Master Thesis Bjorn Sponberg Stockholm University 2012.

8/13/2019 Master Thesis Bjorn Sponberg Stockholm University 2012.

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A new model argues againstJust

in time synthesisin M phase

Student:Bjrn Sponberg

Supervisor:Erik L.L Sonnhammer

Department of Biochemistry and Biophysics,

Stockholm University, Sweden.

Start date: 2011-09-01

inish date: 2012-11-27

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!a"le of #ontents

Table of contents........................................................................................................................2

Abstract......................................................................................................................................3

1 Introduction............................................................................................................................4

1.1 The eukaryotic cell cycle....................................................................................................................................4

1.2 Translational control in eukaryotic cells............................................................................................................4

1.3 A new regulatory model.....................................................................................................................................5

2 Methods...................................................................................................................................6

2.1 Methods of gathering data and experimental design..........................................................................................

2.2 Training and !inary classification with "#Mlight..............................................................................................$

2.3 %lastall................................................................................................................................................................&

2.4 'T(scan............................................................................................................................................................ .)

2.5 Type of control...................................................................................................................................................)

3 Results and discussion..........................................................................................................10

3.1.1 The three principal arguments ......................................................................................................................1*

3.1.2 +esigning the expected plot..........................................................................................................................12

3.1.3 ,otential clusters of homologs in the cell cycle............................................................................................13

3.2 SVM and Blastall analysis on the !"TR# 3!"TR and intron $heels..........................16

3.2.1 The fi-e prime untranslated region 5/'T(0.................................................................................................1

3.2.2 The three prime untranslated region 3/'T(0...................................................................................... ........1$

3.2.3 ntron..................................................................................................................................................... .......2*

3.2.4 "hort summary..............................................................................................................................................21

3.3 "TRscan...........................................................................................................................22

3.3.1 'T(scan on 5/'T(............................................................................................................................. .........22

3.3.2 'T(scan on 3/'T(............................................................................................................................. .........24

3.% Su&&ary results...............................................................................................................2% 'onclusion.............................................................................................................................26

References................................................................................................................................2(

A))*+,I-*S /I All sixteen wheel positions /II ist of 'T(scan motifs.

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Abstract

To clarify as many details as possi!le in the human cell cycle is of great medical interest. t is

an initiati-e to eliminate cancer disease. A new regulatory model for the eukaryotic cell cycle

has !een launched. The new model !uilds on three e-olutionary principal arguments which

com!ined will lead to the alternati-e regulatory solution suggested !y the new model. More

specifically the model hypothesies that as the chromosomes enter their stressful states in M

phase transcripts will !e supplied from ,!ody -esicles. 6or this reason these special

m(7A/s are referred to asP-body transcripts. 8onse9uently the ,!ody transcripts must !e

transcri!ed and transported into ,!odies !efore M phase. According to the new model this

happens :ust after the restriction point in ;1 phase.

4* genes from the 0 and Upstream Open Reading rame u?(60 are potential regulatory

candidates in the ,!ody transcripts which can control their uni9ue temporal storage and

release -ia cytosolic ,!odies.

n conclusion these results can lead to further in-estigations in the wetla! to clarify if and

how the new model functions in -i-o.

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1 Introduction

1.1 The eukaryotic cell cycle

The eukaryotic cell cycle is usually di-ided into four phases= !" S !#andM phase@1. The

time it takes to complete one cycle -aries from organism to organism what type of cell it is

and at what de-elopmental stage the cell is in @2. To complete one successful cell cycle

many different types of proteins are needed continuously. The pool of genes that are needed to

complete one cell cycle are either constitutively e$pressed orperiodically e$pressed@3. The

constituti-ely expressed proteins are a-aila!le to the cell during the whole cycle. n contrast

the periodically expressed genes are expressed at a certain time point in the cycle and

immediately degraded after use.

