Genome

Res

May

doi

PMCID

Accurate

typing

of

short

tandem

repeats

from

genomewide

sequencing

data

and

its

applications

Arkarachai

Fungtammasan

Guruprasad

Ananda

Suzanne

E

Hile

Marcia

ShuWei

Su

Chen

Sun

Robert

Harris

Paul

Medvedev

Kristin

Eckert

and

Kateryna

D

Makova

Author

information

Article

notes

Copyright

and

License

information

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article

has

been

cited

by

other

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Abstract

Short

tandem

repeats

STRs

are

implicated

in

dozens

of

human

genetic

diseases

and

contribute

significantly

to

genome

variation

and

instability

Yet

profiling

STRs

from

shortread

sequencing

data

is

challenging

because

of

their

high

sequencing

error

rates

Here

we

developed

STRFM

s

hort

t

andem

r

epeat

profiling

using

f

lankbased

m

apping

a

computational

pipeline

that

can

detect

the

full

spectrum

of

STR

alleles

from

shortread

data

can

adapt

to

emerging

readmapping

algorithms

and

can

be

applied

to

heterogeneous

genetic

samples

eg

tumors

viruses

and

genomes

of

organelles

We

used

STRFM

to

study

STR

error

rates

and

patterns

in

publicly

available

human

and

inhouse

generated

ultradeep

plasmid

sequencing

data

sets

We

discovered

that

STRs

sequenced

with

a

PCRfree

protocol

have

up

to

ninefold

fewer

errors

than

those

sequenced

with

a

PCRcontaining

protocol

We

constructed

an

error

correction

model

for

genotyping

STRs

that

can

distinguish

heterozygous

alleles

containing

STRs

with

consecutive

repeat

Short

tandem

repeats

STRs

of

base

pairs

per

motif

constitute

of

the

human

genome

Lander

Due

to

the

high

incidence

of

polymerase

slippage

at

STRs

Levinson

and

Gutman

Abdulovic

et

al

Baptiste

and

Eckert

these

repeats

have

elevated

germline

mutation

and

polymorphism

rates

After

a

certain

threshold

length

STRs

are

termed

microsatellites

Kelkar

et

al

Ananda

et

al

The

high

level

of

polymorphism

makes

microsatellites

attractive

markers

for

population

and

conservation

genetics

studies

Jarne

and

Lagoda

Sunnucks

Wan

et

al

Kim

and

Sappington

and

for

identifying

individuals

in

forensics

Hagelberg

et

al

Chambers

et

al

Many

STRs

are

involved

in

gene

regulation

and

protein

function

Li

et

al

with

of

human

genes

containing

STRs

in

their

open

reading

frames

Gemayel

et

al

Although

long

microsatellites

have

attracted

much

attention

length

alterations

even

within

relatively

short

repeat

tracts

are

sometimes

associated

with

disease

Li

et

al

For

instance

differences

in

the

number

of

repeats

at

the

TG

T

STR

located

within

the

splicing

branchacceptor

site

of

the

CFTR

gene

exon

can

affect

inframe

exon

skipping

and

as

a

result

can

influence

the

severity

of

cystic

fibrosis

Cuppens

et

al

Chu

et

al

The

purity

of

STRs

the

degree

to

which

the

perfect

STR

sequence

remains

uninterrupted

also

has

a

functional

effect

Interrupted

STRs

have

lower

mutation

rates

Ananda

et

al

and

this

can

diminish

disease

risk

For

instance

of

Ashkenazi

Jews

have

a

T

to

A

mutation

in

the

APC

gene

encoding

for

a

tumor

suppressor

that

alters

an

interrupted

STR

A

TA

into

a

perfect

A

Laken

et

al

This

increases

the

probability

of

somatic

frameshift

mutation

within

the

STR

leading

to

APC

protein

inactivation

As

a

result

Ashkenazi

Jews

have

a

higher

colorectal

cancer

risk

Gryfe

et

al

Since

even

small

changes

in

STR

length

and

purity

can

have

functional

effects

accurate

STR

profiling

is

crucial

Despite

the

importance

of

STRs

in

evolution

and

disease

their

accurate

genotyping

from

next

generation

sequencing

NGS

data

has

been

challenging

for

review

see

Treangen

and

Salzberg

Sequencing

library

construction

frequently

includes

polymerase

chain

reaction

PCR

steps

during

which

a

polymerase

might

undergo

slippage

at

STRs

leading

to

amplicons

that

differ

in

length

due

to

expansion

and

contraction

of

repeat

units

Ellegren

Wang

et

al

Additionally

base

calling

by

NGS

instruments

at

repetitive

regions

is

frequently

imprecise

These

factors

result

in

high

sequencing

errors

at

homopolymer

runs

produced

by

the

Roche

and

Illumina

instruments

Balzer

et

al

Albers

et

al

From

a

bioinformatics

perspective

if

STRcontaining

reads

are

mapped

in

their

entirety

some

reads

can

not

be

mapped

because

of

high

mismatchindel

penalties

associated

with

STR

lengths

different

than

those

at

the

corresponding

positions

in

the

reference

genome

This

obscures

accurate

estimation

of

allele

frequency

and

underestimates

the

real

level

of

STR

variation

in

the

genome

To

alleviate

this

problem

a

shortread

alignment

approach

using

nonrepetitive

flanks

of

STRcontaining

reads

has

been

proposed

recently

lobSTR

Gymrek

et

al

This

tool

has

fast

running

time

and

takes

into

account

PCR

stutter

noise

during

the

genotyping

stage

However

the

entropy

scanning

implemented

by

lobSTR

to

detect

STRs

has

low

sensitivity

for

mononucleotide

STRs

and

short

STRs

bp

which

constitute

a

large

proportion

of

STRs

in

the

genome

Additionally

the

allele

frequency

at

STRs

for

genetically

heterogeneous

samples

for

which

a

simple

ratio

in

allele

frequency

present

in

heterozygous

diploids

is

not

expected

eg

for

tumors

viral

populations

and

organelles

can

not

be

determined

Furthermore

lobSTR

uses

a

fixed

embedded

in

the

program

mapping

algorithm

Novel

shortread

mapping

and

STR

detection

algorithms

Pellegrini

et

al

Lim

et

al

are

constantly

being

developed

an

STRprofiling

tool

that

can

be

customized

to

incorporate

emerging

mapping

algorithms

is

needed

The

recently

released

PCR

free

Illumina

library

preparation

protocol

hereafter

called

PCR

is

expected

to

improve

STR

genotyping

accuracy

The

direct

advantage

of

limiting

PCR

steps

during

NGS

is

the

increased

uniformity

of

the

sequencing

depth

Kozarewa

et

al

Also

this

protocol

eliminates

duplicate

reads

that

obscure

allele

frequency

profiling

for

heterogeneous

genetic

samples

Importantly

the

degree

to

which

the

accuracy

of

calling

STR

alleles

is

improved

using

the

PCRfree

protocol

has

not

been

evaluated

previously

Moreover

massive

amounts

of

data

have

already

been

generated

by

the

NGS

technology

with

the

PCR

containing

library

preparation

protocol

hereafter

called

PCR

and

some

such

data

can

not

be

regenerated

due

to

the

scarcity

of

samples

andor

time

and

cost

constraints

Therefore

universal

methods

are

urgently

needed

that

can

evaluate

and

correct

STR

errors

generated

by

NGS

technology

both

PCR

and

PCR

and

accommodate

evolving

protocols

and

sequencing

techniques

Some

efforts

have

been

made

to

evaluate

errors

generated

by

NGS

at

STRs

For

instance

errors

at

STRs

sequenced

with

the

PCR

protocol

vary

with

repeat

number

and

motif

size

Luo

et

al

However

an

explicit

quantitation

of

various

sources

of

STRrelated

sequencing

errors

has

been

lacking

which

hinders

an

unambiguous

estimation

of

STR

mutational

properties

Indeed

as

both

mutation

and

sequencing

error

rates

increase

with

STR

length

Kelkar

et

al

Luo

et

al

Highnam

et

al

one

can

not

confidently

decipher

mutation

rates

without

accounting

for

sequencing

error

rates

Recently

a

