Prediction

of

human

mRNA

donor

and

acceptor

sites

from

the

DNA

sequence

Soren

Brunak

Jacob

Engelbrecht

and

Steen

Knudseny

Department

of

Structural

Properties

of

Materials

The

Technical

University

of

Denmark

Lyngby

Denmark

y

Molecular

Biology

Computer

Research

Resource

Dana

-LCB-

Farber

Cancer

Institute

Harvard

School

of

Public

Health

Binney

St.

Boston

MA

USA

Abstract

Artificial

neural

networks

have

been

applied

to

the

prediction

of

splice

site

location

in

human

pre

-LCB-

mRNA

A

joint

prediction

scheme

where

prediction

of

transition

regions

between

introns

and

exons

regulates

a

cutoff

level

for

splice

site

assignment

was

able

to

predict

splice

site

locations

with

confidence

levels

far

better

than

previously

reported

in

the

literature

The

problem

of

predicting

donor

and

acceptor

sites

in

human

genes

is

hampered

by

the

presence

of

numerous

amounts

of

false

positives

in

the

paper

the

distribution

of

these

false

splice

sites

is

examined

and

linked

to

a

possible

scenario

for

the

splicing

mechanism

in

vivo

When

the

presented

method

detects

of

the

true

donor

and

acceptor

sites

it

makes

less

than

false

donor

site

assignments

and

less

than

false

acceptor

site

assignments

For

the

large

data

set

used

in

this

study

this

means

that

on

the

average

there

are

one

and

a

half

false

donor

sites

per

true

donor

site

and

six

false

acceptor

sites

per

true

acceptor

site

With

the

joint

assignment

method

more

than

a

fifth

of

the

true

donor

sites

and

around

one

fourth

of

the

true

acceptor

sites

could

be

detected

without

accompaniment

of

any

false

positive

predictions

Highly

confident

splice

sites

could

not

be

isolated

with

a

widely

used

weight

matrix

method

or

by

separate

splice

site

networks

A

complementary

relation

between

the

confidence

levels

of

the

coding/non

-LCB-

coding

and

the

separate

splice

site

networks

was

observed

with

many

weak

splice

sites

having

sharp

transitions

in

the

coding/non

-LCB-

coding

signal

and

many

stronger

splice

sites

having

more

ill

-LCB-

defined

transitions

between

coding

and

non

-LCB-

coding

Subject

category

Genes

under

the

sub

-LCB-

headings

expression

sequence

and

structure

Journal

of

Molecular

Biology

to

appear

Introduction

Splice

site

selection

in

vertebrate

pre

-LCB-

mRNA

's

-LRB-

see

Green

for

a

review

-RRB-

involves

small

nuclear

ribonucleoproteins

-LRB-

snRNP

's

-RRB-

recognising

sequences

around

the

exon

-LCB-

intron

borders

snRNP

's

recognise

-LRB-

donor

-RRB-

sites

-LRB-

Mount

et

al.

Zhuang

Weiner

-RRB-

and

and

snRNP

's

interact

with

the

end

of

the

intron

-LRB-

Chabot

et

al.

Krainer

Maniatis

Tazi

et

al.

-RRB-

where

a

consensus

signal

-LRB-

acceptor

site

-RRB-

and

a

branch

point

signal

are

located

-LRB-

Keller

Noon

Harris

Senapathy

-RRB-

Numerous

experiments

have

addressed

the

question

of

necessary

and

sufficient

sequence

conditions

for

proper

selection

of

splice

sites

Deletion

of

large

intron

parts

seems

to

have

no

effect

on

the

selection

-LRB-

Wieringa

et

al.

-RRB-

whereas

mutation

or

duplication

of

splice

sites

often

alters

splice

site

selection

-LRB-

Krainer

et

al.

Kuhne

et

al.

-RRB-

Mutation

of

the

splice

site

appears

to

have

an

effect

only

when

the

most

conserved

AG

dinucleotide

is

involved

-LRB-

Reed

Maniatis

-RRB-

Mutations

at

the

site

either

activate

a

nearby

cryptic

site

-LRB-

Treisman

et

al.

-RRB-

or

if

no

suitable

cryptic

site

can

be

located

in

close

proximity

cause

exon

skipping

by

abolishing

splicing

at

the

site

of

the

upstream

intron

as

well

-LRB-

Orkin

-RRB-

The

latter

observation

of

exon

skipping

seems

to

indicate

some

functional

coordination

in

the

recognition

of

the

and

sites

that

surround

an

exon

Truncation

or

elongation

of

an

exon

causes

inhibition

of

splicing

of

the

upstream

intron

by

blocking

splicing

at

the

site

-LRB-

Furdon

Kole

Furdon

Kole

Robberson

et

al.

-RRB-

Thus

the

recognition

of

and

splice

sites

around

internal

exons

may

be

a

concerted

process

in

which

the

exon

length

is

an

important

factor

It

has

been

demonstrated

that

position

effects

are

involved

in

splice

site

selection

-LRB-

Nelson

Green

-RRB-

A

model

of

exon

recognition

would

imply

recognition

of

initial

and

terminal

exons

-LRB-

which

lack

the

and

site

respectively

-RRB-

by

separate

processes

The

apparent

presence

of

consensus

sequences

at

the

and

sites

has

prompted

many

efforts

to

predict

splice

site

selection

based

on

the

primary

DNA

sequence

-LRB-

Ohshima

Gotoh

-RRB-

The

consensus

of

the

site

was

found

to

be

CA

AG/GTGAAGT

-LRB-

Mount

-RRB-

whereas

the

site

and

the

branch

point

are

weakly

defined

by

a

consensus

sequence

-LRB-

Harris

Senapathy

-RRB-

and

seem

to

be

specified

by

an

intervening

polypyrimidine

tract

characterised

by

the

strict

absence

of

AG

dinucleotides

-LRB-

Smith

et

al.

Mengeritsky

Smith

-RRB-

Construction

of

a

finite

state

automaton

as

a

recogniser

of

splice

sites

only

resulted

in

prediction

on

test

genes

-LRB-

Kudo

et

al.

-RRB-

Scoring

and

ranking

schemes

of

potential

and

sites

based

on

nucleotide

weight

tables

have

been

unable

to

locate

the

majority

of

exons

in

small

sets

of

test

genes

-LRB-

Shapiro

Senapathy

Staden

-RRB-

If

more

information

was

included

in

the

scoring

scheme

open

reading

frames

free

energy

of

base

pairing

between

the

snRNP

and

the

mRNA

as

well

as

coding

statistics

the

prediction

of

single

introns

did

rise

to

about

-LRB-

Nakata

et

al.

-RRB-

Simple

neural

network

algorithms

have

been

employed

in

the

recognition

of

and

sites

as

well

as

coding

regions

-LRB-

Lapedes

et

al.