1.2 Translational control in eukaryotic cells

The detailed residue!yresidue transfer of se9uential information from %eo$yribonucleic

acid+7A0 -ia messenger ribonucleic acidm(7A0 to protein is referred to as the central

dogma@4. According to the central dogma synthesis of m(7A transcription0 takes place in

the cell nucleus. The initial transcript -ersion precursor mR&' is modified !efore it is

transported out of the cell nucleus for translation in cytosol @5. 6urther many regulatory

factors !ind the m(7A molecule in the cell nucleus to influence its future fate in cytosol.

These (7A !inding regulatory factors mature the m(7A into a messenger ribonucleoprotein

m(7,0 @. This finalied -ersion of the m(7A molecule is what e-entually lea-es the cell

nucleus for translation. The regulatory factors that !inds m(7A to create the m(7, complex

regulates= translation subcellular location or degradation control. The two most important

m(7A regions that function as !inding sites for these regulatory factors are the three prime

untranslated region3/'T(0 and the five prime untranslated region5/'T(0 @$ &. The next

process after transcription in the central dogma is to translate the m(7A into a functional

protein. 6irst ri!osome su!units assem!les on the 5/'T( end of the m(7A se9uence. An

initial ri!osome complex scans down the m(7A until it reaches the first A'; codon @)

1*. At this stage the unfinished ri!osome complex !ecomes a fully functional

ri!osome !y association with the 4*" ri!osomal su!unit. The complete ri!osome complex is

now competent for translation of the downstream m(7A in the 5/ B 3/ direction.

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(ust in time synthesisis a phrase that is used to descri!e the classical way in how a cell

responds to a stimuli. The phrase Cust in time synthesis can !e interpreted as= that the cell

initiates transcription and translation immediately after it recei-es a stimulation to do so @3.

1.3 A new regulatory model

The current -iew is that the periodically expressed genes in the eukaryotic cell cycle are

regulated in a (ust in time synthesis manner. The new cell cycle model 9uestions if Cust in

time synthesis really takes place in all phases. P-bodies or processing !odies are small

-esicles in cytosol that got its name from their in-ol-ement in mR&' turnover@11. (ecent

studies ha-e shown that these small -esicles can ha-e other functions as well such as

temporal translation control and m(7A storage.


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2 Methods

2.1 Methods of gathering data and experimental design

A!out 4*


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should !e 1**E filled with p!ody transcripts was M phase. DThe wheelD was supposed to

turn gradually) click !y click to e-entually complete one full turn see Appendix 0. To make

sure that all the potentially p!ody transcripts were isolated in at least onewheel position

during a full turn cell cycle0 the wheel was split into two e9ually sied datasets red and

!lue datasets in figure 20.


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3. >ach kmer com!ination fre9uency in a se9uence was annotated as a feature in a sparse

encoded file. After scanning all kmer com!inations o-er a se9uence that se9uence

was represented as a sparse encoded file. A se9uence from the red dataset was

annotated with /G1/. "e9uences from the red data set looked like this with its kmer

fre9uency !ehind num!ersF

G1 1F*.**2 2F*.*345 3F*.)11 etc....

A se9uence from the !lue dataset was annotated with /1/. "e9uences from the !lue

data set looked like this with its kmer fre9uency !ehind num!ersF

1 1F*.**2 2F*.*345 3F*.)11 etc....

4. >ach sparse encoded file was used as input data in s-mHtrain which !ased on this

created a training model. After training the training model was used !y s-mHclassify

to separate potential red dataset genes /G/0 from potential !lue dataset genes //0 from

a group that was left out from the training process.

5. To set a -alue on s-mHclassify/s classification performance Matthe*s correlation

coefficient score +M88 score0 was calculated !ased on com!inations of true positi-e

T,0 true negati-e T70 false positi-e 6,0 and false negati-e 670.

6. Totally 25 parallels were run on each of the sixteen wheel dataset positions. >ach run

was run as a lea-eoneout cross-alidation with fi-e datasets. 6inally the a-erage

M88 scores for each of the sixteen wheelpositions was plotted. To sa-e time only

eight were actually ran in the "#M tests. The reason for this was that at 1&* degrees in

the wheel it all started as from * degree again !ut this time the same se9uences had

changed datasets. To the "#M classification a!ility this meant approximately nothing.