tool

to

guide

genotyping

of

STRs

using

informed

error

profiles

from

inbred

Drosophila

lines

RepeatSeq

has

been

released

Highnam

et

al

This

tool

utilizes

reads

mapped

by

other

programs

such

as

BWA

Li

and

Durbin

and

Bowtie

Langmead

et

al

and

predicts

the

most

probable

genotype

at

a

locus

based

on

STR

motif

length

and

base

quality

However

RepeatSeq

uses

the

wholeread

mapping

approach

which

introduces

a

bias

toward

the

STR

length

in

the

reference

genome

Gymrek

et

al

and

thus

might

obscure

the

true

STR

variation

spectrum

Such

biases

can

be

accounted

for

by

an

error

correction

model

based

on

the

STR

flankbased

method

To

profile

the

full

spectrum

of

STR

lengths

in

the

human

and

other

genomes

and

to

correct

for

NGSassociated

STR

errors

we

developed

STRFM

s

hort

t

andem

r

epeat

profiling

using

a

f

lankbased

m

apping

approach

a

flexible

pipeline

for

detecting

and

genotyping

STRs

from

shortread

sequencing

data

Our

pipeline

can

detect

STRs

of

any

length

including

short

ones

as

short

as

only

two

repeats

includes

an

errorcorrecting

module

and

can

incorporate

any

NGS

mapping

algorithm

with

pairedend

mapping

capability

making

it

adaptable

to

new

mapping

methods

as

they

become

available

Applying

this

pipeline

we

asked

the

following

questions

First

what

are

the

rates

and

patterns

of

sequencing

errors

associated

with

STRs

of

different

motif

sizes

mono

di

tri

and

tetranucleotides

motif

compositions

and

repeat

numbers

These

were

contrasted

between

publicly

available

genomewide

data

Ajay

et

al

and

determined

human

genomewide

germline

mutation

rates

at

STRs

Go

to

Results

The

STRFM

pipeline

We

designed

the

STRFM

pipeline

as

a

collection

of

tools

in

Galaxy

Giardine

et

al

Blankenberg

et

al

Goecks

et

al

providing

integration

with

various

mapping

algorithms

and

customization

for

a

variety

of

applications

Either

singleend

or

pairedend

sequencing

data

can

be

utilized

for

pairedend

read

data

each

read

is

treated

separately

The

core

of

the

pipeline

consists

of

the

following

three

procedures

Supplemental

Fig

First

STRFM

runs

a

shortread

STR

detection

tool

using

a

string

comparison

algorithm

see

Methods

for

details

The

algorithm

can

detect

exact

pure

or

uninterrupted

STRs

mono

through

hexanucleotide

STRs

greater

than

or

equal

to

two

repeats

incomplete

motifs

eg

ATATATA

interrupted

STRs

eg

AAAATAAAAA

or

multiple

STRs

in

a

read

Reads

that

do

not

Li

and

Durbin

Bowtie

Langmead

et

al

or

Bowtie

Langmead

and

Salzberg

treating

each

pair

of

flanking

sequences

as

a

faux

pairedend

read

Finally

STRFM

runs

a

profiler

tool

which

groups

all

reads

with

STRs

that

are

mapped

to

the

same

location

in

the

reference

genome

As

a

result

an

array

of

all

STR

lengths

from

the

reads

mapping

to

a

particular

STRcontaining

locus

is

generated

The

number

of

reads

that

completely

cover

an

STR

and

its

flanks

is

referred

to

as

the

informative

sequencing

depth

See

Supplemental

Text

for

details

about

running

time

and

sensitivity

of

detection

Supplemental

Tables

Supplemental

Fig

Assessment

of

error

profiles

at

STRs

for

PCR

and

PCR

library

preparation

protocols

The

NGS

errors

at

STRs

were

evaluated

using

two

data

sets

First

we

utilized

the

PCR

DNA

sequencing

data

generated

on

a

HiSeq

instrument

depth

for

a

human

male

sequenced

as

a

part

of

the

iPOP

study

Chen

et

al

We

combined

the

blood

and

saliva

samples

as

they

had

similar

error

profiles

see

below

Second

we

utilized

the

PCR

sequencing

data

generated

on

a

HiSeq

instrument

depth

for

a

human

male

blood

sample

sequenced

as

a

part

of

the

Platinum

Genome

Project

Ajay

et

al

The

PCR

data

were

downsampled

to

obtain

the

same

number

of

filtered

STRs

for

each

motif

size

as

present

in

the

PCR

data

set

for

fair

comparison

ie

to

obtain

the

depth

These

two

data

sets

are

comparable

because

both

were

generated

on

a

HiSeq

instrument

using

pairedend

reads

Therefore

the

differences

in

their

STR

error

profiles

are

expected

to

reflect

the

contribution

of

the

PCR

step

during

library

preparation

To

avoid

heterozygous

sites

we

limited

our

analysis

to

the

nonpseudoautosomal

regions

of

Chromosome

X

As

this

chromosome

is

hemizygous

in

males

any

STR

variability

within

an

allele

Alu

and

repetitive

elements

Supplemental

Table

Only

uninterrupted

perfect

STRs

were

considered

A

stringent

procedure

was

used

to

reduce

bioinformatics

errors

eg

we

removed

nonuniquely

mapped

reads

and

reads

with

improper

orientation

between

pairs

etc

see

Methods

for

details

For

each

STR

locus

in

the

reference

genomes

Chromosome

X

we

collected

all

mapped

STRcontaining

reads

using

STRFM

and

assigned

the

most

frequent

STR

length

as

the

major

allele

length

for

that

particular

locus

All

STR

lengths

that

differed

from

the

major

allele

length

were

considered

to

represent

sequencing

errors

As

the

rate

of

sequencing

errors

correlates

with

STR

length

Luo

et

al

Highnam

et

al

we

grouped

STR

loci

by

the

major

allele

length

and

evaluated

their

error

profiles

The

PCR

data

had

significantly

higher

error

rates

than

the

PCR

data

for

both

mono

and

dinucleotide

STRs

Fig

A

except

for

very

short

ones

repeat

number

For

mononucleotide

STRs

the

difference

in

error

rates

increases

until

bp

after

which

it

plateaus

at

an

difference

For

dinucleotide

STRs

the

difference

increases

until

bp

plateauing

at

an

difference

For

most

tri

and

tetranucleotide

loci

studied

Supplemental

Fig

the

PCR

data

also

had

higher

error

rates

than

the

PCR

data

however

the

confidence

intervals

of

the

two

curves

frequently

overlapped

Blood

and

saliva

PCR

data

had

similar

error

profiles

Supplemental

Fig

Our

overall

results

did

not

change

considerably

depending

on

whether

we

used

the

Figure

Erroneous

call

rates

for

Illumina

data

The

dotted

lines

represent

the

confidence

intervals

of

the

multinomial

sampling

Only

repeat

lengths

with

read

support

all

loci

combined

were

plotted

A

Human

male

X

Chromosome

for

PCRcontaining

We

observed

several

trends

that

were

similar

for

both

PCR

and

PCR

data

For

all

four

repeat

motif

sizes

the

error

rates

increased

exponentially

with

repeat

length

and

approached

a

plateau

at

longer

repeats

Fig

A

Supplemental

Figs

corroborating

previous

studies

Albers

et

al

Gymrek

et

al

Montgomery

et

al

For

a

fixed

repeat

length

error

rates

were

highest

for

mononucleotide

intermediate

for

dinucleotide

and

trinucleotide

and

lowest

for

tetranucleotide

STRs

Fig

A

Supplemental

Figs

We

also

classified

sequencing

errors

into

four

categories

shorter

than

the

major

allele

length

by

one

repeat

shorter

by

more

than

one

repeat

longer

by

one

repeat

or

longer

by

more

than

one

repeat

The

was

the

most

common

error

category

for

all

STRs

Fig

BC

Supplemental

Figs

For

mono

and

dinucleotide

STRs

the

was

followed

in

the

order

of

diminishing

prevalence

by

and

Fig

BC

Supplemental

Fig

For

the

other

STRs

no

further

conclusions

could

be

drawn

due

to

the

limited

number

of

sites

and

overlapping

confidence

intervals

Supplemental

Figs

We

also

studied

the

effect

