Gene gain and loss: aCGH ISA CGH

Gene gain and los s : aCGH
ISA CGH
IV Cours e on Microarray data analy s is
March 13, Valencia
Rafael C. Jim enez

Gen etic Variation in h u m an gen om es
From ch rom osom e an om alies to sin gle n u cleotid e ch an ges
Chromosome Gene Nucleotide

CNV/ CNP
Cop y n u m ber variation of DNA segm en ts
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Deletion s
In sertion s
Du p lication s
Kb
Mb
b(nt)

Com p arative Gen om ic Hybrid iz ation
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Meth od for an alyz in g gen om ic DNA for u n balan ced
gen etic alteration s (CVN)
Based on flu orescen t h ybrid iz ation of DNA
Techniques
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FISH, PCR, Sou th ern
A rray CGH
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Clon es (BAC, YAC, PAC)
PCR n on - red u n d an t
Oligonucleotides
cDNA

1. Clon es to cover a gen om ic region
2. Extraction an d p u rification
3. Array DNA on to glass slid es
Exam p le: Array CGH
BAC probes
4. Hybrid iz ation of labeled n orm al an d tu m or gen om ic DNA to th e m icroarray
5. An alyz e flu orescen ce ratio

Exam p le: Array CGH
BAC probes

Array CGH
Adv antage
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An alysis of wh ole- gen om e in a sin gle exp erim en t
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High er resolu tion th an con ven tion al CGH (5- 10 Kb)
Lim itations :
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In ability to d etect m osaicism , balan ced
ch rom osom al tran slocation s, in version s an d wh olegen
om e p loid y ch an ges
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Dep en d in g on th e p lataform ch osen :
- cDNA: lim ited by th e gen es en cod ed on
ch rom osom es, cross- h ybrid iz ation s
- BACs, PACs: DNA am p lification s are n ecessary

Array CGH

ISA CGH
ISACGH allows visualiz in g
array CGH d ata or/ an d
exp ression arrays on to
h u m an or m ou se
ch rom osom al coord in ates
(au tom atically fou n d
th rou gh th eir stan d ard
id en tifiers) an d rep resen ts
th e cop y n u m ber alteration s
fou n d by u sin g d ifferen t
m eth od s.
Correlation s between cop y
n u m ber an d gen e exp ression
level can easily be observed
in a p lot. Th e p rogram
allows fin d in g m in im al
com m on region s with
altered cop y n u m ber across
d ifferen t arrays.
http://isacgh.bioinfo.cipf.es

ISA CGH
Alth ou gh ISACGH can be u sed
alon e, it is tigh tly in tegrated
in th e GEPAS p ackage. Th u s,
n orm aliz ation an d d ifferen t
d ata p re- p rocessin g
op eration s can d irectly be
p erform ed with in th e sam e
en viron m en t.
ISACGH is also con n ected to
d ifferen t tools for fu n ction al
an n otation so en rich m en t in
fu n ction ally relevan t term s
(gen e on tology, p ath ways,
etc) in am p lified or lost
ch rom osom al region s can
d irectly be stu d ied

General
outline
INPUT: gen e exp ression or/ an d
gen om ic h ybrid iz ation valu es.
OUTPUT: p red iction of region s
with alteration s in th e n u m ber of
cop ies, p lotted in th e sam e
figu re th an gen e exp ression
valu es to view th e relation sh ip
between both variables
∙Fou r m eth od s for th e
estim ation of gen om ic cop y
n u m ber
∙Oth er p aram eters:
- Scale
- Man agem en t of m u ltip le
p robes
http://isacgh.bioinfo.cipf.es

Probe
identifiers
Differen t array p latform s for
CGH m easurem en ts (BACs, cDNA
clon es an d oligon u cleotid es for
array sp ots)
Users m u st collect an d keep
u p d ated th e ch rom osom al
coord in ates of th e p robes
ISACGH au tom atically retrieves
th em an d p lots th e h ybrid iz ation
valu es over th e corresp on d in g
p osition s in ch rom osom es.
Differen t IDs can be u p load ed
(En sem bl Id s, accession , Un igen e,
HUGO, RefSeq, Affy, BAC
n am es...)
Ch r1:203465273- 2034
8412
User- d efin ed in form ation on
ch rom osom al coord in ates can
altern atively be su p p lied to th e
p rogram