2.3 %lastall

Blastallis a program that aligns two input se9uences and gi-es the alignmentpair a similarity

score "core!it0 in return @13. %lastall was used to measure general se9uence similarity

among the se9uences in the red dataset in each of the sixteen wheel positions. All possi!le

se9uencepair alignments in each dataset is scored indi-idually then they are summed up to

gi-e the dataset one total se9uence similarity score in "core!it0. The similarity scores was

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plotted in a wheelplot as with "#M. This score will tell something a!out the general

se9uence similarity within each dataset.

The word sie parameter I0 was for 3/'T( set to ) nt 5/'T( & nt 8+" 11 nt and intron 5&

nt. The general parameter settings wasF D!lastall I 2* e 1***** - 3** p !lastnD.

,arameter meaningsF p J "pecifies the type of search I J "pecifies the word sie e J

"pecifies >xpectation -alue cutoff and - J "ets the upper limit of one line description.

2.4 'T(scan

'T(scan scans 5/'T( and 3/'T( regions with 4 known !inding profiles @14. f one of the

4 !inding motifs found a match in a particular dataset wheel position0 a count was added to

that particular profile. After scanning all se9uences in the red dataset the matchcounts foreach motif was summed and registered. %efore the wheel mo-ed to the next click all counts

was eroed again. After all the sixteen datasets in the wheel had !een scanned all scores were

plotted to make the 'T(scans -ersion of the wheel plot. The plot showed how each of the 4

!inding proteins scored in each of the sixteen datasets each wheel position0.

2.5 Type of control

To make sure that the script nor "#M generated any systematic error two measurement was

taken. 6irst the script was tested with a randomied gene list randomied genewheel0. This

was to check that it did not classified noticea!ly on the randomied gene wheel. To check this

the 3/'T( wheel was used as input. The approach in the pro:ect was a ro!ust one see 3.1.20.

The resulting plot from this test ga-e an approximately flat wheel plot which indicated no

noticea!le response in the support -ector machine.

The second control was to test the datasets with %lastall. f the M88 scoring peak from the

support -ector machine method was reproduced with the %lastall method no systematic error

should ha-e influenced the result. The two methods was run from two independent python

scripts.


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3 Results and discussion

3.1.1 The three principal arguments

The new cell cycle model is !ased on three e-olutionary principal arguments. n sum these

three principal arguments are supposed to force the new model to e-ol-e under the

e-olutionary condition suggested.

10 The first principal argument focuses on the e-olutionary pressure the first eukaryotic cells

experienced. As a group these are usually referred to as protist cells @15. They existed o-er 1

!illion years ago and are the earliest ancestors to fungi plants and -erte!rates @1. The protist

cell was unicellular and pro!a!ly relati-ely nonmo!ile. +ue to their nonmo!ile life style

com!ined with a limited supply of food their cell di-ision mechanism should experienceintense pressure on maximiing di-ision speed. According to the first principal argument they

should only ha-e one gear in life fast for*ard with no !reaks. n conclusion the first

principal argument claims that all eukaryotic cells earliest ancestors was forced to de-elop

the fastest di-ision process a-aila!le to them.

20 t pro!a!ly exist se-eral cell cycle model designs which in theory could increase the cell

cycle speed in eukaryotes earliest ancestors. n principal the next le-el of fitness after rapid

di-ision should !e on e-olutionary ro!ustness. That is in a scenario with many solutions that

pro-ided e9ual cell cycle speed the least complex solution would pro!a!ly win. The less

complex solution would pro!a!ly !e more e-olutionary ro!ust and conse9uently gi-e a

higher net num!er of progenitor cells due to the higher sur-i-al rate @1$.


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30 The third principal argument gi-es the new model its !iological ali!i. t states that the

crucial regulatory mechanism in the new modelP-body storage is a realistic design. That is

the literature pro-ide e-idence of that eukaryotic cells are capa!le of importing transcripts

into ,!odies @2* 21 22 and also to !ring transcripts !ack into cytosol after storage inside ,

!odies @23 24. Least cells also ha-e the important ,!odies @25 2 2$ 2&.

