predicting ncrna genes in zebrafish genome: a maching learning approach

8/12/2019 Predicting ncRNA genes in Zebrafish genome: a maching learning approach

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Machine Learning Methods inComputational Biology

Instructor:,George S. Vernikos PhD

Predicting ncRNA genes in Zebrafish

genomeRelevant Vector Machine

Aidonopoulos Orfeas,

Sc Student in Bioinformatics, May !"#


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"$ %ntroductionNo ada!s, one of the most cha""enging #rob"ems in com#utationa" bio"og! is to transform

the huge $o"ume of data, #ro$ided b! ne "! de$e"o#ed techno"ogies, into kno "edge.

%achine "earning has become an im#ortant too" to carr! out this &'(. Se$era" techni)ues

and methods ha$e been de$e"o#ed in order to bui"d mode"s hich can be trained and

make crucia" decisions. *a!esian c"assifiers, "ogistic regression, discriminant ana"!sis,

c"assification trees, random forests, nearest neighbour, neura" net orks, su##ort $ector

machines, ensemb"es of c"assifiers, #artitiona" c"ustering, hierarchica" c"ustering, mi+ture

mode"s, hidden %arko$ mode"s, *a!esian net orks and Gaussian net orks are some of

that kind of methods.

In our #ro ect the aim as to de$e"o# genera"i-ed "inear mode"s using the Re"e$ant Vector

%achine techni)ue in order to #redict ncRNA genes in genomic &DNA( se)uences of

Zebrafish genome. Noncoding RNAs &ncRNA( are RNAs that are transcribed, but not

trans"ated into #rotein. here are t o kinds of ncRNA: short and "ong non/coding RNAs.

he! both inc"ude e""/characteri-ed transfer RNAs and ribosoma" RNAs, snRNAs,

snoRNAs, and miRNAs, as e"" as a #"ethora of ne ncRNAs that ha$e been sho n to #"a!

ma or ro"es in the ce""u"ar #rocesses of a"" "i$ing organisms&0(&1(. In addition, it has been

studied the functiona" ro"e of "ong non/coding RNA in human carcinomas &2(&3(.

Re"e$ant $ector machine &RV%( is a machine "earning method hich e+#"oits a #roba"istic

*a!esian "earning frame ork and ha$e an identica" functiona" form to the e""/kno n

su##ort $ector machine &SV%( &4(. RV% has the abi"it! to construct accurate #rediction

mode"s hich uti"i-e dramatica""! fe er basis function than a SV% hi"e offering se$era"

additiona" ad$antages. he inno$ati$e function of a RV% is the #robabi"istic #redictions it

creates. It doesn5t decide if a dato be"ongs or not in a c"ass but it gi$es it a #robabi"it! of

be"onging to a c"ass. he! can be uti"i-ed for both c"assification and regression #rob"ems.

$ &revious 'or(s and our pro)ect6a$ing done a research about #re$ious orks on the #rediction of non/coding RNA genes

e found on"! one #a#er hich is re"ated to our #ro ect and has been used the RV%

method. In &7(, Do n and 6ubbard tried to gain im#ortant information from non/coding


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regions of simi"arit! bet een genomes. 8s#ecia""!, their aim as to e+tract the strongest

signa" from a set of non/coding conser$ed se)uences using RV%. 9rom this ork it as

sho n that the #redictions of the mode" ere c"ose to the start of annotated genes, as e

can see on the figure be"o . he! a"so $erified that the #romoter signa" is the strongest

sing"e motif/based signa" in the non/coding functiona" fraction of the genome hi"e subsetsof these #romoter regions ha$e an abundance of #G dinuc"eotides.

A#art from the ork of Do n and 6ubbard se$era" other a##roaches has been de$e"o#ed

for the #rediction of non/coding RNA genes or regions. At &;( the #ur#ose as the

c"assification of RNA se)uence a"ignments based on S I and a -/score using the su##ort

$ector machine. S I is a measure for RNA secondar! structure conser$ation hi"e the -/

score re#resents a measure for thermod!namic stabi"it! of a"ignments &norma"i-ed ith

res#ect to se)uence "ength and base com#osition(. At the fo""o ing figure the green circ"es

are the #ositi$e e+am#"es of the training set &nati$e a"ignments( and the red crosses the

negati$e ones &shuff"ed #ositions of random a"ignments(. he background co"or rangingfrom red to green indicates the RNA c"ass #robabi"it! for different regions of the -


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he annotation of noncoding RNA genes remains a ma or bott"eneck in genome

se)uencing #ro ects. %ost genome se)uences re"eased toda! sti"" come ith sets of tRNAs

and rRNAs as the on"! annotated RNA e"ements, ignoring hundreds of other RNA fami"ies.