of

motif

composition

on

the

error

rate

For

dinucleotide

STRs

at

least

bp

seven

units

long

significantly

higher

error

rates

were

observed

at

ATTA

n

repeats

than

at

ACGT

n

AGCT

n

and

GCCG

n

repeats

in

both

PCR

and

PCR

data

Supplemental

Fig

For

mononucleotide

STRs

no

significant

differences

in

error

rates

between

AT

and

GC

repeats

were

observed

for

the

lengths

studied

Supplemental

Fig

For

tri

and

tetranucleotide

STRs

the

number

of

loci

was

insufficient

to

evaluate

the

influence

of

motif

composition

on

error

rates

Somatic

mutations

also

might

contribute

to

the

STR

variability

in

the

two

data

sets

analyzed

here

however

both

the

PCR

and

PCR

data

should

be

affected

by

them

equally

Moreover

the

somatic

mutation

rates

of

dinucleotide

STRs

estimated

in

previous

studies

Hile

et

al

Baptiste

and

Eckert

are

to

lower

than

the

error

rates

estimated

here

for

the

same

repeat

number

Therefore

the

contribution

of

somatic

mutations

to

STR

error

rates

estimation

here

is

negligible

Confirmation

of

STR

error

patterns

via

ultradeep

sequencing

of

plasmids

The

genomewide

PCR

and

PCR

data

sets

utilized

above

were

generated

in

two

different

sequencing

facilities

and

this

might

have

contributed

to

the

discrepancy

in

their

error

rates

eg

due

to

batch

effect

We

therefore

sought

to

confirm

our

findings

with

an

additional

data

set

that

we

generated

entirely

in

our

own

laboratory

namely

using

both

PCR

and

PCR

protocols

we

sequenced

STRs

cloned

in

a

plasmid

vector

All

data

were

generated

in

our

facility

using

the

same

DNA

samples

personnel

and

equipment

We

sequenced

plasmid

libraries

containing

STR

inserts

Supplemental

Tables

and

a

control

vector

plasmid

Quail

In

addition

to

the

inserts

all

plasmid

vector

sequences

contained

and

one

mono

di

and

trinucleotide

STRs

respectively

Supplemental

Table

Our

plasmid

analyses

confirmed

that

PCR

data

had

significantly

higher

STR

error

rates

than

PCR

data

for

mono

and

dinucleotide

STRs

Fig

D

The

error

rates

between

PCR

and

PCR

data

were

comparable

for

short

STRs

however

for

both

mono

and

dinucleotide

STRs

that

were

at

least

seven

repeats

long

the

PCR

data

had

consistently

higher

STR

rates

than

PCR

databy

and

for

mono

and

dinucleotide

STRs

respectively

Our

plasmid

analysis

also

confirms

that

the

is

the

most

common

category

of

errors

Supplemental

Fig

Statistical

model

for

genotyping

STRs

from

NGS

data

The

error

profiles

estimated

in

this

study

can

be

used

to

correct

for

TR

errors

in

several

applications

including

genotyping

of

diploid

samples

distinguishing

between

homozygotes

and

heterozygotes

and

of

heterogeneous

genetic

samples

eg

tumors

viral

populations

and

DNAcontaining

organelles

To

achieve

this

we

developed

an

error

correction

model

that

takes

STR

features

repeat

number

and

motif

identity

into

account

see

Methods

for

details

Our

error

correction

model

can

utilize

error

profiles

error

rates

depending

on

repeat

numbers

and

motif

identity

provided

by

the

user

eg

for

novel

sequencing

technology

and

thus

is

not

limited

to

those

generated

by

this

A

even

in

the

presence

of

A

variant

calls

It

determines

the

most

probable

heterozygote

and

homozygote

alleles

at

a

locus

from

the

NGS

data

and

reports

the

log

odds

ratio

between

their

probabilities

which

can

be

interpreted

as

a

confidence

of

genotyping

the

higher

this

value

is

from

the

more

confidence

we

have

in

this

genotype

For

example

if

for

an

STR

locus

sequenced

at

depth

we

observed

three

three

and

three

reads

with

A

A

and

A

respectively

the

most

probable

heterozygote

is

A

A

and

the

most

probable

homozygote

is

A

According

to

the

model

the

log

odds

ratio

for

A

A

compared

to

A

is

strongly

favoring

a

heterozygote

in

this

case

Accuracy

of

the

genotyping

model

in

diploid

samples

To

test

the

performance

of

our

error

correction

model

we

generated

a

pseudodiploid

data

set

For

each

class

of

STRs

we

selected

loci

on

the

human

male

X

Chromosome

from

the

same

data

sets

that

we

used

to

evaluate

the

STR

error

profiles

of

the

PCR

and

PCR

data

above

that

are

supported

by

at

least

five

reads

Then

we

generated

sets

of

homozygous

loci

by

randomly

selecting

two

loci

with

the

same

motif

and

repeat

number

and

combining

their

reads

Similarly

heterozygous

loci

were

generated

by

randomly

selecting

two

loci

with

the

same

motif

but

different

repeat

The

genotyping

model

correctly

determined

the

genotype

for

of

both

homozygotes

and

heterozygotes

Table

For

STRs

with

motif

size

of

at

least

two

our

models

accuracy

was

at

least

Mononucleotide

STRs

were

slightly

more

difficult

to

genotype

correctly

than

the

other

STR

classes

lower

accuracy

and

were

more

difficult

to

genotype

with

PCR

data

than

with

PCR

data

lower

accuracy

This

is

consistent

with

mononucleotide

STRs

having

higher

error

rates

Fig

AD

Supplemental

Fig

Table

Evaluation

of

the

STR

genotyping

model

by

pseudodiploid

genotyping

To

investigate

factors

contributing

to

erroneous

genotyping

of

mononucleotide

STRs

we

used

logistic

regressions

with

the

correct

prediction

of

an

allele

combination

as

the

response

and

with

four

predictors

the

read

depth

STR

length

the

difference

in

the

lengths

of

STR

alleles

for

heterozygotes

and

the

ratio

of

read

depths

supporting

each

allele

for

heterozygotes

see

Methods

for

details

A

separate

logistic

regression

was

run

for

PCR

and

PCR

data

and

for

heterozygous

versus

homozygous

alleles

All

four

predictors

were

significant

in

at

least

one

of

the

four

models

Supplemental

Table

although

the

sets

of

significant

predictors

and

their

Fig

for

examples

Figure

Factors

that

affect

the

accuracy

of

STR

heterozygote

genotyping

using

the

error

correction

model

A

STR

length

B

The

length

difference

in

a

heterozygote

C

The

ratio

of

read

depths

supporting

each

allele

D

Read

depth

All

read

profiles

are

We

also

evaluated

the

robustness

of

genotyping

using

technically

replicated

samplestwo

PCR

libraries

and

of

the

whole

genome

for

sample

from

the

Platinum

Genome

Project

Ajay

et

al

We

profiled

the

STR

lengths

and

genotyped

those

sites

that

had

a

minimum

informative

read

depth

of

in

both

samples

The

ratio

of

loci

that

had

different

genotypes

the

genotype

discordance

rate

between

the

two

replicated

libraries

was

only

Accuracy

of

the

genotyping

model

in

heterogeneous

genetic

samples

Our

error

correction

model

is

able

to

accommodate

applications

other

than

standard

diploid

genotyping

For

example

the

allele

contribution

ratio

can

be

modified

to

allow

for

a

nondiploid

allele

frequency

ratio

commonly

present

in

tumors

and

genomes

of

organelles

eg

mitochondrial

DNA

The

user

can

find

the

percentage

of

heterogeneity

that

maximizes

the

likelihood

of

the

observed

STR

length

array

or

test

the

ratio

that

was

suggested

by

a

linked

genetic

marker

eg

a

SNP

To

demonstrate

this

capability

we

genotyped

a

sample

of

two

plasmids

with

cloned

mtDNA

Dloop

sequence

premixed

at

five

different

and

allele

whereas

the

lower

proportion

plasmid

has

a

C

allele