8om!ined these three principal arguments should force forward the most perfect solution

a-aila!le in the protist cells more than one !illion years ago figure 30. +uring development

and *ound healing the same perfect di-ision mechanism should ha-e !een fa-ored and

therefore conser-ed in modern multicellular organisms as well. The mechanism should also !e

fa-ored in human cancer cells since in that particular state they practically li-e under the

same e-olutionary principals as unicellular cells do. n conclusion it would therefore !e

sensi!le to look for the predicted p!ody transcripts in


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3.1.2 +esigning the expected plot.

t was also useful to design if not exactly !ut approximately how the plot from analying the

se9uencewheel should look like if the p!ody transcripts existed in -i-o. The new model is

rather diffuse !oth in terms of how many p!ody transcripts there are and how they are

distri!uted.


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6urther if all se9uences in the dataset that peaks contri!ute e9ually the scoring -alue should

decline with a linear growth rate until the plot antipeaks. "ince each wheel position 22.5

degrees0 represents a!out 3* se9uences the signal should lose approximately 3* p!ody

transcripts per wheelclick.


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To homology reduce the dataset @3 and there!y making sure that the homology signal was

diluted was not done. To pin point exactly how much or how little a potential homology

signal affected the other gene regions was of minor interest. t was the plot pattern from the

regulatory regions that was of most interest and making sure that this pattern was not

dominated !y a potential homology signal figure 50.


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3.2 SVM and Blastall analysis on the !"TR# 3!"TR and intron $heels

3.2.1 The fi-e prime untranslated region 5/'T(0

n mammalian cells 4>T is a 5/'T( !inding protein that is known to !lock m(7A

translation. t also has a decapping function inside ,!odies which protects the transcript

from degradation. n addition it has !een shown that 4>T accumulates together with

m(7A/s inside ,!odies @3$. This should make 5/'T( a likely candidateregion to !e acti-e

in the new model. f so the plot from 5/'T( should !eha-e similar as to the expected plot if

the model is correct. t is therefore interesting to o!ser-e that the M88 classification and the

%last similarity scores are similar as to their respecti-e expected plots in figure 4. The M88

classification score peaks at 45 degrees %lastall peaks at $.5 degrees figure $a and $c

respecti-ely0.

6igure $. a0 "#M plot from 5/'T(. !0 5/'T( region. c0 %last plot from 5/'T(.

n summary the "#Mlight and %last scoring plots on the 5/'T( wheel !eha-e approximately

the same as to their respecti-e expected plots in figure 4.

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3.2.2 The three prime untranslated region 3/'T(0

3/'T( is known to !e in-ol-ed in associating m(7A/s with ,!odies -ia the general

repressionKdecay machinery. The repressionKdecay machinery is a!le to mo-e m(7A/s in and

out of ,!odies @23. The literature mentions at least three candidate (7A !inding proteins

which can recruit the repressionKdecay machinery to the transcript -ia 3/'T(. These three

3/'T( !inding proteins are= ,'65 'pf1 and the +8>( complex. n addition the 3/'T( is

known to !e acti-e in temporal translation control in general @3&. This should make 3/'T( a

likely candidateregion to !e acti-e in the new model.


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6igure ). a0 "#M plots from 8+" !lue0 3/'T( red0 and 5/'T( green0. !0 The %last plot

from the protein 3/'T( and 5/'T( wheels. c0 llustration of where in the cell cycle

homology and the p!ody transcripts should ha-e their signal impacts in !lue and red colors

respecti-ely.

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3.2.3 ntron

6rom the literature the regulatory regions that are !est known to regulate temporal translation

control are 3/'T( and 5/'T( @$. n theory the intron region should not ha-e anything to do

with the new model. The reason for this is that the intron region is remo-ed from the

precursor m(7A in the cell nucleus !efore the m(7A is transported out to cytosol @3) 4*.