Se$era" on"ine too"s ha$e been created for this #ur#ose. RNAs#ace.org &=( is one of the

most recent too"s for the #rediction, annotation and ana"!sis of "ncRNA genes. NcRNA.org

is another too" for finding "ong non/coding RNA genes in RNA se)uences gi$ing a"so

information about the secondar! structure of the resu"ts & 9igure : ncRNA.org ( &'>(. Se$era"

other too"s and databases can be found on the ab"e 0 from Gibb5s #ub"ication &3( / 9igure .


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*igure + ncR A$org


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*igure + long non-coding R A online data.ases and tools

#$ Ma(ing the datasetAs e referred, the aim of the #resent ork as to #redict ncRNA genes in genomic &DNA(

se)uences of Zebrafish genome using the ?re"e$ant $ector machine5 method. @ut training

set as created as this: he #ositi$e e+am#"es ere consisted of kno n ncRNA genes

hich ere taken from htt#: .ensemb".org . hen, for the negati$e set, e

do n"oaded a"" the #rotein coding genes of Zebrafish from the ebsite of ensemb"e ande random"! se"ected the same number of ncRNA genes &about 23>> se)uences(. Be did

this in order to ha$e a ba"ance bet een the number of #ositi$e and negati$e e+am#"es.

As e kno , a GC% is a used form of mode" for both c"assification and regression

#rob"ems. It takes the ne+t form:

here is a set of basis functions & hich can be arbitrar! rea"/$a"ued functions( and is

a $ector of eights. In other ords this is e)ua" ith:
http://www.ensembl.org/index.htmlhttp://www.ensembl.org/index.html


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h/01 2 3 4 feature"5L/"14feature 5L/ 146 (Equation 1)

In our #ro ect 2> features ere used:

'. Strand: ' and > for com#"ementar!

0. Position in chromosome: Percentage ith res#ect to the "ength of the chromosome

in hich the se)uence be"ongs

1. om#osition 9re)uencies:

a. A, , , G

b. Dimer & AA, AG,E(

c. rimer & AAA, , GGG and (

2. G content

3. o Ratios: A and AG

4. '' motifs: 9or the finding of motifs, e $isited .motifsearch.com here a de/

no$o DNA motif se)uences search can be im#"emented. Be ga$e as in#ut the DNA

se)uences &from our #ositi$e set/ncRNA se)uences( in 9AS A format and the out#ut

hich as returned ere '' motifs:

FAG AAG AF, FAAG AAG F , FGAAG AAGF, F A GGGAAAF, FG AGGG GF,

F A GAAG F, FA GGGAGA F, F GAAG AAF, FGA GGGAGAF,

FG AAG AGF, FA A GGGAAF.

he ho"e construction of our raining Set as im#"emented in #er" "anguage. he scri#ts

ith their in#uts and out#uts are in fo"der ? rainSet5. In this fo"der there are t o subfo"ders

?NegSet and ? PosSet each one for the corres#onding data set &negati$e and #ositi$e

e+am#"es(. he fina" training set is the fi"e ith the name TrainingSet and is the

concatenation of the fi"es: FeaturesNegSet.bed and FeaturesPosSet.bed / 9igure .
http://www.motifsearch.com/http://www.motifsearch.com/


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In case e ant to test the #erformance our mode" e indicate at "ine 3 of configuration

fi"e our est fi"e and at the "ast "ine 0 fi"es are inc"uded. he one is the out#ut ith the #ost

#robabi"ities and the other is the eights fi"e &from the training ste#( on hich the test run

as based. At the fo""o ing figure e can see the Net*eans en$ironment here the RV%

runs e$er! time.