-RRB-

Predictions

on

test

samples

of

true

and

false

sites

have

been

claimed

to

be

correct

to

but

the

method

was

unable

to

correctly

assign

the

exons

of

a

test

gene

Materials

and

Methods

Characteristics

of

the

data

set

From

GenBank

Release

all

annotated

human

sequences

were

extracted

-LRB-

entries

-RRB-

A

subset

of

these

consisting

of

entries

with

IVS

feature

lines

and

which

did

not

contain

the

string

alternative

splic

in

order

to

leave

out

alternative

splice

sites

contained

sequences

From

this

set

entries

were

discarded

-RRB-

if

they

did

not

contain

the

string

complete

cds

as

well

as

the

complete

RNA

transcript

-RRB-

if

they

did

contain

one

intron

only

and

-RRB-

if

they

contained

more

than

one

gene

which

left

the

entries

listed

in

Table

Criterion

-RRB-

ensured

that

all

genes

had

internal

exons

and

-RRB-

had

the

effect

of

removing

two

very

long

sequences

with

a

large

number

of

repeats

Statistics

regarding

base

content

for

translated

exon

-LRB-

E

-RRB-

intron

-LRB-

I

-RRB-

untranslated

exon

-LRB-

M

-RRB-

and

non

-LCB-

transcribed

-LRB-

N

-RRB-

parts

of

the

sequences

are

given

in

Table

Partially

coding

exons

were

thus

in

part

classified

as

-LRB-

E

-RRB-

and

in

part

as

-LRB-

M

-RRB-

Exon

length

distribution

The

data

set

contained

exons

-LRB-

translated

and

untranslated

categories

E

and

M

respectively

-RRB-

with

a

total

of

nucleotides

The

average

length

of

these

exons

was

thus

approximately

bp

Their

length

distribution

peaks

in

the

interval

bp

as

shown

in

Figure

The

lengths

of

the

internal

and

terminal

exons

were

quite

different

as

can

be

seen

in

Figure

The

average

length

of

internal

exons

was

bp

whereas

the

average

length

of

terminal

exons

was

as

large

as

bp

This

is

in

contrast

to

length

distributions

in

genes

of

some

vertebrates

insects

plants

and

fungi

as

reported

in

-LRB-

Hawkins

-RRB-

Data

set

division

The

data

set

used

for

construction

of

prediction

algorithms

and

for

test

contained

the

genes

listed

in

alphabetical

order

in

Table

The

total

data

set

was

divided

into

two

parts

part

I

used

for

training

of

neural

network

algorithms

included

the

first

entries

and

part

II

used

for

testing

the

remaining

entries

The

total

set

of

genes

contained

donor

sites

-LRB-

and

acceptor

sites

-RRB-

with

donor/acceptor

sites

in

part

I

and

donor/acceptor

sites

in

part

II

The

number

of

nucleotides

in

the

first

entries

was

in

the

last

entries

The

full

data

set

was

utilised

for

training

and

test

in

three

different

versions

-RRB-

on

the

set

of

complete

sequences

GenBank

Locus

Length

D

A

GenBank

Locus

Length

D

A

HUMACCYBA

HUMACTGA

HUMADAG

HUMINCP

HUMALBGC

HUMALPHA

HUMKEREP

HUMALPI

HUMLACTA

HUMALPPD

HUMLCATG

HUMANFA

HUMAPOAIT

HUMMIS

HUMAPOCIA

HUMAPOCIB

HUMAPOCII

HUMAPRTA

HUMMYCC

HUMCACY

HUMMYCRT

HUMCAPG

HUMOPS

HUMCFVII

HUMOTNPI

HUMCKMT

HUMPCNA

HUMPGAMMG

HUMCSFGMA

HUMPLAPL

HUMCSPB

HUMPNMTA

HUMPOMC

HUMCYCAA

HUMPPPA

HUMCYPIIE

HUMPRCA

HUMDES

HUMERPA

HUMFABP

HUMSAA

HUMSAACT

HUMSPRO

HUMGASTA

HUMTFPB

HUMGG

HUMTKRA

HUMGHN

HUMTNFA

HUMTPA

HUMTUBAG

HUMTUBBM

HUMHST

HUMUBILP

HUMVPNP

Table

The

entries

from

GenBank

Release

used

in

this

study

After

the

name

follows

the

length

in

nucleotides

The

number

of

donor

-LRB-

D

-RRB-

and

acceptor

sites

-LRB-

A

-RRB-

is

listed

for

each

entry

The

total

number

of

donor

and

acceptor

sites

was

All

entries

contained

one

gene

only

A

single

donor

site

-LRB-

from

HUMAPRTA

-RRB-

did

not

conform

to

the

standard

GT

rule

in

that

C

was

substituted

for

T

A

blank

line

separates

entries

in

part

I

from

entries

in

part

II

defined

in

section

Class

Bp

count

A

C

G

T

E

I

M

N

S

Table

The

base

distribution

in

the

entries

listed

in

Table

given

for

translated

exon

-LRB-

E

-RRB-

intron

-LRB-

I

-RRB-

untranslated

exon

-LRB-

M

-RRB-

and

non

-LCB-

transcribed

-LRB-

N

-RRB-

DNA

separately

S

denotes

the

complete

sample

The

entries

contained

in

all

nucleotides

of

which

were

unknown

Note

that

the

nucleotides

C

and

G

are

overabundant

in

the

translated

exon

class

whereas

A

and

T

are

underabundant

200 400 600 800

EXONS

LENGTH

INTERVALS

BP

WIDE

Figure

Distribution

of

lengths

of

the

exons

in

the

total

data

set

Each

channel

has

a

length

interval

of

bp

The

distribution

peaks

in

the

length

interval

bp

The

last

interval

includes

all

lengths

bp

200 400 600 800

INTERNAL

EXONS

LENGTH

INTERVALS

BP

WIDE

200 400 600 800

TERMINAL

EXONS

LENGTH

INTERVALS

BP

WIDE

Figure

Distribution

of

lengths

of

the

internal

-LRB-

upper

-RRB-

and

terminal

-LRB-

lower

-RRB-

exons

Each

channel

has

a

length

interval

of

bp

Terminal

exons

are

with

a

few

exceptions

always

longer

than

bp

The

distribution

for

terminal

exons

peaks

in

the

length

interval

bp

The

last

interval

includes

all

lengths

bp

-RRB-

on

a

reduced

data

set

of

sequence

segments

extracted

symmetrically

around

donor

and

acceptor

sites

with

a

range

of

nucleotides

Each

sequence

segment

contained

nucleotides

and

part

I

of

the

reduced

donor

and

acceptor

site

data

sets

thus

contained

segments

with

a

total

of

nucleotides

and

part

II

correspondingly

nucleotides

-RRB-

on

a

set

of

truncated

but

otherwise

complete

sequences

This

data

set

version

was

created

in

order

to

take

into

account

the

fact

that

a

number

of

GenBank

entries

may

contain

parts

of

adjacent

genes

In

the

truncated

data

set

version

nucleotides

outside

a

distance

of

bp

to

the

left

and

to

the

right

of

the

coding

region

were

removed

from

each

entry

This

removal

mainly

had

the

effect

of

reducing

the

initial

part

of

the

sequences

as

most

entries

only

include

small

regions

beyond

the

end

of

the

mature

pre

-LCB-

mRNA

The

truncation

lowered

the

number

of

nucleotides

to

bp

in

part

I

and

to

bp

in

part

II

against

and

in

the

sets

of

complete

sequences

The

percentage

of

nucleotides

in

the

E

exon

category

did

increase

from

in

the

original

data

set

to

in

the

truncated

version

Sequence

similarity

The

alphabetical

order

was

maintained

when

dividing

the

data

set

into

training

and

test

in

order

to

avoid

strong

similarities

between

genes

in

the

two

parts

The

reduced

donor

and

acceptor

site

data

sets

were

searched

for

sequence

similarities

with

the

neural

network

metric

method

-LRB-

Bohr

et

al.

-RRB-

measuring

the

similarity

between

each

of

the

sequence

segments

in

part

II

and

the

sequence

segments

in

part

I

A

comparison

between

this

measure

and

the

measure

of

the

Protein

Identification

Resource

-LRB-

PIR

-RRB-

-LRB-

George

et

al.

-RRB-

can

be

found

in

-LRB-

Bohr

et

al.

Petersen

et

al.

-RRB-

With

the

four

-LCB-

letter

nucleotide

alphabet

the

overlap

between

any

two

random

sequences

is

In

the

present

case

donor

site

sequence

segments

shared

the

central

dinucleotide

GT

acceptor

site

sequence

segments

the

dinucleotide

AG

and

accordingly

the

overlap

between

otherwise

uncorrelated

strings

of

length

nucleotides

was

close

to

instead

We

measured

the

overlaps

between

each

of

the

test

set

sequence

segments

in

part

II

and

each

of

the

sequence

segments

in

the

training

set

-LRB-

part

I

-RRB-

and

only

of

the

maximal

overlaps

between

a

donor

site

sequence

segment

in

part

II

and

part

I

exceeded

exceeded

in

the

acceptor

site

case

exceeded

and

exceeded

from

which

we

concluded

that

the

alphabetically

determined

division

in

training

and

test

was

quite

effective

in

avoiding

unsatisfactorily

large

sequence

similarities

Another

factor

which

effectively

reduced

the

level

of

sequence

similarity

was

the

fact

that

the

data

set

contained

complete

coding

sequences

only

A

large

number

of

the

GenBank

entries

represent

small

fractions

of

other

entries

containing

complete

sequences

The

few

large

overlaps

between

segments

in

part

I

and

II

together

with

average

overlaps

and

standard

deviations

are

given

in

Table

-LRB-

donor

site

sequence

segments

-RRB-

and

in

Table

-LRB-

acceptor

GenBank

Locus

-LRB-

II

-RRB-

Mean

oe

Max

GenBank

Locus

-LRB-

I

-RRB-

HUMPLAPL

HUMALPPD

HUMPLAPL

HUMALPHA

HUMPLAPL

HUMALPHA

HUMPPPA

HUMSAACT

HUMACTGA

HUMSPRO

HUMSPRO

HUMTFPB

HUMALBGC

HUMTUBAG

HUMVPNP

HUMHST

Table

Sequence

similarity

exceeding

among

the

donor

site

sequence

segments

in

the

part

II

and

the

donor

site

sequence

segments

in

part

I.

Listed

is

the

GenBank

locus

the

mean

value

for

the

overlaps

the

standard

deviation

oe

the

largest

overlap

and

the

GenBank

locus

for

the

sequence

segment

with

the

largest

overlap

for

each

of

the

segments

Only

of

the

maximal

overlaps

exceed

site

sequence

segments

-RRB-

Dinucleotide

propensities

If

nucleotides

in

genes

were

distributed

randomly

all

pairs

of

nucleotides

would

occur

with

the

same

probability

i.e.

in

of

the

cases

In

Table

the

actual

distribution

of

GT

and

AG

dinucleotides

in

the

five

functional

categories

is

given

for

the

total

data

set

of

genes

GT

dinucleotides

were

most

abundant

in

introns

against

on

the

average

while

AG

dinucleotides

were

most

abundant

in

translated

exons

and

in

introns

against

on

the

average

The

total

set

of

sequences

contained

GT

dinucleotides

and

AG

dinucleotides

whereas

the

test

set

contained

GT

and

AG

dinucleotides

These

large

numbers

are

to

be

compared

with

the

small

numbers

of

true

donor/acceptor

sites

and

in

part

I+II

and

II

respectively

A

characteristic

of

the

polypyrimidine

tract

upstream

from

the

true

acceptor

sites

was

that

the

dinucleotides

TT

TC

CT

and

CC

gave

different

contributions

with

high

probabilities

of

CT

and

CC

extending

much

further

-LRB-

in

the

interval

fifteen

to

thirty

nucleotides

away

from

the

acceptor

site

-RRB-

than

those

of

TT

and

TC

We

have

computed

the

Kullback

divergence

for

single

nucleotides

-LRB-

Kullback

-RRB-

quantifying

the

contrast

between

the

surroundings

of

GT/AG

sites

functioning

as

splice

sites

and

those

not

functioning

as

splice

sites

in

an

interval

of

thirty

nucleotides

to

each

side

of

the

sites

We

found

these

to

be

in

accordance

with

the

findings

of

Lapedes

et

al.

with

Kullback

divergence

in

the

acceptor

site

case

residing

mostly

in

twenty

nucleotides

at

the

end

of

the

introns

-LRB-

the

polypyrimidine

tract

-RRB-

and

in

the

donor

GenBank

Locus

-LRB-

II

-RRB-

Mean

oe

Max

GenBank

Locus

-LRB-

I

-RRB-

HUMPLAPL

HUMALPHA

HUMPLAPL

HUMALPHA

HUMPLAPL

HUMALPHA

HUMPPPA

HUMTKRA

HUMTKRA

HUMTNFA

HUMERPA

HUMTPA

HUMFABP

Table

Sequence

similarity

exceeding

among

the

acceptor

site

sequence

segments

in

the

part

II

and

the

acceptor

site

sequence

segments

in

part

I.