(ecruitment of m(7A/s into ,!odies takes place in cytosol it is therefore unlikely that the

introns are in-ol-ed in this process. t was therefore surprising to o!ser-e that the M88

classification score on the intronwheel matched that of the expected plot similar as to 5/'T(

and 3/'T( 3/'T( if not distur!ed !y the homology signal see 3.2.20. That the intron

region peaks in the same wheel area 45$.5 degrees0 similar as to 3/'T( and 5/'T( could

not !e neglected as a coincidence figure 1*a0.

6igure 1*. a0 "#M plot from the intron region. !0 The intron region in a eukaryotic gene.

+id the literature say anything a!out introns role in the cell cycle n -erte!rates a

phenomenon called minor splicinggi-es the intron region a conser-ed role in the cell cycle

@41 42. The minor splicing system targets a rare class of introns called minorclass introns

carrying distinct and highly conser-ed 5/splicesite and !ranchprotein se9uences @43. n

addition the minorclass splicing takes place in cytosol instead of in the cell nucleus. This

piece of information can explain the result in figure 1*a and opens up for introns possi!le

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in-ol-ement in the new model. n -itro experiments ha-e shown that minorclass splicesome

acti-ity takes place in M phase. 6urther down regulation of the minorclass splicing systems

splicesomescauses lethal phenotypes in human cancerous


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6igure 11. "ummariation of the results from "#M and %last analysis on the cell cycle genes.

3.3 "TRscan

'T(scan is a software tool that scans 3/'T( and 5/'T( regions with 4 different !inding

profiles which are stored in the 'T(scans data!ase @14. This approach was an attempt to

add !iologic information to the p!ody transcripts and there!y the new model. f one of

'T(scan/s 4 !inding profiles is acti-e in the p!ody transcripts its fre9uency score should

peak similar as to the expected plot for the red dataset. "ince the 'T(scan scans allse9uences

in the red dataset similar as to %last its plot should !eha-e similar as to the expected plot for

the %last output scores.

3.3.1 'T(scan on 5/'T(

?f the 4 possi!le candidate !inding motifs see full list in Appendix 0 only Upstream

Open Reading rame u?(60 ga-e a meaningful pattern on the 5/'T( se9uencewheel

figure 12 left0. The fre9uency pattern was reminiscent of the expected plot shown in red in

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figure 4. This should not !e neglected as a coincidence. The u?(6 motif peaks at $.5

degrees and ha-e its glo!al minimum at 24$.5 degree on the 5/'T( wheel.

6igure 12. 'T(scan plot from the 5/'T( left0 and 3/'T( wheel right0.

nterestingly from the literature u?(6/s function in 5/'T( is !elie-ed to regulate

temporal translation control @45. This can ser-e an important purpose to the new model.

That is it would pro!a!ly !e !eneficial to the cell if the ,!odies released all of its p!ody

transcripts in one !urst instead of /handpicking/ them indi-idually as they are needed. The

easiest solution would !e to release all p!ody transcripts at once and then let u?(6 fine

distri!ute their temporal translation. Iithout a mechanism like with u?(6 this would !e

difficult !ecause all the transcripts would !e translated at the same time. f the ,!ody !urst

all p!ody transcripts should associate with the ri!osome complex and !e translated.


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3.3.2 'T(scan on 3/'T(

Musashi binding element M%>0 ga-e a meaningful plot on the 3/'T( se9uencewheel

figure 12 right0. M%> is known from the literature to ha-e the same function as

,ytoplasmic polyadenylation element8,>0 in 3/'T( @4. Ihat is interesting a!out this is

that 8,> and M%> are !oth represented in the list of 4 !inding profiles in 'T(scan list in

appendix 0. does not gi-e any meaningful responseKplot only M%>. t is a

discussion in the community to understand the exact roles to 8,> and M%> in temporal

translation control @4$. ?ne paper has shown that M%> regulates the temporal order of

m(7A translation during oocyte cell cycle progression @4&. Mutational analysis in this

work indicates that M%> rather than 8,> directs cyclin % translational acti-ation in the

cycling oocyte cells. 8yclin % is a key regulatory gene in the cell cycle/s M phase @4).

Therefore cyclin % should !elong to the pool of p!ody transcripts in the cell cycle.