*igure + Configuration *ile of RVM

*igure + &ost &ro.a.ilities


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*igure + :eights of .asis functions


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*igure + etBeans %;>? and ClassificationAccuracy 2 >>$! ?

nd Step 8 *eature e0traction and their importance

9irst of a"" it5s im#ortant to sa! that for each raining set e created a #ointer fi"e/tab"e so

e can see each bar hich feature re#resents on the fo""o ing charts sho s. *e"o is the

tab"e ith the #ointers and their features:

Pointer 9eature

' Strand

0 Position

1 A

2 G

3

4


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7 AA

; AG

= A

'> A'' GA

'0 GG

'1 G

'2 G

'3 A

'4 G

'7

';

'= A

0> G

0'

00

01 AAA

02 GGG

03

04

07 G J ontent

0; A

0= A G

1> FAG AAG AF

1' FAAG AAG F

10 FGAAG AAGF

11 F A GGGAAAF

12 FG AGGG GF


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13 F A GAAG F

14 FA GGGAGA F

17 F GAAG AAF

1; FGA GGGAGAF1= FG AAG AGF

2> FA A GGGAAF

he fo""o ing subcha#ters describe the mode"s e created for each different training ste#,

ho the! ere constructed, the features hich ere e+tracted, their im#ortance and their

#ossib"e ro"e in the #rob"em of #redicting non/coding RNA genes from genomic se)uences.

7raining 'ith All *eatures

9irst"!, as e said, in our training set a"" features ere inc"uded. he RV% mode" e+c"uded

= of 2> features. hese ere the Strand , t o nuc"eotides G, C and si+ of nine motifs

&AAGCTAAGC, GAAGCTAAG, TGAAGCTAA, GATGGGAGA, GCTAAGCAG,

ACATGGGAA (. he ma orit! of the rest features had a negati$e eight. 8s#ecia""!, as one

can see at 9igure , on"! = features had a #ositi$e contribution to our mode" ositi$e

eights( and these ere the Position of se)uence, the A and T nuc"eotides, the @C

content and 3 motifs & AGCTAAGCA, CATGGGAAA, GCAGGGCTG, CACTGAAGC, ATGGGAGAC (.

herefore, e ha$e constructed our ' st Genera"i-ed Cinear %ode". In our occasion e

cannot rite in this documentation our mode" because of the "arge number of basic

functions &2> features(. So, e #ro$ide a tab"e ith the eight of each basic function

hich as e+tracted from RV% machine:

*eature :eight

Position =.='e/>2GA /

0.30>'2;


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2

GG/

0.'220;4

G

/1.;2471=

3

A

/2.'34>34

3

A/

0.414';=

G

/2.0;

74443/

0.1=0>==

/2.144>03

7

A

/0.3>>=3'

=

G

/2.143;==

'

A2.373470

;4

/2.'7140>

=

/

0.>1044'

3

AAA

/>.1111=;

1


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GGG/

>.47=1;3

/>.3007;=

4

/'.07=22;

0

G J ontent

;.0;>;'4

3'

A

/>.>24307

2

A G/

;.>;e/>3

FAG AAGAF

>.'=';24

'7

F A GGGAAAF

>.17411>

=1

FG AGGG GF

>.0>10>0

32F A GAA

G F

>.1010=2

3

FA GGGAGA F

>.107270

;2

;.471103

41

AA

/'.>4

>0420

AG

/1.;==31'

=

A/

1.4=2'1


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A

/2.>1>04>

3

O75 fi"es.

*igure + :eights of all features

he contribution of each structura" feature to our mode" is e$a"uated through a function

&R( that )uantifies the re"ati$e feature im#ortance, rather than the actua" feature eight

&W (. *rief"!, the im#ortance R of each feature is e+#ressed as the #roduct of the

corres#onding eight and the corres#onding standard de$iation & SD ( of the feature $a"ues

in the training set. Be #refer to assess the feature contribution to the mode", through the

R rather than the W $a"ue, because R takes into account the $ariabi"it! of the data set,

norma"i-ing the $a"ues ith the corres#onding SD &''(. %oreo$er, it is #ossib"e to fa"" into

the tra# of considering that a feature ith a "arge #ositi$e eight can enhance the

c"assifier5s se#arating abi"it!. here are man! cases hen a feature ith a #ositi$e eight

has a sma"" R. his ha##ens due to the feature5s dis#ersion. A feature ith a "arge #ositi$e


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eight but ith sma"" dis#ersion/and conse)uent"! sma"" SD/ i"" be ha$e a sma""er R than

B, as R2:5S; .