Listed

is

the

GenBank

locus

the

mean

value

for

the

overlaps

the

standard

deviation

oe

the

largest

overlap

and

the

GenBank

locus

for

the

sequence

segment

with

the

largest

overlap

for

each

of

the

segments

Only

of

the

maximal

overlaps

exceed

Class

GT

AG

E

I

M

N

S

Table

The

GT/AG

dinucleotide

distribution

in

the

entries

listed

in

Table

given

for

translated

exon

-LRB-

E

-RRB-

intron

-LRB-

I

-RRB-

untranslated

exon

-LRB-

M

-RRB-

and

non

-LCB-

transcribed

-LRB-

N

-RRB-

DNA

separately

S

denotes

the

complete

sample

INFORMATION

INFORMATION

POSITION

Figure

The

Shannon

information

for

GT

sites

-LRB-

upper

-RRB-

and

AG

sites

-LRB-

lower

-RRB-

splice

sites

-LRB-

ffl

-RRB-

and

non

-LCB-

splice

sites

-LRB-

-RRB-

in

the

GenBank

entries

listed

in

Table

The

central

G

in

each

site

is

surrounded

by

thirty

bases

in

both

directions

The

unit

of

information

is

bits

per

nucleotide

site

case

in

six

bases

on

the

intron

side

and

in

three

bases

on

the

exon

side

We

also

computed

the

Kullback

divergence

for

all

the

sixteen

dinucleotides

and

the

sixty

-LCB-

four

trinucleotides

and

found

it

to

be

residing

in

roughly

the

same

intervals

The

variation

of

the

Shannon

information

-LRB-

Shannon

-RRB-

is

shown

in

Figure

for

both

GT

sites

-LRB-

donor/non

-LCB-

donor

-RRB-

and

AG

sites

-LRB-

acceptor/non

-LCB-

acceptor

-RRB-

where

a

value

of

bits/nucleotide

indicates

complete

randomness

The

context

of

the

GT/AG

non

-LCB-

splice

sites

was

apart

from

small

deviations

stemming

from

minor

asymmetries

in

the

overall

dinucleotide

probabilities

completely

random

whereas

the

context

of

the

true

splice

sites

showed

a

non

-LCB-

randomness

exactly

matching

the

one

found

by

inspecting

the

Kullback

divergence

Architecture

of

the

neural

networks

The

neural

networks

used

in

this

investigation

were

of

the

feedforward

type

They

received

input

from

windows

scanning

the

DNA

sequence

each

window

configuration

was

represented

numerically

as

a

binary

string

see

Figure

and

below

for

details

A

G

G

C

A

C

C

A

G

G

T

A

G

G

G

G

A

G

C

Figure

Architecture

of

the

feed-forward

neural

network

used

for

classifying

the

middle

nucleotide

in

one

of

two

categories

the

dichotomy

splicing

donor

site

or

non

-LCB-

splicing

donor

site

or

splicing

acceptor

site

or

non

-LCB-

splicing

acceptor

site

or

coding

-LRB-

E

-RRB-

or

non

-LCB-

coding

-LRB-

IMN

-RRB-

The

particular

architecture

shown

has

nineteen

nucleotides

visible

to

it

in

the

input

layer

twenty

non

-LCB-

linear

hidden

units

and

one

output

unit

The

input

layer

encoded

each

DNA

window

configuration

as

a

binary

string

with

bits

representing

nineteen

blocks

of

four

elements

an

A

by

the

binary

pattern

T

by

G

by

and

C

by

The

bits

were

broadcasted

to

the

twenty

non

-LCB-

linear

processing

units

interfacing

the

input

layer

and

the

output

layer

Each

of

the

twenty

units

computed

a

weighted

sum

with

terms

and

subsequently

this

sum

-LRB-

subtracted

an

internal

threshold

value

-RRB-

was

made

the

argument

of

a

non

-LCB-

linear

sigmoid

function

giving

a

real

number

limited

downwards

by

zero

and

upwards

by

one

From

these

twenty

reals

the

single

unit

in

the

output

layer

similarly

computed

a

real

number

The

activity

of

the

output

unit

was

compared

with

a

cutoff

value

separating

the

two

output

category

assignments

from

each

other

Notice

that

only

connections

branching

off

from

the

first

and

the

last

unit

in

each

layer

are

shown

on

the

figure

This

architecture

contained

weights

and

internal

thresholds

making

a

total

of

adjustable

parameters

The

networks

were

internally

equipped

with

processing

elements

which

performed

simple

computations

summing

up

input

values

Ij

each

of

which

were

multiplied

by

a

specific

weight

factor

Wj

subtracting

from

this

sum

an

internal

threshold

value

t

and

subsequently

making

the

result

the

argument

of

a

non

-LCB-

linear

function

possessing

a

sigmoidal

-LRB-

i.e.

S

-LCB-

shaped

-RRB-

form

The

value

of

the

latter

represented

a

real

-LCB-

numbered

activity

limited

downwards

by

zero

and

upwards

by

one

O

oe

-LRB-

NX

j

WjIj

t

-RRB-

-LRB-

-RRB-

where

oe

-LRB-

x

-RRB-

-LRB-

exp

-LRB-

x

-RRB-

-RRB-

and

N

the

number

of

inputs

One

layer

of

connections

in

these

neural

networks

connected

the

input

layer

with

a

hidden

layer

of

non

-LCB-

linear

processing

elements

and

a

second

layer

of

connections

connected

the

hidden

layer

with

the

output

layer

The

size

in

nucleotides

Sw

of

the

window

into

the

DNA

sequence

was

in

the

range

from

to

and

the

number

of

hidden

units

Sh

in

the

range

from

to

The

binary

strings

encoding

the

window

configuration

was

composed

of

Sw

blocks

of

four

elements

an

A

being

represented

by

the

binary

pattern

T

by

G

by

and

C

by

The

unknown

nucleotides

were

represented

by

In

the

output

layer

one

processing

element

represented

the

assignment

of

the

middle

nucleotide

in

the

input

window

by

the

patterns

and

-LRB-

In

the

actual

implementation

of

the

algorithm

binary

was

represented

by

and

binary

by

in

order

to

speed

up

the

convergence

of

the

training

process

-RRB-

The

binary

representation

is

similar

to

the

one

used

in

the

NETtalk

network

-LRB-

Sejnowski

Rosenberg

-RRB-

which

assigned

phonemes

to

alphabetic

letters

of

English

text

and

the

one

used

in

networks

-LRB-

Qian

Sejnowski

Bohr

et

al.

Holley

Karplus

Kneller

et

al.

-RRB-

which

classifies

amino

acids

into

categories

of

their

corresponding

protein

secondary

structure

alpha-helix

beta-sheet

and

coil

A

similar

sparse

coding

scheme

was

applied

in

a

network

predicting

distance

constraints

on

the

three-dimensional

structure

of

protein

backbones

-LRB-

Bohr

et

al.

-RRB-

During

training

the

network

was

exposed

to

input

window

configurations

of

size

Sw

and

the

corresponding

output

category

assignments

and

the

strengths

of

the

connections

were

modified

-LRB-

Rumelhart

et

al.

-RRB-

so

that

the

numerical

value

of

the

output

neuron

gradually

approached

the

desired

value

or

depending

on

the

window

configuration

in

question

Each

connection

say

between

neuron

i

and

j

was

modified

by

a

small

amount

proportional

to

the

negative

gradient

of

the

error

made

by

the

network

Wij

ffi

E

ffi

Wij

-LRB-

-RRB-

where

the

total

network

error

E

was

the

sum

of

the

squared

deviations

from

the

desired

output

values

E

X

ff

X

k

-LRB-

Offk

Dffk

-RRB-

-LRB-

-RRB-

ff

enumerated

the

input

window

configurations

and

k

the

output

neurons

The

input

window

was

sequentially

shifted

one

nucleotide

at

a

time

down

the

sequence

of

each

gene

The

total

gene

training

set

-LRB-

part

I

-RRB-

consisted

of

input

window

configurations

the

gene

test

set

-LRB-

part

II

-RRB-

of

The

reduced

sequence

segment

data

sets

contained

and

configurations

respectively

For

a

network

with

Sw

nucleotides

in

the

window

possible

window

configurations

exist

a

number

that

grows

rapidly

with

increasing

window

size

for

Sw

for

Sw

and

so

on

The

goal

of

the

training

was

by

adjusting

the

network

parameters

to

make

an

internal

representation

of

the

systematics

in

the

mapping

between

the

set

of

window

configurations

in

part

I

and

the

two

output

categories

that

did

generalise

well

to

the

corresponding

mapping

in

part

II

During

a

training

session

the

success

of

the

training

was

monitored

in

two

different

ways

one

concerning

the

training

set

as

a

whole

another

concerning

each

window

configuration

separately

The

performance

on

the

training

set

as

a

whole

was

quantified

by

monitoring

the

decrease

in

the

total

network

error

E

If

E

remained

constant

for

a

large

number

of

presentations

of

the

training

set

it

indicated

that

no

improvements

would

be

gained

from

further

training

The

network

had

learnt

a

single

window

configuration

if

the

real

-LCB-

numbered

activity

of

the

output

neuron

was

falling

on

the

same

side

of

a

cutoff

value

as

the

training

target

of

the

window

configuration

in

question

The

cutoff

value

separating

the

two

output

category

assignments

from

each

other

was

mostly

chosen

to

be

Thus

at

any

moment

during

training

we

had

an

objective

criterion

for

successful

learning

a

criterion

pointing

uniquely

to

those

inputs

not

being

classified

correctly

In

a

previous

study

-LRB-

Brunak

et

al.

b

-RRB-

it

was

shown

how

the

examination

of

the

unlearnable

window

configurations

led

to

the

detection

of

wrongly

assigned

donor

sites

due

to

insufficient

proofreading

of

databank

entries

as

well

as

erroneous

interpretation

of

experiments

Information

about

the

degree

of

regularity

of

for

example

donor

site

window

configurations

could

be

obtained

by

monitoring

the

course

of

the

training

For

the

donor

site

assignment

problem

window

configurations

learned

early

in

the

training

process

showed

stronger

conformity

to

the

standard

consensus

sequence

CA

AG/GTGAAGT

than

those

being

learned

late

see

section

and

Table

for

details

Evaluation

measures

For

each

of

the

three

classification

tasks

dealt

with

in

this

paper

the

distribution

over

categories

was

highly

asymmetric

The

non

-LCB-

donor

sites

largely

outnumbered

the

splicing

donor

sites

the

non

-LCB-

acceptor

sites

largely

outnumbered

the

splicing

acceptor

sites

and

in

the

coding

-LRB-

E

-RRB-

or

non

-LCB-

coding

-LRB-

IMN

-RRB-

problem

we

also

had

a

skewed

distribution

although

to

a

smaller

extent

As

a

consequence

of

this

imbalance

mere

percentages

of

correctly

predicted

window

configurations

are

bad

indicators

of

the

predictive

performance

An

algorithm

which

assigns

donor

sites

to

all

the

nucleotides

in

a

gene

will

correctly

predict

the

presence

of

donor

sites

with

an

accuracy

of

but

is

not

of

much

use

An

alternative

measure

which

takes

the

relation

between

correctly

predicted

positives

and

negatives

as

well

as

false

positives

and

negatives

into

account

is

the

correlation

coefficient

-LRB-

Mathews

-RRB-

C

-LRB-

X

-RRB-

-LRB-

PXNX

-RRB-

-LRB-

NfXP

fX

-RRB-

q

-LRB-

NX

NfX

-RRB-

-LRB-

NX

P

fX

-RRB-

-LRB-

PX

NfX

-RRB-

-LRB-

PX

P

fX

-RRB-

-LRB-

-RRB-

where

X

can

be

any

of

the

categories

E

I

ID

IA

M

or

N.