RebolledoJaramillo

et

al

Each

mixed

DNA

sample

was

sequenced

on

a

MiSeq

instrument

using

pairedend

reads

at

the

depth

of

per

site

Each

mixture

was

sequenced

twice

to

generate

two

replicates

We

ran

our

STRFM

pipeline

separately

on

each

mixture

and

each

replicate

and

genotyped

the

STR

locus

in

each

case

Since

the

plasmids

were

sequenced

at

a

very

high

depth

the

prediction

should

reflect

the

competency

of

the

model

to

detect

the

rare

allele

and

not

the

sequencing

depth

see

below

The

minor

allele

was

detected

for

both

replicates

at

ratios

of

and

and

in

one

of

the

Genotyping

diseaseassociated

long

trinucleotide

STRs

We

explored

the

abilities

of

the

STRFM

pipeline

and

statistical

model

to

genotype

long

trinucleotide

repeats

associated

with

diseases

Using

the

PCRdata

described

above

sequenced

with

reads

at

depth

Ajay

et

al

we

genotyped

diseaserelated

trinucleotide

STRs

from

Supplemental

Table

in

Castel

et

al

Five

of

them

had

read

depths

above

repeat

length

and

thus

could

be

genotyped

with

confidence

These

included

a

homozygote

CAG

repeat

in

the

gene

Wilburn

et

al

which

is

associated

with

Huntingtons

diseaselike

and

a

also

homozygote

CCG

repeat

in

the

RELN

gene

which

is

associated

with

a

higher

risk

of

autism

Supplemental

Table

For

the

remaining

five

loci

including

those

associated

with

spinocerebellar

ataxia

Jacobsen

syndrome

dentatorubralpallidouysian

atrophy

and

Huntingtons

disease

repeat

length

of

bp

the

informative

read

depth

was

below

preventing

us

from

genotyping

such

loci

with

confidence

Supplemental

Table

STR

germline

mutation

rates

and

patterns

The

germline

mutation

rate

is

a

fundamental

parameter

in

molecular

evolution

however

it

has

not

been

evaluated

for

human

STRs

on

a

genomewide

scale

We

utilized

our

genotyping

model

to

estimate

STR

germline

mutation

rates

using

a

threegeneration

pedigreefour

grandparents

two

parents

and

children

Fig

Asequenced

at

high

depth

with

a

PCR

protocol

as

part

of

the

Platinum

Genome

Project

Ajay

et

al

For

each

sequenced

individual

we

detected

and

genotyped

uninterrupted

perfect

STRs

with

at

least

and

bp

for

mono

di

tri

and

tetranucleotide

STRs

respectively

Figure

Germline

mutation

rate

analyses

using

STRFM

A

Cartoon

of

the

pedigree

used

with

arrows

showing

branches

where

mutations

were

detected

B

The

numbers

of

de

novo

mutations

arising

in

the

male

and

female

germ

lines

C

The

germline

mutation

rates

Overall

the

distribution

of

STR

allele

lengths

as

evaluated

from

sample

one

of

the

children

was

similar

to

that

described

in

the

recent

studies

McIver

et

al

Payseur

et

al

Gymrek

et

al

Willems

et

al

The

proportions

of

variable

loci

increased

with

repeat

length

and

at

the

same

repeat

length

were

lower

for

STRs

with

longer

motifs

Supplemental

Fig

Here

we

confirm

these

trends

for

mononucleotide

STRs

which

were

removed

from

previous

studies

due

to

high

sequencing

error

rates

or

inadequacy

of

error

profiles

We

also

confirm

that

alleles

at

most

heterozygous

STRs

differ

by

just

one

motif

unit

Supplemental

Fig

Payseur

et

al

Gymrek

et

al

Thus

accurate

genotyping

of

such

heterozygotes

is

important

We

studied

germline

indel

mutations

insertion

or

deletion

of

whole

repeat

units

in

autosomes

that

occurred

in

transmissions

from

grandparents

to

parents

and

that

were

confirmed

by

genotypes

of

children

Supplemental

Figs

To

be

scored

as

a

germline

mutation

the

event

was

required

to

have

the

following

characteristics

a

parent

having

an

allele

absent

from

hisher

father

or

mother

a

putative

mutant

allele

a

putative

mutant

allele

present

in

at

least

two

children

to

confirm

that

it

is

a

germline

variant

and

a

putative

mutant

allele

absent

in

the

second

parent

Supplemental

Fig

If

We

required

STRcontaining

loci

to

be

sequenced

to

at

least

informative

read

depth

in

all

children

parents

and

grandparents

Fig

A

The

number

of

loci

satisfying

this

requirement

totaled

loci

in

father

and

in

mother

equal

to

transmitted

STR

alleles

as

one

allele

is

transmitted

from

each

grandparent

At

such

loci

we

detected

germline

mutation

events

which

is

equivalent

to

mutations

per

locus

per

generation

Supplemental

Tables

Among

these

mutations

and

occurred

in

the

male

and

female

germ

line

Supplemental

Table

respectively

corroborating

male

mutation

bias

Kelkar

et

al

Sun

et

al

The

parental

origin

of

the

remaining

de

novo

mutations

could

not

be

determined

The

level

of

male

mutation

bias

varied

among

STR

classes

and

motifs

Fig

B

Supplemental

Fig

The

germline

mutation

rate

increased

with

repeat

length

and

among

STRs

of

the

same

length

was

highest

for

mononucleotide

repeats

Fig

C

The

STRs

with

C

and

AT

motif

are

the

most

mutable

among

mono

and

dinucleotide

STRs

Supplemental

Fig

corroborating

previous

studies

Denver

et

al

Kelkar

et

al

Sun

et

al

For

mono

and

dinucleotide

STRs

the

were

only

slightly

more

abundant

than

among

STR

de

novo

mutations

Fig

D

Supplemental

Fig

the

confidence

intervals

highly

overlapped

This

is

in

contrast

to

STR

NGS

sequencing

errors

that

were

predominantly

Fig

BC

Supplemental

Figs

We

next

examined

whether

these

mutations

were

distributed

randomly

along

the

genome

The

observed

number

of

mutant

mononucleotide

STR

loci

estimated

in

windows

had

high

correlation

with

the

number

of

mutations

expected

based

on

our

estimated

mutation

rates

by

repeat

number

and

corrected

by

the

number

of

loci

present

in

each

window

Pearson

correlation

Spearman

correlation

Supplemental

Fig

suggesting

that

these

mutations

were

randomly

distributed

along

the

genome

Only

two

windows

Chr

and

Chr

had

a

significantly

higher

number

of

mutations

than

expected

the

P

value

of

the

posthoc

binomial

exact

test

of

these

two

windows

was

and

respectively

Supplemental

Fig

Our

data

are

insufficient

to

test

the

randomness

of

the

distribution

at

smaller

windows

or

for

repeats

with

a

larger

motif

size

Required

depth

for

accurate

genotyping

The

power

to

accurately

genotype

a

locus

depends

on

many

factors

Fig

including

the

length

of

true

alleles

the

combination

of

true

alleles

the

ratio

of

read

depths

supporting

each

allele

and

the

informative

read

depth

at

that

locus

Based

on

the

error

profile

we

assessed

and

the

genotyping

model

we

formulated

we

can

estimate

the

minimal

read

depth

required

to

correctly

determine

an

allele

combination

for

a

locus

given

the

observed

STR

length

array

from

the

reads

mapping

to

this

locus

Recall

that

informative

read

depth

at

a

locus

is

the

number

of

reads

that

span

the

entire

STR

and

possess

flanks

on

both

of

its

sides

We

first

evaluated

the

minimum

informative

read

depth

required

to

accurately

genotype

a

locus

as

a

function

of

the

allele

length

We

focused

on

heterozygous

loci

with

the

two

alleles

containing

consecutive

repeat

numbers

since

these

are

the

hardest

cases

to

genotype

We

used

simulations

see

Methods

to

compute

the

minimal

number

of

informative

reads

required

to

correctly

genotype

with

accuracy

Fig

Supplemental

Table

For

instance

to

correctly