So, sa$ing Net*eans out#ut in a fi"e &dir: RV%Runs/

Per"9i"esLout#utsLNetbeans ommandCine@uts( e can #arse the SDs of each feature and

then ca"cu"ate their significance R. he #er" scri#t that im#"ements this is the ari!"."# .

he im#ortance of each feature is de#icted on 9igure . his chart sho $erifies that

percentage of Adenines and 7hymines in a se)uence #"a! an im#ortant ro"e for

deciding if in a DNA se)uence there is a ncRNA gene &I&A( '=.144= and I& ( 10.7>4'(.

*ut the most significant feature for making a decision is the percentage of @C content

in se)uence &I&G J @N 8N ( 42.420'(.

*igure + *eatures %mportance


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7raining Set 'ith %n*eatures

In order to $erif! our #re$ious resu"ts e constructed a dataset on"! ith the features for

hich RV% ga$e them a non/-ero eight. he resu"ts for eights and Im#ortance are cited

be""o . As e can see from the 9igure for once again the most of the features had a

negati$e eight. @n"! ; from 1' features ere #ositi$e eighted. herefore, it is $erified

the fact that the most of them are negati$e"! corre"ated among each other.

&ointer *eature' Position0 A12 AA3 AG

4 A7 A; GA= GG

'> G'' G'0 A'1 G'2'3'4 A'7 G';'=0> AAA0' GGG0001


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02G J ontent

03 A04 A G

07

FAG AA

G AF0;

F A GGGAAAF

0=FG AGGG

GF

1>F A GAAG F

1'FA GGGAGA F

*igure + :eights for the %n*eature dataset

As far as the features5 im#ortance it is a"so $erified that @C content is the most

significant feature &I 4;.>>>>(. he )uantities of 7hymines and Adenines in a se)uence

come second and third on the "ist.


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*igure + %n*eature s importance

7raining Set 'ith Out*eatures

In order to make some conc"usions on unseen data e created a training set ith the

features that had a -ero/ eight &M@ut9eatures (. hese features ere the fo""o ing:

&ointer *eature' Strand0 G1

2FAAG AAG F

3FGAAGAAGF

4F GAAG

AAF

7

FGA GGG

AGAF;

FG AAGAGF

=FA A GGGAAF


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a"cu"ating the corres#onding eights and then the im#ortance, e can easi"! see that the

motifs 3/= are strong"! corre"ated. 8ssentia"!, this is some kind of "ogica" as these motifs

ha$e a $er! sma"" #resence in se)uences. A"so, FAAG AAG F motif is hig"! non/significant.

Strand and G do ha$e some re"ationshi# as e can see from 9igure but the! are being

$erified as features hich affect negati$e"! our mode" & 9igure (.

*igure + :eights of Out*eatures

*igure + Out*eatures importance


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7raining Set 'ith egative *eatures

It is im#ortant to refer again that e #refer inter#reting the eight B as a measure that

sho s us ho much a feature affects one other rather than making conc"usions about its

im#ortance as a #redictor. Ne$erthe"ess, in our #ro ect e chose not to ignore the meaning

of negati$e eighted features as the! indicate the fact there is a negati$e corre"ation

bet een these basis functions. onse)uent"!, e ou"d "ike to obser$e ho the negati$e

eighted features beha$e. he features for hich RV% ga$e a negati$e eight ere the

fo""o ing:

&ointer*eature

' AA0 AG

1 A2 A3 GA4 GG7 G; G= A

'> G'''0

'1 A'2 G'3'4'7 AAA'; GGG'=0>0' A00 A G

@ne thing that #ro$es that the most of the abo$e basis functions affect our modem in a

Mnegati$e a! is the fo""o ing chart sho & 9igure (. Be can readi"! obser$e that most of

the features continue to ha$e the same beha$ior. he most of them ha$e a negati$e

eight. @n"! $% and &% are re"ated each other. his can be "ogica""! e+#"ained due to the


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*igure + %mportance of eg*eatures

7raining Set 'ith &ositive *eatures

Cast"!, e created a raining set inc"uding the features that the first RV% run ga$e them a

#ositi$e eight. raining es#ecia""! these features e cou"d conc"ude that the #osition,

Adenine, h!mine, G content and the motifs 4 and ; are strong"! corre"ated & 9igure (. Not

on"! does their re"ationshi# is stab"e but their im#ortance too. he 9igure de#icts the fact

that their im#ortance to a mode" hich inc"udes on"! these basis functions are stead!. Cast

but not "east, there aren5t man! differences in im#ortance magnitude &most features5

im#ortance measure ranges from >.'3 to >.33(. So e cannot sa! for e+am#"e that motif

FAG AAG AF has a more im#ortant ro"e than G content.

