greater
than
a
given
length
(say
100
bases)
to
avoid
and
perhaps
highlight
the
effects
of
lots
of
'shrapnel'
contigs.
Mean
contig
length
and
What
is
the
distribution
of
contig
lengths?
SD
This
assesses
the
likely
completeness
of
the
transcriptome
(and
also
reports
Number
of
contigs
on
the
original
RNA
quality).
greater
than
1
kb
Maximum
contig
length
What
is
the
maximal
contig
length?
trimming
is
probably
a
good
thing
number
of
contigs
should
be
LESS
than
the
number
of
transcription
units
expected
in
the
genome
(e.g.
~20‐25,000
for
a
metazoan)
and
APPROACH
the
number
of
transcripts
expected
in
the
stage/tissue
surveyed
(e.g.
about
10‐12,000
for
a
metazoan
adult
tissue)
singletons
should
be
a
small
percentage
of
the
total
number
of
input
reads
in
a
high‐density
dataset
this
should
approach
the
expected
median
length
for
full
length
transcripts
in
the
species
studied
(e.g.
for
C.
elegans
it
is
~1
kb);
comparing
all
contigs
N50
and
contigs
>100
bases
N50,
there
shouldn't
be
much
reduction
this
should
approach
the
expected
mean
length
for
full
length
transcripts
in
the
species
studied
(e.g.
for
C.
elegans
it
is
~1.2
kb)
the
better
assembler
will
have
more
contigs
>
1kb
Total
span
of
contigs
What
is
the
total
length
of
the
contigs
produced?
Mean
length
of
singletons
Mean
quality
of
singletons
Singleton
mean
length
is
often
much
shorter
than
the
mean
length
of
the
input
reads:
singletons
may
be
poor
sequence
or
fragmented
transcripts.
The
longest
contig
should
not
usually
exceed
about
5
kb.
The
longest
contigs
should
have
'credible'
blast
matches
to
NR
protein/UniRef100
(i.e.
they
should
match
only
to
genes
that
have
long
transcripts,
and
not
to
odd
collections
of
different
genes
each
with
short
transcripts).
the
total
span
should
approach
the
expected
span
for
the
organism
studied
(e.g.
for
metazoans
from
25
to
40
Mb)
mean
length
of
singletons
should
be
less
than
the
mean
length
of
reads
Singleton
mean
quality
is
often
much
less
than
the
mean
quality
of
the
input
reads,
indicating
that
singletons
may
be
poor
sequence.
mean
quality
of
singletons
should
be
less
than
the
mean
quality
of
reads
We
do
not
expect
to
generate
many
huge
contigs
(though
there
are
genes
with
huge
transcripts).
Some
assemblers
may
generate
large
contigs
by
generating
'illegal'
chimaeras.
Optimality
Criteria
for
de
novo
Transcriptome
Assembly
Mark
Blaxter,
Sujai
Kumar,
Stephen
Bridgett
September
2010
page
1
Time
taken
Number
of
unique
bases
in
contigs
Number
of
novel
bases
in
contigs
compared
to
other
assemblies
Shared
bases
from
different
assemblies
Heterozygosity
Mapping
of
reads
Comparison
to
external
cognate
reference
transcripts
Comparison
to
external
reference
'complete'
proteome
Comparison
to
a
reference
conserved
proteome
set
Chimaera
checking
An
assembler
that
completes
in
a
shorter
wall
clock
time
permits
the
user
to
run
the
assembly
with
different
parameter
sets
in
a
reasonable
timeframe.
The
assembly
should
NOT
result
in
lots
of
contigs
that
objectively
could
be
coassembled.
Self‐clustering
should
result
in
a
minimal
reduction
in
the
extent
of
sequence.
Different
assemblers
will
yield
different
spans
of
assembly.
The
assembler
that
produces
the
greatest
extent
of
unique
sequence
is
likely
to
have
accessed
the
greatest
proportion
of
the
transcriptome.
This
can
be
done
by
doing
a
co‐clustering
between
two
assemblies
and
identifying
the
clusters
that
result
that
have
contributions
from
only
one
of
the
input
assemblies.