genotype

A

A

informative

reads

are

required

for

the

PCR

protocol

whereas

only

seven

informative

reads

are

required

for

the

PCR

protocol

For

PCR

data

only

five

informative

reads

per

locus

is

required

for

genotyping

of

STRs

for

all

motif

sizes

and

repeat

numbers

up

to

nine

The

PCR

data

usually

requires

a

higher

number

of

informative

reads

for

mono

and

dinucleotide

STRs

For

tri

and

tetranucleotide

STRs

up

to

repeats

the

number

of

informative

reads

required

is

only

five

Fig

as

they

have

lower

error

rates

Fig

A

Supplemental

Figs

The

sequencing

depth

requirements

for

longer

tri

and

tetranucleotide

STRs

could

not

be

evaluated

because

we

lacked

error

profile

data

for

such

long

STRs

We

also

evaluated

the

effect

of

the

motif

itself

since

some

motifs

are

more

error

prone

than

others

Supplemental

Table

For

the

STR

lengths

studied

we

observed

a

difference

in

the

number

of

informative

reads

required

to

genotype

dinucleotides

of

allele

combination

informative

reads

are

required

to

genotype

AT

AT

but

only

five

for

AC

AC

and

AG

AG

The

trends

for

minimum

informative

read

depth

for

genotyping

with

accuracy

are

similar

Supplemental

Table

Figure

The

minimal

informative

read

depth

required

for

correct

genotyping

of

STR

heterozygous

alleles

of

consecutive

repeat

number

with

success

rate

of

A

n

was

used

for

mononucleotide

STRs

and

AC

n

for

dinucleotide

STRs

All

tri

and

tetranucleotide

Next

we

calculated

the

average

genomewide

depth

needed

to

achieve

a

given

number

of

informative

reads

with

likelihood

see

Methods

This

depth

is

defined

as

the

ratio

of

the

number

of

sequenced

bases

to

the

genome

length

The

needed

genomewide

depth

is

higher

than

the

informative

depth

due

to

the

randomness

of

the

sequencing

process

and

the

need

to

have

sufficiently

long

nonunique

flanking

regions

Because

our

calculations

do

not

account

for

sequencing

quality

score

genome

location

sequencing

bias

or

mapping

bias

they

only

serve

as

approximations

for

what

is

minimally

needed

in

practice

Figure

shows

the

required

average

genomewide

depth

as

a

function

of

repeat

length

of

interest

and

read

length

utilized

The

advantage

of

long

reads

is

more

pronounced

for

higher

repeat

lengths

For

instance

to

achieve

informative

read

depth

for

STRs

independent

of

their

repeat

numbers

one

needs

to

accomplish

genomewide

sequencing

depth

of

and

for

and

singleend

reads

respectively

However

to

achieve

the

same

depth

for

STRs

one

needs

and

genomewide

sequencing

depth

for

and

singleend

reads

respectively

Figure

Genomewide

sequencing

depth

required

for

informative

read

depth

of

bp

repeats

with

a

success

rate

of

using

different

read

lengths

The

conversion

from

informative

to

genomewide

sequencing

depth

substantially

increases

the

difference

in

depth

requirements

between

PCR

and

PCR

protocols

For

instance

in

the

A

A

genotyping

example

above

the

difference

in

the

required

informative

sequencing

depth

between

PCR

and

PCR

protocols

was

only

versus

To

achieve

these

informative

read

depths

with

reads

the

genomewide

sequencing

depth

required

for

PCR

and

PCR

protocols

is

and

respectively

leading

to

the

difference

of

This

result

emphasizes

the

advantage

of

the

PCR

library

preparation

protocol

in

STR

genotyping

Go

to

Discussion

STRs

are

important

causative

agents

of

human

diseases

Pearson

et

al

Castel

et

al

and

are

useful

genetic

markers

Wright

and

Bentzen

Gupta

and

Varshney

Miah

et

al

AbdulMuneer

but

their

genotyping

with

NGS

technology

is

a

challenge

To

overcome

this

we

developed

STRFM

a

versatile

STR

profiling

method

that

is

applicable

to

a

wide

range

of

STR

lengths

and

motifs

We

have

thoroughly

evaluated

STR

error

profiles

generated

using

Illumina

PCR

and

PCR

library

preparation

protocols

using

both

computational

and

experimental

approaches

As

a

result

we

provide

the

scientific

community

with

an

STR

genotyping

tool

including

an

error

correction

model

and

a

tool

to

evaluate

the

sequencing

depth

required

for

accurate

genotyping

of

STR

loci

Factors

that

determine

STR

error

rates

and

patterns

STR

error

profiles

depend

on

repeat

length

Fig

AD

Supplemental

Fig

motif

length

Fig

AD

Supplemental

Fig

and

motif

composition

Supplemental

Fig

corroborating

previous

findings

Shinde

et

al

Highnam

et

al

For

the

STR

length

ranges

considered

in

this

study

mononucleotide

STRs

have

the

highest

error

rate

followed

by

di

tri

and

tetranucleotide

STRs

respectively

Fig

AD

Supplemental

Figs

The

patterns

of

STR

NGS

errors

we

describe

here

are

similar

to

those

previously

reported

for

STRs

typed

via

gel

electrophoresis

Murray

et

al

Walsh

et

al

Shinde

et

al

the

most

common

STR

error

category

resulting

from

slippage

during

PCR

amplification

is

the

eg

Murray

et

al

Walsh

et

al

The

error

rate

from

to

observed

by

us

here

for

fiveunit

mononucleotide

STRs

Fig

is

consistent

with

an

experimentally

measured

onebase

deletion

error

rate

within

a

fivebase

mononucleotide

STR

error

per

nucleotides

synthesized

by

Taq

polymerase

Eckert

and

Kunkel

Also

our

results

based

on

the

NGS

data

Supplemental

Figs

agree

with

previous

observations

that

tri

and

tetranucleotide

microsatellites

have

fewer

stutter

bands

compared

with

di

and

mononucleotide

STRs

Ellegren

The

Illumina

NGS

data

studied

by

us

were

generated

using

the

Phusion

polymerase

Ajay

et

al

Chen

et

al

Oyola

et

al

therefore

the

observed

error

patterns

might

be

driven

by

the

error

profile

of

this

enzyme

The

error

rates

of

the

Phusion

enzyme

are

lower

than

those

of

the

Taq

polymerase

Frey

and

Suppmann

However

the

similarity

between

our

error

patterns

and

those

of

the

Taq

polymerase

Shinde

et

al

suggest

that

the

error

patterns

of

these

two

polymerases

are

comparable

The

PCR

library

preparation

protocol

is

expected

to

reduce

bias

in

sequencing

coverage

Kozarewa

et

al

Our

findings

reveal

that

eliminating

the

PCR

step

reduces

STR

errors

as

well

Based

on

our

Chromosome

X

data

approximately

a

and

error

reduction

was

observed

for

mono

and

dinucleotide

STRs

with

greater

than

six

and

five

repeats

respectively

This

allows

the

PCR

protocol

to

achieve

the

equivalent

power

of

the

PCR

with

lower

sequencing

depth

For

example

to

distinguish

A

from

A

using

reads

we

need

sequencing

depth

for

PCR

data

but

we

need

only

depth

for

PCR

data

Here

we

used

the

reference

genome

sequence

Mapping

STRs

to

Rosenbloom

et

al

might

increase

the

number

of

loci

that

we

will

identify

but

should

not

affect

our

overall

conclusions

Indeed

we

utilized

stringent

mapping

parameters

to

map

STR

flanking

regions

without

mismatches

and

indels

This

minimized

incorrect

mapping

to

paralogous

loci

that

are

potentially

more

abundant

in

the

new

reference

genome

Minimum

sequencing

depth

We

developed

a

method

to

estimate

the

informative

and

genomewide

sequencing

depths

required

for

STR

genotyping

We

demonstrate

that

the

required

sequencing

depth

depends

on

the

STR

error

profile

as

discussed

above

sequencing

read

length

allele

combination

and

allele

sequencing