PX

and

NX

are

the

correctly

predicted

positives

and

negatives

and

P

fX

and

NfX

are

similarly

the

incorrectly

predicted

positives

and

negatives

A

perfect

prediction

gives

C

-LRB-

X

-RRB-

whereas

a

truly

imperfect

one

gives

C

-LRB-

X

-RRB-

This

measure

as

well

as

the

relation

between

the

number

of

true

and

false

positives

will

be

used

to

quantify

the

performance

of

the

algorithms

in

the

present

paper

Results

Donor

sites

The

ability

of

the

network

method

to

predict

the

function

of

a

nucleotide

as

being

splicing

donor

or

not

was

assessed

by

training

networks

of

widely

different

architectures

Perceptrons

with

and

hidden

units

were

used

all

with

and

nucleotides

visible

to

them

in

the

input

layer

For

the

reduced

data

set

of

sequence

segments

the

result

of

runs

with

these

parameters

is

shown

in

Figures

and

Figure

shows

the

maximal

test

correlation

coefficient

Cmax

-LRB-

D

-RRB-

obtained

during

presentations

of

the

sequence

segments

in

the

training

set

The

corresponding

test

set

contained

the

sequence

segments

from

part

II

each

with

a

length

of

nucleotides

nucleotides

before

and

after

the

donor

site

For

the

networks

with

hidden

units

Cmax

-LRB-

D

-RRB-

was

in

the

interval

fifteen

nucleotides

and

forty

hidden

units

being

superior

with

Cmax

-LRB-

D

-RRB-

This

network

correctly

classified

of

the

donor

sites

-LRB-

-RRB-

and

of

the

non

-LCB-

donor

sites

-LRB-

-RRB-

The

probability

for

false

negatives

was

thus

as

low

as

and

for

false

positives

even

lower

The

optimal

network

trained

on

sequence

segments

-LRB-

Sw

hidden

units

-RRB-

had

a

C

-LRB-

D

-RRB-

when

tested

on

the

complete

sequences

in

part

II

of

the

data

set

The

network

again

correctly

classified

of

the

donor

sites

-LRB-

-RRB-

but

incorrectly

classified

of

the

non

-LCB-

donor

sites

C

-LRB-

D

-RRB-

NUMBER

OF

HIDDEN

UNITS

Figure

The

maximal

test

correlation

coefficient

Cmax

-LRB-

D

-RRB-

as

function

of

the

number

of

perceptron

hidden

units

for

donor

site

sequence

segments

Eight

curves

show

the

results

for

perceptron

's

with

and

nucleotides

in

the

window

C

-LRB-

D

-RRB-

WINDOW

SIZE

Figure

The

maximal

test

correlation

coefficient

Cmax

-LRB-

D

-RRB-

as

function

of

window

size

in

nucleotides

for

perceptron

's

without

hidden

units

obtained

on

donor

site

sequence

segments

For

the

eight

window

sizes

-LRB-

Sw

and

-RRB-

Cmax

-LRB-

D

-RRB-

is

non

-LCB-

zero

C

-LRB-

D

-RRB-

WINDOW

SIZE

Figure

The

maximal

test

correlation

coefficient

Cmax

-LRB-

D

-RRB-

on

the

complete

sequences

obtained

during

presentations

of

the

complete

sequences

as

function

of

window

size

in

nucleotides

for

perceptron

's

with

hidden

units

as

being

donor

sites

The

number

of

GT

dinucleotides

in

part

II

was

so

the

network

still

reduced

the

number

of

possible

donor

site

candidates

considerably

The

false

positive

donor

sites

were

not

distributed

at

random

in

the

sequences

in

section

and

the

distributions

of

false

positive

donor

and

acceptor

sites

are

analysed

in

detail

For

the

truncated

version

of

the

data

set

of

complete

sequences

the

number

of

false

positives

was

reduced

to

giving

C

-LRB-

D

-RRB-

The

results

for

training

and

testing

on

complete

sequences

are

given

in

Figure

The

number

of

hidden

units

was

kept

fixed

at

forty

The

best

Cmax

-LRB-

D

-RRB-

was

obtained

by

a

window

size

of

nucleotides

Cmax

-LRB-

D

-RRB-

Out

of

possible

ones

the

network

made

only

false

positive

assignments

but

of

the

donor

sites

was

incorrectly

classified

as

being

non

-LCB-

donor

sites

the

recognition

percentage

being

Information

about

the

degree

of

regularity

of

donor

site

window

configurations

was

obtained

by

monitoring

the

order

in

which

the

networks

learned

to

assign

window

configurations

correctly

In

this

way

classes

of

donor

sites

could

be

identified

showing

a

decreasing

degree

of

complementarity

to

the

nucleotides

of

the

The

largest

cluster

comprised

sequences

conforming

to

the

standard

consensus

sequence

CA

AG/GTGAAGT

as

well

as

sequences

with

substitution

at

position

on

the

intron

side

In

primates

sequences

with

substitution

at

position

are

close

to

being

as

frequent

as

sequences

with

complete

consensus

agreement

-LRB-

Jacob

Gallinaro

-RRB-

In

Table

it

is

shown

how

donor

site

window

configurations

learned

early

in

the

training

process

conformed

more

strongly

to

the

standard

consensus

sequence

than

those

being

learned

late

A

central

substring

like

GACCCAG/GTGAGTGG

from

HUMACCYBA

was

learned

earlier

than

for

example

GGAGTTT/GTAAGCTC

from

HUMGHN

which

is

further

from

the

consensus

sequence

The

single

donor

site

from

HUMAPRTA

which

did

not

conform

to

the

standard

GT

rule

-LRB-

C

substituted

for

T

-RRB-

was

extremely

hard

to

learn

A

secondary

factor

influencing

the

order

in

the

training

was

the

presence

of

non

-LCB-

splicing

donor

window

configurations

closely

resembling

the

true

splicing

donor

window

configurations

The

presence

of

these

almost

conflicting

window

configurations

did

push

the

input-output

relation

of

the

networks

towards

increased

non

-LCB-

linearity

which

was

harder

to

obtain

by

the

infinitesimal

adjustment

of

network

parameters

-LRB-

-RRB-

Networks

trained

on

window

configurations

deprived

of

either

the

left

or

the

right

context

showed

considerable

differences

in

the

ability

to

learn

the

donor

site

assignment

task

Networks

deprived

of

the

left

context

learned

much

faster

than

networks

deprived

of

the

right

context

which

indicated

that

correlations

in

the

base

composition

in

the

intron

part

is

much

more

important

in

the

donor

site

recognition

than

the

base

composition

in

the

exon

part

This

observation

is

in

agreement

with

the

magnitudes

of

the

Kullback

divergence

and

the

Shannon

information

given

in

section

A

similar

picture

emerged

in

the

acceptor

site

assignment

case

False

donor

sites

We

have

examined

the

densities

of

false

positive

donor

sites

in

the

functionally

different

parts

of

the

total

data

set

For

the

optimal

network

trained

on

donor

site

sequence

segments

and

tested

on

the

total

data

set

-LRB-

part

I

and

II

in

all

bp

-RRB-

the

average

density

was

false

positives

per

bp

ranging

from

in

translated

exon

-LRB-

E

-RRB-

in

untranslated

exon

-LRB-

M

-RRB-

to

in

intron

-LRB-

I

-RRB-

and

in

non

-LCB-

transcribed

DNA

The

density

was

remarkably

low

in

initial

-LRB-

E/M

-RRB-

exons

close

to

the

average

exon

value

in

internal

exons

-LRB-

E

-RRB-

and

somewhat

higher

in

terminal

-LRB-

E/M

-RRB-

exons

The

robustness

of

the

detection

of

true

donor

sites

can

be

assessed

from

Figure

where

the

effect

of

changing

the

value

of

the

cutoff

for

donor

site

assignment

can

be

observed

A

window

configuration

was

classified

as

splicing

donor

only

if

the

real

-LCB-

numbered

activity

of

the

output

neuron

exceeded

the

cutoff

fixing

the

confidence

level

of

the

assignment

The

numbers

given

in

section

were

obtained

with

a

cutoff

value

of

Larger

correlation

coefficients

could

be

obtained

by

increasing

the

cutoff

value

up

to

around

at

the

cost

of

a

considerable

decrease

in

the

number

of

true

donor

sites

detected

To

investigate

the

possibility

of

a

functional

role

of

false

donor

sites

in

terminal

exons

we

have

for

comparison

plotted

the

probability

of

false

donor

sites

as

a

function

of

the

distance

from

the

last

acceptor

site

in

internal

exons

and

in

terminal

exons

Figure

shows

the

probability

of

the

first

false

positive

donor

site

after

the

acceptor

site

for

internal

exons

-LRB-

upper

part

-RRB-

and

terminal

exons

-LRB-

lower

part

-RRB-

If

the

false

positives

were

distributed

at

random

we

would

expect

a

decreasing

probability

distribution

like

the

one

found

on

the

plot

for

the

internal

exons

The

distribution

on

terminal

exons

initially

shows

an

increase

and

then

a

decreasing

probability

signaling

a

non

-LCB-

random

distribution

of

the

false

positives

The

density

of

false

positive

donor

sites

in

terminal

exons

peaks

in

the

distance

bp

from

the

Epoch

GenBank

Locus

Sequence

TACATCTTCTTTAAAGGTAAGGTTGCTCAACCA

CCTGAAGCTCTCCAAGGTGAGATCACCCTGACG

HUMACCYBA

CCACACCCGCCGCCAGGTAAGCCCGGCCAGCCG

HUMACCYBA

CGAGAAGATGACCCAGGTGAGTGGCCCGCTACC

HUMACTGA

GCGCCCCAGACACCAGGTGAGTGGATGGCGCCG

HUMACTGA

AGAGAAGATGACTCAGGTGAGGCTCGGCCGACG