Contigs
that
result
from
different
assemblies
but
that
have
the
same
sequence
are
likely
to
be
of
more
believable
quality.
This
can
be
done
by
doing
a
co‐clustering
between
two
assemblies
and
identifying
the
clusters
that
result
that
have
contributions
from
both
of
the
input
assemblies.
The
nature
of
the
input
sample
should
allow
assessment
of
the
'sanity'
of
assemblies
based
on
the
expected
number
of
haploid
genomes
in
the
mix.
Thus
if
there
is
one
diploid
organism
as
input,
the
maximum
number
of
alleles
at
a
particular
base
should
be
2.
An
excess
of
alleles
at
a
position
will
suggest
over‐assembly
of
reads.
Mapping
back
of
reads
using
an
independent
mapping
algorithm
will
assess
the
'sanity'
of
the
derived
contigs.
Surprisingly,
it
is
possible
for
assemblers
to
generate
contigs
that
have
zero
reads
mapping
back
to
them!
If
there
are
existing
transcripts
from
the
species
of
interest
(or
a
very
close
relative)
a
good
assembly
will
efficiently
cover
these
independently
acquired
data.
These
reference
sequences
can
be
submitted
mRNAs
or
Sanger
ESTs.
Reads
can
be
mapped
back
using
a
standard
reference
mapper.
If
there
is
a
good
reference
proteome
from
a
genome
project
for
a
related
organism,
we
can
map
the
contigs
to
this
and
assess
the
coverage
of
the
proteome
by
the
contigs.
Thus
for
a
dipteran,
we
could
use
the
D.
melanogaster
'refseq'
protein
set
derived
from
the
genome.
By
defining
core
protein
sets
that
we
expect
to
be
present
in
all
members
of
the
group
from
which
the
target
species
derives
(e.g.
all
Metazoa)
we
can
ask
whether
the
assembly
contains
representatives
of
these
'essential'
genes.
Matching
is
done
using
BLAST,
with
a
reasonably
high
cutoff
(E‐value
1e‐12).
Long
contigs
may
result
from
chimaeric
joining
of
reads
that
derive
from
different
genes.
This
can
be
checked
by
taking,
for
all
contigs
greater
than
1
kb
in
length,
the
first
500
bases
and
the
last
500
bases
and
comparing
them
to
a
reference
proteome
set
(we
would
recommend
the
reference
conserved
protein
set).
Both
ends
of
each
contig
should
match
the
same
conserved
protein
(or
conserved
protein
group)
quicker
is
better
(but
speed
is
not
all)
an
all
against
all
blast
clustering
should
reveal
very
few
clusters
of
contigs
that
are
near‐identical
the
assembly
with
the
most
novel
bases
is
likely
to
be
better
[this
gives
an
assessment
of
the
number
of
putatively
well
assembled
bases
in
each
assembly]
based
on
the
expected
number
of
haploid
genomes
input,
the
better
assembly
will
not
display
allele
counts
exceeding
the
input
genome
count
the
best
assembler
will
have
the
minimum
number
of
'naked'
bases
when
reads
are
mapped
back.
the
better
assembler
will
leave
fewer
bases
of
the
cognate
reference
naked
the
better
assembler
will
generate
contigs
that
match
to
a
greater
proportion
of
the
reference
proteome,
and
will
cover
a
greater
number
of
residues
of
the
reference
proteome
the
better
assembler
will
produce
contigs
that
match
to
a
greater
proportion
of
the
reference
conserved
proteome,
and
will
cover
a
greater
number
of
residues
of
the
reference
conserved
proteome
the
better
assembler
will
have
a
low
percentage
of
possible
chimaeras
>
Optimality
Criteria
for
de
novo
Transcriptome
Assembly
Mark
Blaxter,
Sujai
Kumar,
Stephen
Bridgett
September
2010
page
2
Optimality
Criteria
for
de
novo
Transcriptome
Assembly
Mark
Blaxter,
Sujai
Kumar,
Stephen
Bridgett
September
2010
page
3