depth

skew

in

a

heterozygote

The

depthestimating

tool

integrated

in

Galaxy

Giardine

et

al

Blankenberg

et

al

Goecks

et

al

can

be

used

for

selecting

sequencing

depth

while

planning

a

new

project

and

evaluating

which

STRs

can

be

accurately

genotyped

from

a

completed

sequencing

project

eg

using

a

publicly

available

data

set

The

desired

level

of

resolution

contributes

to

determining

the

required

read

depth

For

instance

for

some

questions

eg

for

studying

allele

spectrum

in

search

of

loci

evolving

under

selection

distinguishing

alleles

of

consecutive

lengths

in

a

heterozygote

is

important

For

other

questions

eg

scoring

normal

versus

premutation

diseasecausing

alleles

separated

by

several

repeat

numbers

such

high

resolution

might

not

be

needed

Therefore

the

recommended

minimal

depth

We

found

that

to

accurately

with

success

rate

genotype

STRs

with

less

than

nine

repeats

using

the

PCR

library

protocol

the

required

informative

read

depth

is

only

which

corresponds

to

genomewide

sequencing

depths

of

and

for

mono

di

tri

and

tetranucleotide

STRs

respectively

for

reads

These

sequencing

depths

are

comparable

to

those

present

in

most

publicly

available

data

sets

Our

genotyping

model

and

minimum

sequencing

depth

study

provide

us

with

an

opportunity

to

recommend

the

minimal

depth

needed

for

sequencing

of

diseasecausing

STRs

For

instance

a

change

from

to

repeats

at

the

CGC

n

repeat

present

at

the

gene

encoding

polyA

binding

protein

causes

oculopharyngeal

muscular

dystrophy

OPMD

Brais

et

al

Pearson

et

al

Running

our

simulations

we

came

to

a

conclusion

that

at

least

five

informative

reads

are

required

to

correctly

genotype

the

CGC

CGC

heterozygote

with

a

success

rate

This

corresponds

to

a

genomewide

sequencing

depth

of

for

reads

using

either

the

PCR

or

PCR

protocol

Tools

for

STR

length

profiling

The

STRFM

pipeline

allows

one

to

detect

a

broad

range

of

STR

lengths

and

motifs

including

mononucleotide

STRs

and

short

STRs

Supplemental

Table

clearly

distinguish

between

perfect

uninterrupted

and

interrupted

STRs

Supplemental

Table

and

be

flexible

with

respect

to

the

algorithm

to

be

used

for

mapping

the

STR

flanking

sequences

Unlike

other

approaches

STRFM

can

profile

mononucleotide

STRs

can

profile

heterogeneous

genetic

samples

that

do

not

follow

a

allele

frequency

distribution

down

to

an

allele

frequency

of

and

can

detect

STRs

in

species

without

annotated

STRs

in

the

reference

genome

Because

STRFM

aligns

Giardine

et

al

Blankenberg

et

al

Goecks

et

al

and

thus

can

be

customized

via

a

graphical

user

interface

The

STRFM

pipeline

also

could

be

used

to

extract

STRs

from

mapped

long

reads

generated

by

SMRT

technology

Pacific

Biosciences

Roberts

et

al

Chaisson

et

al

or

MinION

Oxford

Nanopore

Schneider

and

Dekker

The

long

reads

from

these

technologies

would

allow

us

to

study

longer

microsatellites

which

exceed

the

current

read

length

of

Illumina

technology

Due

to

the

differences

in

read

lengths

and

sequencing

errors

between

Illumina

and

these

technologies

however

such

data

would

require

special

mapping

procedures

and

the

development

of

separate

error

models

Alternatively

STRFM

can

take

mapped

reads

in

standard

SAM

format

detect

STRs

in

the

mapped

reads

and

profile

the

STR

length

Although

the

unbiased

property

of

flankbased

mapping

will

be

lost

this

way

this

demonstrates

the

versatility

of

the

STRFM

in

profiling

STRs

even

when

an

ad

hoc

Germline

mutation

rates

of

STRs

in

the

human

genome

We

report

here

the

first

genomewide

study

of

STR

mutation

rates

Using

the

NGS

data

we

genotyped

million

STR

loci

as

compared

to

loci

examined

in

a

previous

study

Sun

et

al

The

pattern

of

de

novo

STR

mutations

we

observed

differs

from

that

of

STR

sequencing

errors

Figs

BC

Supplemental

Figs

a

result

that

demonstrates

the

importance

of

applying

our

error

correction

genotyping

model

when

studying

STR

mutations

in

genomes

Specifically

germline

mutations

have

comparable

rates

for

expansions

and

deletions

among

the

repeat

lengths

we

studied

whereas

STR

sequencing

errors

have

a

very

strong

deletion

bias

The

male

mutation

bias

of

dinucleotide

STRs

observed

here

is

lower

than

that

described

in

another

recently

published

study

Sun

et

al

which

was

not

genomewide

and

included

a

higher

proportion

of

ACrepeats

than

we

find

genomewide

cf

Sun

et

al

Supplemental

Table

with

Supplemental

Table

in

the

present

study

Because

ACrepeats

have

the

highest

male

mutation

bias

among

the

dinucleotide

repeats

we

studied

Supplemental

Fig

this

could

have

inflated

male

mutation

bias

in

the

study

by

Sun

et

al

Also

a

confounder

in

our

study

is

that

we

could

identify

the

parental

origin

for

only

of

de

novo

mutations

We

could

not

assign

a

parental

mutation

bias

when

both

grandparents

carried

an

allele

identical

to

the

mutant

allele

in

the

next

generation

eg

mutation

from

A

to

A

when

both

grandparents

had

A

allele

which

could

underestimate

the

real

male

mutation

bias

in

our

study

The

germline

mutation

rates

we

estimated

for

STRs

are

higher

than

recently

obtained

estimates

Sun

et

al

Several

factors

could

contribute

to

this

difference

We

hypothesize

that

a

major

factor

is

that

we

used

perfect

uninterrupted

STRs

exclusively

in

this

study

whereas

the

study

of

Sun

et

al

included

interrupted

STRs

that

as

we

have

shown

have

significantly

lower

mutation

rates

genomewide

Ananda

et

al

Additionally

our

study

presents

a

genomewide

analysis

including

all

STRcontaining

loci

in

the

genome

for

which

we

could

obtain

data

of

high

depth

In

contrast

Sun

and

colleagues

analyzed

loci

used

in

largescale

genemapping

studies

and

thus

potentially

affected

by

ascertainment

bias

Finally

our

analysis

is

based

on

the

analysis

of

a

single

family

whereas

Sun

et

al

included

hundreds

of

families

and

this

can

also

lead

to

differences

in

mutation

rates

We

can

apply

these

STR

germline

mutation

rates

and

patterns

in

several

areas

In

population

genetics

these

rates

can

be

used

to

estimate

divergence

times

of

recent

population

splits

Since

STRs

have

higher

mutation

rates

than

SNPs

Ellegren

Campbell

and

Eichler

the

former

can

accumulate

many

mutations

in

a

short

period

of

time

and

thus

can

provide

higher

resolution

In

forensics

knowing

mutation

rates

can

aid

in

estimating

the

level

of

STR

polymorphism

in

computing

the

number

of

STR

markers

necessary

for

individual

identification

or

kinship

testing

and

in

computing

the

probability

of

relatives

having

different

alleles

due

to

a

mutation

Also

in

medical

research

we

can

evaluate

the

likelihood

of

premutation

STR

loci

to

become

diseasecausing

STRs

in

the

next

generation

Although

most

studied

diseasecausing

STRs

are

long

trinucleotide

repeats

Pearson

et

al

some

short

STRs

were

also

shown

to

have

strong

implications

in

several

diseases

Cuppens

et

al

Chu

et

al

Pearson

et

al

Go

to

Methods

Data

sets

used

to

assess

STR

error

rates

The

PCR

data

were

downloaded

from

the

NCBI

Sequence

Read

Archive

httpwwwncbinlmnihgovsra

with