HUMACTGA

CACCATGAAGATCAAGGTGAGTCGAGGGGTTGG

HUMADAG

TCTTATACTATGGCAGGTAAGTCCATACAGAAG

HUMALPHA

CGTGGCTCTGTCCAAGGTAAGTGCTGGGCTACC

HUMALPI

CCTGGCTCTGTCCAAGGTAAGGGCTGGGCCACC

HUMALPPD

TGTGGCTCTGTCCAAGGTAAGTGCTGGGCTACC

HUMAPRTA

CCTGGAGTACGGGAAGGTAAGAGGGCTGGGGTG

HUMCAPG

GAAGGCTGCCTTCAAGGTAAGGCATGGGCATTG

HUMCFVII

GGAGTGTCCATGGCAGGTAAGGCTTCCCCTGGC

CACCTTGGGCTGCAAGGTGAGAGGCTGATCTCG

CACCTTGGGCTGCAAGGTGAGAGGCTGATCTCG

GTGGCAATGGCTCCAGGTAAGCGCCCCTAAAAT

HUMCSFGMA

AATGTTTGACCTCCAGGTAAGATGCTTCTCTCT

HUMCSPB

AAAGACTTCCTTTAAGGTAAGACTATGCACCTG

GCTACGGACACCTCAGGTGAGCGCTGGGCCGGG

CCTGGGACAGTGAATCGTAAGTATGCCTTTCAC

AAAATGAAGACAGAAGGTGATTCCCCAACCTGA

CGCCACCCTGGACCGGGTGAGTGCCTGGGCTAG

GAGAGTACCAGACCCGGTGAGAGCCCCCATTCC

ACCGTCTCCAGATACGGTGAGGGCCAGCCCTCA

GGGCTGTCTTTCTATGGTAGGCATGCTTAGCAG

CACCGACTGGAAAAAGGTAAACGCAAGGGATTG

HUMACCYBA

GCGCCCCAGGCACCAGGTAGGGGAGCTGGCTGG

HUMACCYBA

CAGCCTTCCTTCCTGGGTGAGTGGAGACTGTCT

HUMACCYBA

CACAATGAAGATCAAGGTGGGTGTCTTTCCTGC

HUMACTGA

TCGCGTTTCTCTGCCGGTGAGCGCCCCGCCCCG

HUMADAG

CTTCGACAAGCCCAAAGTGAGCGCGCGCGGGGG

HUMADAG

TGTCCAGGCCTACCAGGTGGGTCCTGTGAGAAG

HUMADAG

CGAAGTAGTAAAAGAGGTGAGGGCCTGGGCTGG

AACGCAACAGAAATCCGTGAGTGGATGCCGTCT

HUMGHN

AACACAACAGAAATCCGTGAGTGGATGCCTTCT

CTCTAATGCTTCTGATGTAGGTGCTCTGGTTTC

ACCTCCTGCAAGAAGAGTGAGTGTGAGGCCATC

ATACCAGAGTATCTCAGTGAGTATCTCCTTGGC

HUMHST

GCGGACACCCGCGACAGTGAGTGGCGCGGCCAG

HUMLACTA

GACATCTCCTGTGACAGTGAGTAGCCCCTATAA

ATCGAACCAGAGGAGTGTACGCCTGGGCCAGAT

CACCTACCAGGAGTTTGTAAGTTCTTGGGGAAT

HUMGHN

CACCTACCAGGAGTTTGTAAGCTCTTGGGGAAT

HUMALPHA

CAACATGGACATTGATGTGCGACCCCCGGGCCA

HUMCFVII

CTGATCGCGGTGCTGGGTGGGTACCACTCTCCC

HUMADAG

CCTGGAACCAGGCTGAGTGAGTGATGGGCCTGG

HUMAPOCIB

TCCAGCAAGGATTCAGGTTGTTGAGTGCTTGGG

HUMALPHA

CGGGCCAAGAAAGCAGGTGGAGCTGGGGCCCGG

HUMAPRTA

ATCGACTACATCGCAGGCGAGTGCCAGTGGCCG

Table

Donor

site

window

configurations

being

learned

early

and

late

in

the

course

of

training

The

applied

network

was

small

-LRB-

nucleotides

in

the

window

hidden

units

and

output

unit

-RRB-

and

it

was

trained

on

small

bp

segments

surrounding

the

splice

sites

in

part

I

of

the

data

set

Shown

is

the

training

epoch

at

which

a

configuration

was

assigned

splicing

donor

correctly

by

the

network

its

GenBank

locus

and

the

nucleotide

context

surrounding

the

central

G.

Segments

with

large

deviations

from

the

standard

donor

site

consensus

sequence

CA

AG/GTGAAGT

were

learned

only

after

a

relatively

large

number

of

presentations

CUTOFF

ASSIGNMENT

LEVEL

FALSE

TRUE

CORRELATION

Figure

Prediction

of

donor

sites

on

complete

sequences

Shown

are

percentages

of

true

and

false

positive

donor

sites

and

the

correlation

coefficient

C

-LRB-

D

-RRB-

-LRB-

-RRB-

as

function

of

the

donor

site

assignment

cutoff

value

The

transition

in

the

amount

of

false

positives

at

stems

from

the

encoding

of

binary

as

see

section

acceptor

site

The

distribution

of

all

the

false

positives

in

internal

and

terminal

exons

shows

a

similar

behaviour

Acceptor

sites

In

compiling

the

acceptor

site

correlation

coefficients

C

-LRB-

A

-RRB-

we

found

these

to

be

smaller

than

those

of

the

donor

sites

C

-LRB-

D

-RRB-

in

general

as

can

be

seen

from

the

figures

which

correspond

to

figures

of

the

donor

sites

The

optimal

network

architecture

for

predicting

acceptor

sites

embedded

in

sequence

segments

had

a

window

size

of

nucleotides

and

hidden

units

as

opposed

to

the

nucleotides

and

hidden

units

in

the

donor

site

case

Despite

the

variation

in

the

number

of

hidden

units

in

the

perceptrons

and

hence

in

the

amount

of

non

-LCB-

linearity

available

the

decreasing

coverage

of

the

window

configuration

spaces

for

larger

Sw

resulted

in

decreasing

predictive

performance

as

quantified

by

the

correlation

coefficients

The

optimal

window

size

of

nucleotides

covers

precisely

the

polypyrimidine

tract

The

optimal

network

correctly

classified

of

the

acceptor

sites

-LRB-

-RRB-

and

of

the

non

-LCB-

acceptor

sites

-LRB-

-RRB-

the

correlation

coefficient

being

C

-LRB-

A

-RRB-

A

characteristic

of

the

false

negatives

was

that

the

polypyrimidine

tract

was

contaminated

with

purines

In

two

cases

even

with

AG

dinucleo

200 400 600 800

P

-LRB-

D

-RRB-

DISTANCE

FROM

A

SITE

200 400 600 800

P

-LRB-

D

-RRB-

DISTANCE

FROM

LAST

A

SITE

Figure

The

probability

of

the

first

false

donor

site

after

the

acceptor

site

in

internal

exons

-LRB-

upper

-RRB-

and

in

terminal

exons

-LRB-

lower

-RRB-

Probabilities

were

computed

as

the

number

of

incorrectly

assigned

donor

sites

divided

by

the

number

of

possible

ones

Counts

were

merged

in

bp

intervals

C

-LRB-

A

-RRB-

NUMBER

OF

HIDDEN

UNITS

C

-LRB-

A

-RRB-

NUMBER

OF

HIDDEN

UNITS

Figure

The

maximal

test

correlation

coefficient

Cmax

-LRB-

A

-RRB-

as

function

of

the

number

of

perceptron

hidden

units

for

acceptor

site

sequence

segments

Eight

curves

show

the

results

for

perceptron

's

with

and

nucleotides

in

the

window

C

-LRB-

A

-RRB-

WINDOW

SIZE

Figure

The

maximal

test

correlation

coefficient

Cmax

-LRB-

A

-RRB-

as

function

of

window

size

in

nucleotides

for

perceptron

's

without

hidden

units

obtained

on

acceptor

site

sequence

segments

For

the

four

window

sizes

and

Cmax

-LRB-

A

-RRB-

is

zero

and

hence

the

acceptor/non

-LCB-

acceptor

assignment

problem

is

not

linearly

separable

50 100 150 200

C

-LRB-

A

-RRB-

WINDOW

SIZE

Figure

The

maximal

test

correlation

coefficient

Cmax

-LRB-

A

-RRB-

on

complete

sequences

as

function

of

window

size

in

nucleotides

for

perceptron

's

with

hidden

units

The

test

set

comprised

window

configurations

taken

from

the

last

genes

in

the

data

set

tides

being

present

just

to

the

left

of

the

true

acceptor

site

in

as

-LRB-

TCTGGAGCAG

-RRB-

and

in

HUMTKRA

as

-LRB-

GGGAGCACAG

-RRB-

In

a

false

positive

acceptor

site

was

caused

by

an

additional

AG

dinucleotide

just

to

the

left

of

the

true

splice

site

which

itself

was

classified

correctly

-LRB-

CTTCTAGCAG

-RRB-

The

presence

of

these

cases

-LRB-

HUMTKRA

and

-RRB-

contradicts

the

proposed

model

by

Smith

et

al.

of

the

acceptor

site

being

determined

by

a

scanning

process

that

recognises

the

first

AG

located

of

the

polypyrimidine

tract

When

tested

on

the

complete

test

sequences

from

part

II

of

the

data

set

this

network

again

correctly

assigned

the

acceptor

sites

but

incorrectly

assigned

of

the

non

-LCB-

acceptor

sites

bp

in

the

test

sample

as

acceptor

sites

thereby

lowering

the

correlation

coefficient

C

-LRB-

A

-RRB-

to

The

number

of

AG

dinucleotides

in

the

test

sample

was

For

the

truncated

version

of

the

data

set

of

complete

sequences

the

number

of

false

positives

was

reduced

to

giving

again

a

correlation

coefficient

C

-LRB-

A

-RRB-

The

results

for

training

and

testing

on

complete

sequences

are

given

in

Figure

The

best

Cmax

-LRB-

A

-RRB-

was

obtained

by

a

window

size

of

nucleotides

Cmax

-LRB-

A

-RRB-

Out

of

possible

ones

the

network

made

only

false

positive

assignments

but

of

the

acceptor

sites

were

incorrectly

classified

as

being

non

-LCB-

acceptor

sites

the

recognition

percentage

being

as

low

as

Networks

trained

on

complete

sequences

learn

to

be

very

restrictive

in

assigning

acceptor

and

donor

sites

During

training

they

have

been

exposed

to

too

many

examples

inside

introns

and

exons

which

from

a

local

viewpoint

closely

resemble

true

sites

but

which

are

not

selected

by

the

biological

machinery

As

anticipated

no

strong

clustering

of

consensus

-LCB-

like

contexts

did

result

from

monitoring

the

course

of

training

on

acceptor

sites

corresponding

to

the

donor

site

case

as

shown

in

Table

However

a

weaker

pattern

was

definitely

present

in

that

acceptor

sites

surrounded

by

C

and

G

NNNCAGGNNN

were

learned

first

together

with

a

smaller

number

of

sites

having

either

one

of

these

present

NNNCAGNNNN

or

NNNNAGGNNN

It

took

in

general

more

presentations

for

the

networks

to

learn

the

acceptor

site

assignment

when

the

immediate

neighbourhood

contained

T

or

A

This

picture

is

in

agreement

with

the

consensus

sequence

-LRB-

TC

-RRB-

nN

-LRB-

CT

-RRB-

AG/G

described

in

-LRB-

Mount

-RRB-

where

of

the

nucleotides

immediately

to

the

left

of

the

the

conserved

AG

nucleotides

were

found

to

be

C

and

of

the

nucleotides

immediately

to

the

right

were

found

to

be

G

The

network

had

severe

difficulties

in

learning

the

two

non

-LCB-

AG

acceptor

site

assignments

in

the

GenBank

entry

-LRB-

acc

no

-RRB-

Consultation

of

the

original

publication

-LRB-

Matsuura

et

al.