&ointer *eature' Position

0 A1

2G J ontent

3FAG AAG AF

4F A GGGAAAF


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7FG AGGG

GF

;F A GAAG F

=FA GGGAGA F

*igure + :eights of &os*eatures


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*igure + &os*eatures importance

=$ @eneral results 8 Biological interpretationA"" trainings ha$e been considered, e can conc"ude that G content is a measure that e

ought to obser$e and stud! more essentia""!. G content as e+ce""ed among a"" the

features. Pre$ious orks ha$e sho n that G /rich isochores inc"ude in them man! #rotein

coding genesK thus determination of ratio of these s#ecific regions contributes in ma##ing

gene/rich regions of the genome. 9or e+am#"e, as e said in the beginning, it has been

sho n that human genes associated ith #G is"ands increase in number as the! increase

in of Guanine O !tosine "e$e"s, and that most genes associated ith #G is"ands are

"ocated in the G /richest com#artment of the human genome. herefore, for this reason

e create 0 distribution in order to see the differences bet een #ositi$e &kno n ncRNA

genes( and negati$e rotein coding genes( e+am#"es.


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9rom the abo$e figures e can see that in -ebrafish genome #rotein coding genes are a"itt"e richer in G content than kno n ncRNAs, such as in human genome. he GO

content is bigger than 2> in about 13>> ncRNA se)uences and in 1=>> Protein Genes.

he #ercentages of Adenine and h!mine in genome se)uences a"so e+#orted and $erified

as significant #redictors. his is )uite "ogica" as e #ro$ed that G content #"a!s an

im#ortant ro"e in our mode". onse)uent"!, the com#"ementar! bases of G and ma! a"so

be #"a!ing some ro"e. he fo""o ing distributions sho us the #ercentage of Adenines and

h!mines & ith res#ect to the "ength of each se)uence( for both Positi$e and Negati$e

e+am#"es.


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9rom the abo$e chart sho s, first of a"", e are seeing that the ma+imum of both Adenines

and h!mines content in each se)uence doesn5t e+ceed the #ercentage of 2> . he

se)uences hich are encoded for a #rotein are a "itt"e richer in Adenines than the ones

hich gi$e RNA genes, "ike in the case of G ontent. @n the other hand, both #rotein

coding and ncRNA genes ha$e the same content of h!mines in their content. Be cannot

e+tract an! ma or difference bet een our e+am#"es e+ce#t for the the kind of distribution

of Adenines in t o datasets. he a""ocation in Protein Genes is more ba"anced than in

ncRNA ones.

herefore, G content is rea""! a significant #redictor for making a decision if a DNAse)uence i"" be trans"ated into a #rotein or not.


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References'. Carra aga P, a"$o *, Santana R, *ie"-a , Ga"diano Q, In-a I, et a". %achine "earning in

bioinformatics. *rief. *ioinform. 0>>4 %ar 'K7&'(:;4'' @ct 1K4&'>(:e03='3.

3. Gibb 8A, *ro n Q, Cam BC. he functiona" ro"e of "ong non/coding RNA in humancarcinomas. %o". ancer. 0>'' A#r '1K'>&'(:1;.

4. i##ing %8. S#arse ba!esian "earning and the re"e$ance $ector machine. Q %ach CearnRes. 0>>' Se#K':0''>2 Se# '3K3&'(:'1'.

;. Bashiet" S, 6ofacker IC, Stad"er P9. 9ast and re"iab"e #rediction of noncoding RNAs.Proc. Nat". Acad. Sci. H. S. A. 0>>3 9eb '3K'>0&7(:0232>; Qu" 'K14&su##" 0(:B73>; 9eb 'K';&0(:11'

predicting ncrna genes in zebrafish genome: a maching learning approach

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