accession

numbers

is

a

pairedend

data

set

sequenced

from

blood

of

a

human

male

at

the

depth

and

are

pairedend

data

sets

sequenced

from

saliva

of

a

human

male

at

the

depth

The

PCR

data

were

downloaded

from

the

European

Sequence

Read

Archive

httpwwwebiacukena

with

accession

numbers

and

This

male

individual

is

a

Utah

resident

from

the

CEPH

collection

whose

blood

was

sequenced

at

the

depth

Using

STRFM

to

assess

error

profiles

in

the

context

of

STRs

for

Illumina

data

Detecting

STRs

in

short

reads

Our

STR

detection

module

for

uninterrupted

STRs

executes

the

following

three

steps

Scanning

reads

using

sliding

windows

For

a

given

k

eg

k

for

dinucleotide

STRs

we

compared

consecutive

k

mer

window

size

sequences

with

a

step

size

of

k

If

a

sequence

at

a

given

position

matches

the

previous

kmer

it

was

marked

with

a

plus

and

with

a

minus

if

otherwise

Since

we

do

not

allow

mutations

in

the

reported

STR

consecutive

plus

signal

sequence

means

that

a

k

mer

STR

is

present

in

this

sample

Report

k

mer

STRs

if

the

length

is

larger

than

the

threshold

provided

by

the

user

The

method

to

detect

interrupted

STRs

can

be

found

in

the

Supplemental

Text

Using

the

STRFM

pipeline

we

detected

mono

di

tri

and

tetranucleotide

STRs

with

lengths

and

bp

respectively

in

raw

sequencing

reads

These

four

classes

of

STRs

were

analyzed

separately

throughout

the

analysis

We

also

required

that

all

bases

in

the

STRs

and

the

flanking

bases

on

both

sides

have

a

Phred

quality

score

of

Mapping

flanking

bases

of

STRs

to

the

reference

genome

At

least

twenty

bases

flanking

the

STRs

upstream

and

downstream

in

the

STRcontaining

reads

were

extracted

as

FASTQ

files

and

mapped

to

the

human

reference

genome

with

BWA

version

Li

and

Durbin

as

pairedend

reads

using

aln

and

sampe

mode

To

minimize

incorrect

mapping

mismatches

and

indels

were

not

allowed

in

mapping

The

resulting

alignments

were

filtered

to

retain

only

the

uniquely

mapping

flank

pairs

with

proper

orientation

of

the

upstream

and

downstream

flanks

We

discarded

STRs

that

mapped

to

the

Y

Chromosome

pseudoautosomal

regions

and

human

selfalignment

regions

from

the

UCSC

Genome

Browser

to

obtain

only

unique

hemizygous

loci

on

Chromosome

X

reads

mapping

to

autosomes

were

discarded

Duplicated

reads

were

not

removed

throughout

the

analysis

to

get

accurate

magnitude

of

errors

generated

by

the

PCR

and

PCR

library

preparation

protocols

Profiling

STR

length

at

each

locus

At

each

STR

locus

in

the

reference

genome

identified

using

the

same

script

to

detect

STRs

in

reads

starting

from

the

FASTAformatted

human

reference

genome

all

mapped

STRcontaining

reads

were

binned

and

the

STRs

contained

in

these

reads

were

collated

Only

uninterrupted

reference

STR

loci

were

considered

STRs

with

the

same

motif

size

mono

di

tri

or

tetranucleotide

repeats

on

the

X

Chromosome

that

are

closer

than

bp

were

excluded

to

avoid

incorrect

STRs

profiling

from

overlapping

STRs

In

total

and

loci

mono

di

tri

and

tetranucleotide

STRs

respectively

were

studied

The

STRs

were

Construction

of

bootstrap

confidence

intervals

For

every

STR

motif

size

motif

composition

and

allele

length

using

the

observed

counts

of

error

variants

of

different

lengths

we

generated

multinomially

distributed

random

counts

of

error

variants

and

computed

the

error

rate

using

these

counts

This

was

repeated

times

and

the

confidence

intervals

for

the

error

rate

were

constructed

from

the

to

the

percentiles

of

the

error

rates

The

bootstrap

confidence

intervals

were

not

drawn

when

the

sampling

numbers

of

error

counts

at

particular

percentiles

were

zero

For

example

several

data

points

miss

the

lower

bound

of

confidence

intervals

due

to

the

small

number

Plasmid

construction

sequencing

and

analysis

The

construction

of

plasmids

containing

artificially

inserted

tandem

repeat

sequences

has

been

previously

described

Eckert

et

al

Kelkar

et

al

Ananda

et

al

Baptiste

et

al

The

standard

procedures

were

used

for

library

preparation

and

the

sequencing

on

MiSeq

The

analysis

was

performed

using

comparable

parameters

with

the

analysis

in

human

X

Chromosome

See

Supplemental

Text

for

complete

description

Building

an

error

correction

model

for

genotyping

The

raw

reads

were

processed

to

generate

an

STR

length

array

using

the

STRFM

pipeline

The

STR

length

array

for

each

locus

contained

repeat

lengths

number

of

repeats

and

motif

The

three

most

common

STR

lengths

from

the

input

STR

length

array

of

a

single

locus

were

considered

for

formulating

homozygotes

and

heterozygotes

The

probability

for

any

given

allele

length

to

be

observed

was

retrieved

from

either

PCR

or

PCR

error

profiles

generated

from

X

Chromosome

data

When

the

conversion

could

not

be

observed

from

an

error

profile

eg

AG

was

not

observed

to

change

to

AG

the

base

substitution

rate

Kong

et

al

was

used

The

probability

value

for

all

the

repeat

lengths

in

the

input

STR

length

array

was

multiplied

to

calculate

the

probability

to

generate

the

whole

STR

length

array

from

either

a

homozygote

or

a

heterozygote

For

homozygotes

each

of

the

three

most

common

STR

lengths

was

considered

The

allele

that

gave

the

highest

probability

to

generate

the

observed

STR

array

at

a

locus

was

considered

to

represent

the

probable

homozygous

allele

For

heterozygotes

the

three

most

common

STR

lengths

were

grouped

into

three

sets

of

alleles

For

each

set

the

probability

of

the

two

alleles

to

generate

the

observed

repeat

lengths

was

averaged

The

most

probable

set

of

alleles

was

used

as

a

probable

heterozygous

form

The

logodds

ratio

of

the

homozygous

form

to

the

heterozygous

form

was

then

reported

with

a

score

greater

than

Evaluation

of

the

error

correction

models

correct

prediction

rate

for

diploid

samples

We

used

the

male

X

Chromosome

sequencing

data

from

the

iPOP

study

Chen

et

al

for

PCR

and

the

Platinum

Genome

Project

Ajay

et

al

for

PCR

to

in

silico

generate

pseudodiploid

data

to

test

the

genotyping

model

see

Results

We

generated

homozygous

loci

by

combining

two

loci

with

the

same

motif

and

major

allele

length

Heterozygous

loci

were

generated

similarly

To

ensure

that

the

test

data

set

had

an

error

profile

similar

to

the

actual

data

we

focused

on

loci

in

which

the

major

alleles

were

at

least

one

repeat

longer

than

our

minimum

cutoff

and

bp

for

mono

di

tri

and

tetranucleotide

STRs

respectively

to

allow

STR

contractions

which

are

the

major

category

of

STR

errors

We

considered

Logistic

regressions

were

performed

using

the

standard

R

package

R

Core

Team

Pseudo

R

squared

of

a

model

was

calculated

as

D

o

D

D

o

where

D

o

is

the

deviance

and

D

is

the

residual

deviance

of

the

model

Relative

contribution

of

each

predictor

to

a

model

was

calculated

using

D

o

D

D

o

D

p

D

o

D

where

D

p

is

the

deviance

of

a

model

obtained

by

removing

the

predictor

of

interest

Fungtammasan

et

al

The

genotype

concordancediscordance

rate

was

evaluated

on

libraries

and

from

the

European

Sequence