-RRB-

revealed

that

the

GenBank

assignment

at

position

is

in

conflict

with

the

assignment

at

position

in

the

paper

which

agrees

with

the

assignment

made

by

the

network

Furthermore

the

assignment

of

a

splicing

acceptor

at

position

in

the

paper

does

not

seem

to

be

supported

by

the

chicken

cDNA

clone

-LRB-

Kishi

et

al.

-RRB-

used

by

Matsuura

et

al.

to

make

the

assignment

The

human

and

chicken

genes

have

a

rather

low

degree

of

sequence

similarity

in

this

region

The

network

assigns

position

as

splicing

acceptor

This

must

however

be

confirmed

by

experiment

False

acceptor

sites

The

densities

of

false

positive

acceptor

sites

in

the

functionally

different

parts

of

the

data

set

were

in

general

higher

than

in

the

donor

site

case

For

networks

trained

on

acceptor

site

sequence

segments

and

tested

on

the

total

data

set

-LRB-

part

I

and

II

in

all

bp

-RRB-

the

average

density

was

false

positives

per

bp

ranging

from

in

translated

exon

-LRB-

E

-RRB-

in

intron

-LRB-

I

-RRB-

to

in

untranslated

exon

-LRB-

M

-RRB-

and

in

non

-LCB-

transcribed

DNA

The

density

in

initial

-LRB-

E/M

-RRB-

exons

was

in

internal

exons

-LRB-

E

-RRB-

and

in

terminal

-LRB-

E/M

-RRB-

exons

The

robustness

of

the

detection

of

true

acceptor

sites

can

be

assessed

from

Figure

where

the

effect

of

changing

the

cutoff

for

acceptor

site

assignment

can

be

observed

The

numbers

given

in

section

were

obtained

with

a

cutoff

value

of

Larger

correlation

coefficients

could

be

obtained

by

shifting

the

cutoff

value

to

around

at

the

cost

of

a

considerable

decrease

-LRB-

down

to

ss

-RRB-

in

the

percentage

of

true

acceptor

sites

detected

The

distribution

of

false

positive

acceptor

sites

in

exons

did

not

have

a

strong

non

-LCB-

random

appearance

although

the

density

of

false

positive

acceptor

sites

in

terminal

exons

was

higher

than

in

internal

exons

a

feature

that

also

was

observed

in

the

case

of

the

false

positive

donor

sites

as

described

above

Figure

shows

the

probability

of

the

first

false

positive

acceptor

site

after

the

true

acceptor

site

for

internal

exons

-LRB-

upper

part

-RRB-

and

terminal

exons

-LRB-

lower

part

-RRB-

The

distribution

of

all

the

false

positives

in

internal

and

terminal

exons

shows

a

similar

behaviour

From

inspection

of

predictions

of

networks

trained

to

classify

nucleotides

in

translated

exon

-LRB-

E

-RRB-

or

not

-LRB-

see

section

-RRB-

it

seemed

clear

that

the

exon

-LCB-

signal

from

the

CUTOFF

ASSIGMENT

LEVEL

FALSE

TRUE

CORRELATION

Figure

Prediction

of

acceptor

sites

on

complete

sequences

Shown

are

percentages

of

true

and

false

positive

acceptor

sites

and

the

correlation

coefficient

C

-LRB-

A

-RRB-

-LRB-

-RRB-

as

function

of

the

acceptor

site

assignment

cutoff

value

The

transition

in

the

amount

of

false

positives

at

stems

from

the

encoding

of

binary

as

see

section

coding

part

of

the

terminal

exons

differed

markedly

from

the

exon

-LCB-

signal

of

internal

and

initial

exons

Figure

Large

regions

of

terminal

exons

seemed

to

be

intron

-LCB-

like

especially

in

the

first

third

and

in

this

part

densities

of

false

positive

acceptor

sites

approached

the

average

density

in

introns

Coding/non

-LCB-

coding

classification

The

problem

of

classifying

pieces

of

DNA

sequence

according

to

their

function

as

translated

exon

intron

untranslated

exon

or

other

alternatives

has

been

addressed

by

several

methods

-LRB-

von

Heijne

-RRB-

primarily

either

based

on

the

compilation

of

tables

of

codon

usage

-LRB-

Staden

-RRB-

or

detection

of

local

nonrandomness

in

the

sequence

-LRB-

Fickett

-RRB-

This

problem

is

far

easier

when

sequence

segments

are

pure

-LRB-

Lapedes

-RRB-

that

is

when

they

in

their

entirety

stem

from

one

specific

class

only

Difficulties

arise

when

segments

cover

regions

of

transition

between

the

different

categories

which

in

the

context

of

locating

true

splice

sites

are

of

prime

importance

In

order

to

test

the

network

approach

to

this

problem

networks

were

trained

on

the

truncated

data

set

with

window

sizes

of

and

nucleotides

in

each

case

with

hidden

units

in

them

The

largest

network

contained

adjustable

parameters

Table

gives

figures

for

the

quality

of

the

prediction

for

the

-LRB-

truncated

-RRB-

test

sequences

in

part

II

Both

the

percentage

of

correctly

assigned

window

configurations

in

the

translated

exon

-LRB-

E

-RRB-

category

and

the

correlation

coefficient

C

-LRB-

E

-RRB-

did

increase

with

the

size

of

the

window

up

to

Sw

nucleotides

When

a

network

with

this

size

was

tested

on

part

II

of

the

truncated

data

set

C

-LRB-

E

-RRB-

became

as

high

as

with

of

the

window

configurations

in

the

translated

exon

-LRB-

E

-RRB-

category

correctly

assigned

The

false

positive

non

-LCB-

coding

category

assignments

were

most

frequent

in

small

exons

and

in

the

initial

part

of

terminal

exons

We

also

tried

to

train

networks

to

classify

window

configurations

in

two

categories

where

the

coding

exon

category

-LRB-

E

-RRB-

and

the

non

-LCB-

coding

exon

category

-LRB-

M

-RRB-

were

merged

in

order

to

get

an

EM/IN

assignment

The

result

was

negative

even

networks

with

hidden

units

had

severe

difficulties

in

learning

the

task

indicating

that

similarities

between

window

configurations

in

the

E

and

M

categories

are

very

weak

or

absent

Joint

coding/non

-LCB-

coding

and

splice

site

classification

The

general

picture

concerning

the

confidence

levels

on

the

splice

site

prediction

and

the

E/IMN

classification

-LRB-

as

measured

by

the

output

neuron

activities

-RRB-

was

that

exons

smaller

than

bp

had

rather

weak

-LRB-

E

-RRB-

exon

output

neuron

levels

-LRB-

-RRB-

but

relatively

strong

donor

and

acceptor

site

output

neuron

levels

-LRB-

-RRB-

Conversely

longer

internal

exons

in

general

had

rather

sharp

edges

in

the

output

neuron

activities

with

donor

and

acceptor

site

activities

being

somewhat

weaker

In

order

to

improve

the

ratio

between

true

and

false

donor

and

acceptor

sites

assigned

we

have

exploited

the

complementary

relation

between

200 400 600 800

P

-LRB-

A

-RRB-

DISTANCE

FROM

A

SITE

200 400 600 800

P

-LRB-

A

-RRB-

DISTANCE

FROM

LAST

A

SITE

Figure

The

probability

of

the

first

false

acceptor

site

after

the

acceptor

site

in

internal

exons

-LRB-

upper

-RRB-

and

in

terminal

exons

-LRB-

lower

-RRB-

Probabilities

were

computed

as

the

number

of

incorrectly

assigned

acceptor

sites

divided

by

the

number

of

possible

ones

Counts

were

merged

in

bp

intervals

Window

size

Cmax

-LRB-

E

-RRB-

E

IMN

Table

Quality

of

the

coding

-LRB-

E

-RRB-

or

non

-LCB-

coding

-LRB-

I/M/N

-RRB-

category

prediction

Listed

is

the

maximal

test

correlation

coefficient

Cmax

-LRB-

E

-RRB-

percentages

of

correctly

assigned

window

configurations

for

all

categories

for

the

translated

exon

-LRB-

E

-RRB-

category

and

for

the

category

covering

intron

-LRB-

I

-RRB-

untranslated

exon

-LRB-

M

-RRB-

and

other

-LRB-

N

-RRB-

given

for

each

of

the

five

window

sizes

3000 3200 3400 3600

ACTIVITY

3400 3500 3600 3700

3800 3900 4000

ACTIVITY

POSITION

Figure

Typical

examples

of

the

result

of

the

coding

-LRB-

E

-RRB-

or

non

-LCB-

coding

-LRB-

IMN

-RRB-

prediction

in

terminal

-LRB-

E

-RRB-

exons

The

terminal

exons

shown

are

from

the

GenBank

entries

HUMMYCRT

-LRB-

upper

-RRB-

and

HUMPNMTA

-LRB-

lower

-RRB-

Large

output

neuron

activities

for

the

E

exon

category

commence

at

some

distance

from

the

end

of

many

terminal

exons

in

part

II

of

the

data

set

The

boxes

correspond

to

correct

exons

the

adjoining

lines

to

introns

whereas

the

top

line

gives

the

extension

of

the

transcript

The

network

had

a

window

size

of

nucleotides

and

hidden

units

the

exon

and

the

splice

site

confidence

levels

This

was

done

by

letting

the

exon

signal

control

the

threshold

for

assigning

splice

sites

In

regions

with

abruptly

decreasing

exon

activity

donor

sites

should

be

promoted

and

acceptor

sites

suppressed

and

vice

versa

in

regions

with

abruptly

increasing

exon

activity

In

regions

with

only

small

changes

in

exon

activity

that

is

where

the

level

was

constantly

high

-LRB-

inside

exons

-RRB-

and

where

it

was

constantly

low

-LRB-

inside

introns

-LRB-

I

-RRB-

in

untranslated

exons

-LRB-

M

-RRB-

and

in

intergenic

DNA

-LRB-

N

-RRB-

-RRB-

a

rather

high

confidence

level

in

the

splice

site

assignment

should

be

demanded

in

order

to

suppress

false

positives

To

detect

edges

in

the

E/IMN

output

neuron

levels

we

computed

what

was

essentially

the

first

derivative

of

the

-LRB-

E

-RRB-

exon

output

neuron

activity

by

summing

up

activities

to

the

right

of

a

given

point

and

subtracting

the

corresponding

sum

for

the

left

side

dividing

this

difference

by

the

number

of

addends

In

order

to

reduce

the

number

of

cases

where

the

individual

sums

covered

both

intron

or

exon

regions