+uring em!ryo de-elopment 8,> and M%> has !een shown to play a role in a silencing

mechanism known as /masking/ @3&. This translation control mechanism hinders translation

!y pre-enting el64>/s interaction with el64; which is crucial for translation initiation. As

mentioned in 3.2.1 .-T is a 5/'T( !inding protein which is known to !lock m(7A

translation and to ha-e decapping functions inside ,!odies @3$. This is interesting !ecause

4>T ha-e also !een known to take part in some forms of /masking/ !ut in the 3/'T( region

figure 130 @5*. t is there!y indirectlycoupled to the M%> !inding element figure 130.

6igure 13. 4>T are known to interact with 8,>% which !inds to the M%> or 8,> !inding

element in 3/'T( @5*.

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3.% Su&&ary results

ene

reion Su&&ary results

!"TR

Analytic dataF "#M and %last plots on the 5/'T( se9uence wheel performed

similarly as the expected plot in figure 4 3.2.10.

"uggested role in the new modelF 6ine tuning of temporal translation -ia u?(6 to let

the p!odies !urst and release all of its content at once. "upportF 'T(scan plot on the

5/'T( wheel 3.3.10. 4>T !locks m(7A translation and protects the transcript from

decapping inside ,!ody.

u?(6 reference @45]

4>T reference @3$

3!"TR

Analytic dataF #ague M88 classification plot. "trong %last plot shape on the 3/'T(

se9uence wheel. 6igure &a and &c 3.2.20. ,erformed similarly as the expected plot in

figure 4.

"uggested role in the new modelF DMaskingD with 4>T of m(7A/s to protect against

decapping while it is stored inside ,!odies. "upportF 'T(scan plot on the 3/'T(

wheel connects M%> !inding element to the p!ody transcripts which o-erlap with

the function to 8,> !inding element figure 12 right0.

M%> reference @3&].

4>T reference @5*.

Intron

Analytic dataF "#M plot on the intron wheel performed similarly as the expected

plot. 6igure 1*a 3.2.30.

"uggested role in the new modelF


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% 'onclusion

Ie systematically analyed the fi-e prime untranslated region 5/'T(0 the 8oding +7A

"e9uence region 8+"0 the three prime untranslated region 3/'T(0 and introns in cell cycle

genes from


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References

@1 8ooper ;M 2***0. D8hapter 14F The >ukaryotic 8ell 8ycleD. The cellF a molecular approach 2nd

ed.0. Iashington +.8F A"M ,ress. "%7 *&$&)31*.

@2 %al!ach "T >ste-es T8


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@35 Al!erts % Cohnson A ewis C (aff M (o!erts P Ialter , 2**20. D8hapter 5F +7A

(eplication (epair and (ecom!inationD. Molecular %iology of the 8ell. ;arland "cience. "%7 *

&153321&1.

@3 i I Carosewski ;odik A. 8lustering of highly homologous se9uences to reduce the sie of

large protein data!ases. %ioinformatics. 2**1 Mar=1$30F2&23.

@3$ 6erraiuolo MA %asak " +ostie C Murray > "choen!erg +( "onen!erg 7. A role for the

e64>!inding protein 4>T in ,!ody formation and m(7A decay. C 8ell %iol. 2**5 "ep

12=1$*0F)1324.

@3& Puersten " ;oodwin >%. The power of the 3/ 'T(F translational control and de-elopment. 7at

(e- ;enet. 2**3 Aug=4&0F23$.

@3) Curica M" Moore MC. ,rem(7A splicingF awash in a sea of proteins. Mol 8ell. 2**3Cul=1210F514.

@4* 7ilsen TI ;ra-eley %(. >xpansion of the eukaryotic proteome !y alternati-e splicing. 7ature.2*1* Can 2&=43$2&*0F45$3.

@41 PQnig


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Appendix F "#M !inary classification red-s !luedataset. %lastall reddataset only.

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Appendix F u"8A7 !inding profile list 40.

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Master Thesis Bjorn Sponberg Stockholm University 2012.

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Transcript of Master Thesis Bjorn Sponberg Stockholm University 2012.