Read

Archive

httpwwwebiacukena

The

genotype

discordance

was

defined

as

the

incongruence

of

at

least

one

allele

between

genotypes

of

two

replicated

libraries

Pompanon

et

al

Minimum

read

depth

was

set

at

Evaluation

of

the

genotyping

models

correct

prediction

rate

for

heterogeneous

genetic

samples

To

assess

the

accuracy

and

precision

of

the

error

correction

model

we

prepared

heterogeneous

genetic

samples

by

mixing

DNA

from

two

different

plasmids

see

Supplemental

Text

for

details

The

ratios

of

artificial

heterogeneous

genetic

samples

clones

to

were

designed

as

and

The

sequencing

data

of

mixed

clones

were

profiled

using

the

STRFM

pipeline

described

above

The

sequences

from

the

two

plasmids

were

also

profiled

to

check

STR

lengths

in

the

inserted

DNA

region

that

were

different

between

the

two

plasmids

The

error

correction

and

genotyping

were

performed

using

the

error

rates

estimated

from

Using

an

error

correction

model

to

evaluate

germline

mutation

rates

and

patterns

PCR

wholegenome

sequencing

data

of

the

threegeneration

pedigree

from

the

Platinum

Genome

Project

Ajay

et

al

was

downloaded

from

This

family

consists

of

four

grandparents

two

parents

and

children

The

data

include

libraries

and

Sequencing

depth

for

each

individual

is

We

considered

only

autosomal

STRcontaining

loci

which

are

sequenced

to

at

least

informative

read

depth

in

all

children

parents

and

grandparents

STRs

on

sex

chromosomes

were

excluded

We

genotyped

all

individuals

of

the

pedigree

using

error

rates

estimated

from

X

Chromosome

of

PCR

data

which

was

evaluated

from

an

individual

of

this

pedigree

to

reduce

potential

differences

due

to

batch

effect

The

germline

mutations

were

classified

by

the

gender

Computing

minimum

informative

read

depth

required

for

accurate

genotyping

We

simulated

a

series

of

read

profiles

of

all

possible

combinations

of

alleles

with

consecutive

repeat

numbers

for

and

depth

for

motifs

A

T

AT

AC

AG

combined

motif

of

trinucleotide

STRs

and

combined

motif

of

tetranucleotide

STRs

First

for

each

motif

and

STR

length

combination

we

selected

the

range

of

observed

STRs

from

our

error

profiles

and

generated

all

possible

combinations

of

STR

arrays

In

this

step

we

did

not

include

the

observed

STR

lengths

that

have

the

probability

of

occurrence

rare

error

form

to

reduce

computational

time

We

limited

our

study

to

Estimation

of

required

sequencing

depth

First

we

derived

an

equation

to

calculate

the

locusspecific

sequencing

depth

needed

to

achieve

a

certain

informative

read

depth

by

assuming

uniform

sequenced

reads

along

the

genome

Suppose

that

the

sequenced

reads

of

length

L

uniformly

represent

parts

of

a

genome

that

has

an

infinite

size

and

no

redundant

sequences

If

we

place

these

reads

on

every

position

of

this

genome

the

sequencing

depth

will

be

equal

to

read

length

L

If

an

STR

of

interest

is

r

bp

long

and

we

require

at

least

F

bp

of

flanking

regions

both

upstream

and

downstream

there

will

be

L

F

r

reads

that

are

associated

with

at

least

one

base

of

STR

or

flanking

regions

Among

these

reads

F

r

start

positions

and

F

r

end

positions

of

reads

locate

within

STR

or

flanking

regions

These

positions

are

mutually

exclusive

as

long

as

reads

are

longer

than

F

r

Thus

F

r

reads

do

not

cover

at

least

one

base

of

STR

and

F

bases

either

upstream

or

downstream

is

the

correction

for

start

and

end

position

at

the

outer

edge

of

flanking

regions

which

can

still

map

Since

the

reads

that

do

not

cover

the

whole

STR

of

length

r

or

do

not

cover

at

least

the

required

minimum

flanked

bases

are

not

useful

for

profiling

the

informative

read

depth

of

L

is

L

F

r

F

r

L

F

r

To

achieve

the

informative

read

depth

of

X

an

STR

at

a

specific

locus

needs

to

be

sequenced

at

depth

where

L

is

read

length

F

is

the

number

of

flanked

bases

required

at

each

flank

and

r

is

the

expected

repeat

length

of

an

STR

of

interest

We

will

use

y

required

to

represent

this

value

This

equation

is

true

for

all

flankbased

mapping

approach

programs

that

require

certain

lengths

for

both

flanking

regions

In

reality

the

sequenced

reads

are

not

evenly

distributed

along

the

genome

Therefore

we

used

Poisson

distribution

to

estimate

the

required

genomewide

sequencing

depth

the

average

depth

for

all

loci

in

the

genome

that

guarantees

that

a

certain

percentage

of

STRs

in

the

genome

eg

have

at

least

a

certain

level

of

locusspecific

sequencing

depth

and

informative

read

depth

Using

y

to

represent

specific

level

of

sequencing

depth

and

λ

to

represent

genomewide

sequencing

depth

we

can

write

the

equation

that

the

probability

of

observing

a

locus

with

depth

y

or

P

Y

y

is

We

are

interested

in

finding

the

required

genomewide

sequencing

depth

λ

that

gives

sufficient

informative

read

depth

for

a

certain

percentage

eg

of

the

genome

In

other

words

we

want

to

find

the

minimum

value

of

λ

that

makes

P

Y

y

required

greater

than

or

equal

to

a

specific

percentage

of

the

genome

The

P

Y

y

required

can

be

calculated

from

Each

term

of

the

Poisson

distribution

can

be

calculated

from

Equation

and

y

required

can

be

calculated

from

Equation

The

tool

to

estimate

locusspecific

y

required

and

genomewide

sequencing

depth

λ

is

provided

in

the

Galaxy

toolshed

Galaxy

tool

description

The

tools

from

the

STRFM

pipeline

are

freely

available

on

Galaxy

httpsusegalaxyorg

Giardine

et

al

Blankenberg

et

al

Goecks

et

al

Users

can

install

local

Galaxy

and

download

all

tools

used

in

this

research

from

repository

see

Supplemental

Text

for

details

These

tools

are

also

available

at

github

httpsgithubcomArkarachaiSTRFM

The

STR

error

rates

estimated

in

this

study

can

be

downloaded

from

Go

to

Data

access

The

sequencing

data

of

the

plasmid

containing

artificially

inserted

tandem

repeat

sequences

in

both

PCR

and

PCR

systems

all

the

ratios

of

heterogeneous

genetic

samples

and

the

original

plasmids

before

mixing

have

been

submitted

to

the

NCBI

Sequence

Read

Archive

SRA

httpwwwncbinlmnihgovsra

under

accession

number

Go

to

Supplementary

Material

Supplemental

Material

Click

here

to

view

Go

to

Acknowledgments

We

thank

Sarah

J

Carnahan

Craig

for

her

comments

on

the

manuscript

Howard

Fescemyer

Wilfried

Guiblet

Rahulsimham

Vegesna

Monika

Michalovová

Marta

Tomaszkiewicz

Maria

Krasilnikova

and

Samarth

Rangavittal

for

suggestions

on

sequencing

data

analysis

Francesca

Chiaromonte

and

Prabhani

Kuruppumullage

Don

for

statistical

comments

Anton

Nekrutenko

Daniel

Blankenberg

and

Martin

Cech

for

their

help

with

integration

of

our

tools

in

Galaxy

and

the

Huck

Institute

of

the

Life

Sciences

Genomics

Core

FacilityUniversity

Park

for

their

help

with

the

plasmid

sequencing

analyses

This

work

was

supported

in

part

by

NIH

grant

to

KAE

and

KDM

NSF

grant

to

KDM

Penn

State

Go

to

Footnotes

Supplemental

material

is

available

for

this

article

Article

published

online

before

print

Article

supplemental

material

and

publication

date

are

at

Go

to

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