we

used

half

the

average

length

of

the

internal

exons

in

the

training

set

bp

as

the

scope

when

summing

giving

derivatives

close

to

in

the

end

of

introns

and

close

to

in

the

end

for

perfect

E/IMN

assignments

In

Figures

and

average

quality

examples

of

the

E/IMN

signal

the

derivative

and

the

forest

of

donor

and

acceptor

site

signals

exceeding

an

activity

of

are

given

for

the

GenBank

entries

HUMOPS

and

HUMUBILP

from

part

II

of

the

data

set

Note

that

some

regions

inside

introns

and

in

the

non

-LCB-

transcribed

part

of

the

sequences

show

exon

-LCB-

like

behaviour

In

compiling

algorithms

which

regulates

the

splice

site

assignment

levels

we

used

the

following

expressions

to

assess

possible

weightings

between

the

strength

of

the

exon

signal

and

the

output

O

from

the

separate

splice

site

networks

if

Odonor

eD

cD

-LRB-

-RRB-

assign

splicing

donor

site

and

if

Oacceptor

eA

cA

-LRB-

-RRB-

assign

splicing

acceptor

site

where

was

computed

as

described

above

The

two

constants

cD

and

cA

are

equivalent

to

the

ordinary

cutoff

assignment

parameters

used

in

figures

and

whereas

eD

and

eA

control

the

impact

of

the

exon

signal

Together

the

four

constants

controlled

the

relative

strengths

between

the

donor

site

and

the

E/IMN

network

on

one

side

and

between

the

acceptor

site

and

E

the

E/IMN

network

on

the

other

The

four

constants

were

optimised

to

give

optimal

performance

in

terms

of

correlation

coefficients

and

in

terms

of

the

percentage

of

true

splice

sites

detected

In

the

upper

part

of

Figure

the

percentage

of

true

positive

donor

sites

detected

is

plotted

against

the

percentage

of

false

positive

donor

sites

assigned

for

complete

sequences

For

a

given

required

level

of

detection

of

true

sites

optimal

cutoff

parameters

was

found

for

-RRB-

the

separate

donor

site

assignment

network

-RRB-

the

nucleotide

weight

matrix

method

of

Staden

-LRB-

Staden

-RRB-

and

-RRB-

the

joint

assignment

method

In

the

upper

1000 2000 3000 4000

5000 6000 7000

ACTIVITY

-LRB-

E

-RRB-

1000 2000 3000 4000

5000 6000 7000

DELTA

1000 2000 3000 4000

5000 6000 7000

ACTIVITY

-LRB-

D

-RRB-

1000 2000 3000 4000

5000 6000 7000

ACTIVITY

-LRB-

A

-RRB-

POSITION

Figure

HUMOPS

prediction

-LRB-

Top

-RRB-

Analog

output

from

the

output

neuron

in

the

E/IMN

network

showing

strong

exon

signals

from

the

correct

exons

Inside

the

introns

and

in

the

terminal

non

-LCB-

transcribed

part

of

the

sequence

a

number

of

regions

show

exon

-LCB-

like

behaviour

The

boxes

correspond

to

correct

exons

the

adjoining

lines

to

introns

whereas

the

top

line

gives

the

extension

of

the

transcript

The

derivative

of

the

analog

E/IMN

output

The

donor

site

activities

-LRB-

-RRB-

from

the

donor

site

assignment

network

-LRB-

Bottom

-RRB-

The

acceptor

site

activities

-LRB-

-RRB-

from

the

acceptor

site

assignment

network

The

variable

cutoff

assignment

level

for

a

detection

of

true

splice

sites

is

shown

as

a

dashed

curve

on

the

pin

-LCB-

diagrams

and

500 1000 1500 2000

2500 3000 3500

ACTIVITY

-LRB-

E

-RRB-

500 1000 1500 2000

2500 3000 3500

DELTA

500 1000 1500 2000

2500 3000 3500

ACTIVITY

-LRB-

D

-RRB-

500 1000 1500 2000

2500 3000 3500

ACTIVITY

-LRB-

A

-RRB-

POSITION

Figure

HUMUBILP

prediction

-LRB-

Top

-RRB-

Analog

output

from

the

output

neuron

in

the

E/IMN

network

showing

strong

exon

signals

from

the

correct

exons

In

the

initial

and

the

terminal

non

-LCB-

transcribed

parts

of

the

sequence

a

number

of

regions

show

exon

-LCB-

like

behaviour

The

boxes

correspond

to

correct

exons

the

adjoining

lines

to

introns

whereas

the

top

line

gives

the

extension

of

the

transcript

The

derivative

of

the

analog

E/IMN

output

The

donor

site

activities

-LRB-

-RRB-

from

the

donor

site

assignment

network

-LRB-

Bottom

-RRB-

The

acceptor

site

activities

-LRB-

-RRB-

from

the

acceptor

site

assignment

network

The

variable

cutoff

assignment

level

for

a

detection

of

true

splice

sites

is

shown

as

a

dashed

curve

on

the

pin

-LCB-

diagrams

and

FALSE

POSITIVES

D

C

-LRB-

D

-RRB-

e

c

TRUE

POSITIVES

D

e

c

Figure

Results

for

testing

donor

site

assignment

on

complete

sequences

The

upper

part

of

the

figure

shows

the

percentage

of

false

positive

donor

sites

as

function

of

the

percentage

of

correctly

predicted

true

positive

donor

sites

The

dashed

line

gives

the

results

for

Staden

's

method

obtained

with

optimal

cutoff

values

for

each

percentage

of

true

sites

detected

the

unbroken

line

the

results

for

the

separate

donor

site

network

with

a

simple

variation

of

the

cutoff

assignment

level

yielding

specific

percentages

of

true

donor

site

detection

The

ffl

line

gives

the

result

for

the

joint

approach

where

the

cutoff

assignment

level

was

controlled

by

the

exon

signal

For

detection

of

true

donor

sites

the

three

percentages

-LRB-

Staden

separate

network

joint

assignment

-RRB-

are

for

for

for

for

for

for

and

for

The

middle

part

of

the

figure

shows

the

corresponding

correlation

coefficients

C

-LRB-

D

-RRB-

for

the

joint

assignment

-LRB-

ffl

-RRB-

and

for

Staden

's

method

-LRB-

dashed

-RRB-

The

lower

part

of

the

figure

shows

the

values

of

the

constants

eD

and

cD

giving

the

smallest

number

of

false

positive

donor

sites

for

the

same

succession

of

points

as

on

the

upper

and

middle

parts

part

of

Figure

the

unbroken

line

represents

the

situation

where

the

cutoff

assignment

level

was

not

controlled

by

the

exon

signal

but

adjusted

so

that

a

specified

percentage

of

true

splice

sites

was

detected

by

the

separate

donor

site

network

The

dashed

line

refers

to

the

method

of

Staden

with

results

obtained

by

determining

optimal

cutoff

values

for

each

percentage

of

true

sites

detected

and

the

ffl

line

gives

the

results

for

the

joint

assignment

Note

that

the

scale

for

the

percentage

of

false

positive

donor

sites

is

logarithmic

The

number

of

false

positives

assigned

was

much

lower

when

the

cutoff

assignment

level

was

controlled

by

the

exon

signal

by

a

factor

between

two

and

thirty

depending

on

the

required

level

of

detection

of

true

sites

When

a

recognition

level

of

for

the

true

donor

sites

was

required

from

the

joint

approach

the

lowest

level

of

false

positives

was

and

C

-LRB-

D

-RRB-

The

optimal

constants

for

obtaining

a

recognition

level

of

was

eD

and

cD

as

can

be

seen

on

the

lower

part

of

Figure

where

the

values

of

eD

and

cD

correspond

to

the

same

succession

of

points

as

on

the

upper

curve

for

the

joint

assignment

A

recognition

level

of

implied

a

level

of

false

positives

close

to

with

C

-LRB-

D

-RRB-

and

optimal

constants

eD

and

cD

For

a

recognition

level

of

no

false

positive

donor

sites

were

assigned

meaning

that

with

the

joint

approach

close

to

one

fifth

of

the

donor

sites

could

be

detected

without

ambiguity

at

all

The

effect

of

the

joint

approach

for

prediction

of

acceptor

sites

is

shown

in

Figure

The

number

of

false

positive

acceptor

sites

assigned

was

again

much

lower

when

the

cutoff

assignment

level

was

controlled

by

the

exon

signal

In

the

interval

where

the

joint

method

did

assign

at

least

one

false

positive

acceptor

site

the

exon

signal

was

able

to

reduce

the

level

of

false

positive

assignments

by

a

factor

between

two

and

forty

For

a

recognition

level

of

no

false

acceptor

sites

were

assigned

at

all

meaning

that

with

the

joint

approach

more

than

a

fourth

of

the

acceptor

sites

could

be

detected

without

ambiguity

at

all

When

the

splice

site

assignment

was

based

on

the

coding/non

-LCB-

coding

network

only

that

is

by

inspection

of

the

values

the

quality

of

the

donor

site

prediction

was

sligthly

worse

than

the

quality

of

Staden

's

method

for

all

detection

levels

of

true

donor

sites

In

the

interval

below

detection

of

true

sites

the

performance

for

acceptor

sites

was

better

than

both

Staden

's

method

and

the

separate

acceptor

site

network

Above

detection

of

true

sites

it

was

about

the

same

For

the

two

examples

in

figures

and

HUMOPS

and

HUMUBILP

the

variable

cutoff

assignment

level

with

a

detection

level

of

true

sites

is

shown

together

with

the

real

-LCB-

numbered

output

from

the

separate

splice

site

networks

It

can

be

observed

how

false

splice

sites

with

confidence

levels

exceeding

are

suppressed

and

how

the

assignment

cutoff

is

lowered

for

true

splice

sites

with

confidence

levels

below

FALSE

POSITIVES

A

C

-LRB-

A

-RRB-

e

c

TRUE

POSITIVES

A

c

e

Figure

Results

for

testing

acceptor

site

assignment

on

complete

sequences

The

upper

part

of

the

figure

shows

the

percentage

of

false

positive

acceptor

sites

as

function

of

the

percentage

of

correctly

predicted

true

positive

acceptor

sites

The

dashed

line

gives

the

results

for

Staden

's

method

obtained

with

optimal

cutoff

values

for

each

percentage

of

true

sites

detected

the

unbroken

line

the

results

for

the

separate

acceptor

site

network

with

a

simple

variation

of

the

cutoff

assignment

level

yielding

specific

percentages

of

true

acceptor

site

detection

The

ffl

line

gives

the

result

for

the

joint

approach

where

the

cutoff

assignment

level

was

controlled

by

the

exon

signal

For

detection

of

true

acceptor

sites

the

three

percentages

-LRB-

Staden

separate

network

joint

assignment

-RRB-

are

for

for

for

for

for

for

and

for

The

middle

part

of

the

figure

shows

the

corresponding

correlation

coefficients

C

-LRB-

A

-RRB-

for

the

joint

assignment

-LRB-

ffl

-RRB-

and

for

Staden

's

method

-LRB-

dashed

-RRB-

The

lower

part

of

the

figure

shows

the

values

of

the

constants

eA

and

cA

giving

the

smallest

number

of

false

positive

acceptor

sites

for

the

same

succession

of

points

as

on

the

upper

and

middle

parts

Conclusion

Artificial

neural

networks

have

been

trained

to

predict

splice

sites

in

human

pre

-LCB-

mRNA

The

problem

of

sorting

the

dinucleotides

GT

and

AG

on

the

basis

of

their

function

is

hard

the

GT

dinucleotide

constitutes

roughly

of

the

dinucleotides

in

human

DNA

but

only

a

very

small

percentage

of

these

belongs

to

the

splicing

donor

category

in

the

order

of

The

dinucleotide

AG

constitutes

roughly

of

all

the

dinucleotides

and

only

around

of

these

function

as

splicing

acceptor

sites

The

results

obtained

in

this

paper

build

on

a

very

conservative

preparation

of

the

data

sets

The

test

set

contained

nearly

of

the

data

extracted

from

GenBank

both

in

terms

of

sequences

specific

sites

and

nucleotides

The

training

and

test

sets

were

searched

for

mutual

sequence

similarities

only

of

maximal

overlaps

did

exceed

for

the

donor

sites

and

only

of

maximal

overlaps

did

exceed

for

the

acceptor

sites

If

all

the

splice

sites

in

the

data

sets

used

in

this

study

were

selected

on

the

basis

of

the

local

sequence

composition

only

we

would

expect

to

be

able

to

model

the

splicing

mechanism

perfectly

given

a

training

set

sufficiently

large

to

match

the

complexity

of

the

splice

site

selection

process

However

when

prediction

of

splicing

donor

sites

was

based

on

local

information

only

-LRB-

the

immediate

neighbourhood

bp

of

a

GT

dinucleotide

-RRB-

the

number

of

false

positive

donor

site

assignments

was

in

the

order

of

five

times

the

number

of

true

positives

with

correlation

coefficients

C

-LRB-

D

-RRB-

at

the

level

of

When

the

donor

site

prediction

based

on

local

information

was

joined

by

a

larger

network

trained

to

recognise

coding

regions

building

on

information

of

a

more

global

nature

the

number

of

false

positive

donor

site

assignments

could

be

reduced

by

a

factor

between

two

and

thirty

resulting

in

correlation

coefficients

at

the

level

of

on

complete

GenBank

sequence

extracts

Both

the

separate

network

and

the

joint

assignment

method

did

perform

considerably

better

than

the

weight

matrix

method

of

Staden

For

a

detection

of

true

donor

sites

Staden

's

method

assigns

false

positives

where

the

separate

network

and

the

joint

method

assigns

and

respectively

For

detection

of

true

donor

sites

the

corresponding

percentages

are

and

The

network

prediction

of

splicing

acceptor

sites

based

only

on

local

information

had

a

worse

performance

with

a

ratio

between

false

and

true

positive

sites

close

to

eight

and

correlation

coefficients

close

to

When

joined

with

the

coding/non

-LCB-

coding

network

the

number

of

false

positive

acceptor

site

assignments

could

be

reduced

by

a

factor

between

two

and

forty

resulting

in

correlation

coefficients

at

the

level

of

on

complete

GenBank

sequence

extracts

Also

for

the

acceptor

site

assignment

problem

both

the

separate

network

and

the

joint

assignment

method

did

perform

considerably

better

than

the

weight

matrix

method

of

Staden

For

a

detection

of

true

acceptor

sites

Staden

's

method

assigns

false

positives

where

the

separate

network

and

the

joint

method

assigns

and

respectively

For

detection

of

true

acceptor

sites

the

corresponding

percentages

are

and

With

the

joint

assignment

method

more

than

a

fifth

of

the

true

donor

sites

and

around

one

fourth

of

the

true

acceptor

sites

could

be

detected

without

accompaniment

of

any

false

positive

predictions

Highly

confident

splice

sites

could

not

be

isolated

with

either

Staden

's

method

or

the

separate

splice

site

networks

A

complementary

relation

between

the

confidence

levels

of

the

coding/non

-LCB-

coding

and

the

separate

splice

site

networks

was

observed

with

many

weak

splice

sites

having

sharp

transitions

in

the

coding/non

-LCB-

coding

signal

and

many

stronger

splice

sites

having

a

more

ill

-LCB-

defined

transition

between

coding

and

non

-LCB-

coding

We

have

not

utilised

the

constraint

of

a

continued

open

reading

frame

in

the

selection

of

compatible

splice

sites

although

this

may

reduce

the

number

of

false

positive

predictions

further

In

the

general

case

where

information

on

the

translation

products

is

not

available

the

combinatorial

problem

is

hard

and

very

vulnerable

to

frameshift

errors

in

the

sequencing

The

joint

assignment

method

was

tested

on

a

new

independent

set

of

twenty

-LCB-

two

sequences

extracted

from

GenBank

release

January

These

sequences

were

not

present

in

release

and

have

not

been

available

to

us

during

development

of

the

algorithm

The

results

were

very

close

to

the

ones

found

for

the

original

test

set

On

the

new

test

set

-LRB-

bp

A/D

sites

-RRB-

the

results

were

a

little

better

for

the

acceptor

sites

and

slightly

worse

for

the

donor

sites

Both

correlation

coefficients

and

percentages

of

false

positive

predictions

had

nearly

the

same

behaviour

as

function

of

the

originally

chosen

constants

eD

cD

and

eA

cA

as

on

Figures

and

The

new

test

set

was

also

screened

for

sequences

similarities

The

difference

in

length

distributions

of

initial

internal

and

terminal

exons

suggests

that

if

exons

are

recognised

as

a

whole

then

different

mechanisms

with

different

steric

constraints

may

be

involved

in

the

recognition

of

the

three

types

of

exons

The

non

-LCB-

random

distribution

of

false

positive

sites

-LRB-

possible

cryptic

sites

-RRB-

in

the

terminal

exons

might

be

of

biological

significance

The

accumulation

of

predicted

false

positive

sites

around

a

bp

distance

from

the

site

can

be

compared

with

the

demonstration

by

Robberson

et

al.

-LRB-

-RRB-

of

inhibition

of

splicing

if

the

terminal

exon

was

extended

beyond

basepairs

The

inhibition

of

splicing

of

the

last

intron

was

reversed

if

a

site

was

included

in

the

extended

terminal

exon

In

conclusion

we

can

not

rule

out

the

existence

of

different

classes

of

splice

sites

or

occasional

interaction

with

cell

-LCB-

or

developmental

-LCB-

stage

specific

factors

in

the

splice

site

selection

process

We

also

can

not

rule

out

interference

from

particular

secondary

structures

of

the

mRNA

in

specific

cases

or

models

based

on

local

competition

between

mutually

exclusive

splice

sites

or

spliceosome

assemblies

Consequently

with

the

network

approach

we

extract

general

features

of

all

the

presented

cases

although

this

leads

to

a

splice

site

prediction

that

is

not

perfect

In

the

prediction

of

splice

site

candidates

therefore

a

compromise

between

finding

the

maximum

number

of

true

splice

sites

and

obtaining

the

minimal

number

of

false

positive

splice

sites

has

to

be

found

The

balance

of

the

compromise

depends

on

the

purpose

of

the

prediction

If

the

purpose

is

to

predict

the

mRNA

-LRB-

and

protein

-RRB-

primary

structure

of

a

newly

identified

gene

of

which

the

sequence

of

a

genomic

clone

exists

the

neural

network

can

be

used

to

predict

a

number

of

candidate

splice

sites

that

can

be

tested

by

PCR

sequencing

directly

on

the

cellular

mRNA

In

this

scenario

obtaining

the

highest

prediction

rate

of

true

sites

is

at

premium

The

false

positive

predictions

can

be

discarded

after

testing

by

the

PCR

sequencing

We

have

therefore

provided

a

set

of

parameters

that

allow

different

compromises

for

the

relation

between

true

and

false

positives

For

large

values

of

the

ratio

between

true

sites

detected

and

false

sites

assigned

correlation

coefficients

in

the

interval

-LRB-

-RRB-

could

be

obtained

which

to

our

knowledge

is

better

than

any

other

splice

site

prediction

method

described

in

the

literature

We

thank

Olaf

Nielsen

for

comments

on

the

manuscript

This

work

was

supported

in

part

by

the

Danish

Natural

Science

Research

Council

under

grants

No

J.nr

and

The

trained

NetGene

program

will

be

made

freely

available

in

versions

running

under

DOS

VMS

and

UNIX

For

instructions

send

an

e

-LCB-

mail

to

brunak@nbivax.nbi.dk,

or

use

one

of

the

addresses

given

above

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computer{prediction
exon selection
intron{splicing