Genomic copy num ber
es tim ation
Sm oothing: variation of th e Ad ap tive Weigh ts Sm ooth in g m eth od im p lem en ted in th e p rogram
GLAD.
Binary s egm entation: ch ecks wh eth er every sin gle p oin t in th e d ata set is a breakp oin t in an
iterative way. It u ses a p erm u tation d istribu tion to test for m ean d ifferen ces between grou p s
d rawin g robu st in feren ces th at d o n ot rely u p on an y m od el of th e d ata
Regres s ion: u ses som e ch aracteristics of lin ear regression of in ten sity m easu re on p osition
alon g th e gen om e. Su ch a regression lin e h as slop e close to z ero wh en fitted in region s with
h om ogen eou s in ten sity levels bu t th e slop e d iffers form z ero wh en in ten sity m easures on
th e left- h an d sid e are h igh er or lower th an in ten sity m easu res in th e righ t- h an d sid e. Given
th e p oin ts of in ten sity m easu rem en ts ord ered by th eir p osition in th e
ch rom osom e, ou r p roced u re fits a regression lin e for each N con secu tive p oin ts. Th u s a vector
of slop es equ ivalen t in som e way to a d erived cu rve of th e origin al d ata is obtain ed .
t- test is u sed to to id en tify th e slop e- p eaks th at are big en ou gh to in d icate a break- p oin t in
th e in ten sity levels.
Is ow indow : tries to id en tify bord ers between region s with a sign ifican t ch an ge in th e valu es
of in ten sity of h ybrid iz ation . Given th e in ten sity m easu rem en t p oin ts from th e array ord ered
by th eir p osition in th e ch rom osom e, a first step fin d s th ose th at are good can d id ates of
bein g su ch bord ers. Rou gh ly sp eakin g, a p oin t will be a good can d id ate if th e p - valu e of
a t- test com p arin g som e close p oin ts located at its left an d righ t n eigh borh ood s is low
en ou gh .
Th e bin ary segm en tation m eth od u ses th e global d istribu tion of th e d ataset wh ile th e oth ers
are m ore based on th e local d istribu tion s of th e p oin ts.

Plotting data and copy num ber
es tim ation
One array / All
chrom os om es
Expression data
Breakpoint estimation

Plotting data and copy num ber
es tim ation
One chrom os om e / All
array s

Study ing breakpoints and details at gene
lev el
Click to get a d etailed rep resen tation
of th e ch rom osom al region sp an n in g
4Mb th at in clu d es th e corresp on d in g
p robes of th e array located th erein
as well as gen es m ap p ed by En sem bl.
A blu e lin e rep resen ts th e estim ation
of th e gen om ic cop y n u m ber.

Minim al com m on regions w ith cons is tent
copy num ber alterations acros s s ev eral
ISACHG estim ates th e gen om ic array cop y n usm ber in each in d ivid u al arrays
an d th en m erge th is in form ation in a u n iqu e p lot.
Th e p lot rep resen ts th ose region s th at are eith er con sisten tly
gain ed or lost in all th e arrays. Th e average h ybrid isation in ten sity
is also rep resen ted .

Correlation betw een genom ic and
expres s ion data
Qu ite u sefu l wh en stu d yin g th e effect of gen om ic
cop y n u m ber alteration s on th e exp ression level of
gen es is to h ave a sim p le rep resen tation of th e
relation ship between both variables.

Functional annotation of altered copy
num ber regions
Fu n ction al an n otation of ch rom osom al region s based on th e gen e on tology (GO)
an n otation s.
An n otation s for region s of gain or loss are com p ared to th e backgrou n d of
an n otation s corresp on d in g to th e rest of gen es p resen t in th e array, in
ord er to see wh eth er th is region h as an y ch aracteristic fu n ction ality or
n ot.
A Fish er exact test for con tin gen cy tables is p erform ed to look for GO
term s sign ifican tly overrep resen ted in th e ch rom osom al region stu d ied
com p ared to th e backgrou n d abu n d an ce of GO term s in th e rest of th e
gen om e.
P- valu es ad ju sted for m u ltip le testin g u sin g th e FDR m eth od .

GEPAS, the integrated env ironm ent
Norm aliz ati
on
Prep roces
s
Clu steri
n g
Differen tial
exp ression

An exam ple

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1. Choose an
organism

2. CGH array data
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3. Class labels (optional)
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4. Gender labels (optional)
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5. Choose the copy number estimation mehod
Fast
∙ Isowindow
∙ Binary
Segmentation
∙Regression
∙ Smoothing
Precission

6. Gene expression data
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7. Position data (optional)
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8. Scale value (optional)
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8. Scale value (opcional)
Scale: default
(max)
Scale: 5

9. Reference lines (optional)
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9. Reference lines (optional)
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10. If you have replicates
∙ Average
∙ Max
∙ Min

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10. Define tipo de representación

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11. Choose the representation

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12. You can download map data

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13. Download data form regions

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14. Check correlation expression­copy number

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15. Zoom y functional analysis
ISA zoom

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15. Zoom y functional analysis
Ensembl DAS zoom

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15. Zoom y functional analysis
Análisis funcional
(FatiGO)

15. All arrays plotting
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http://gepas.bioinfo.cipf.es/cgi­bin/tutoXX?c=/isacgh/isacgh.config

Acknow ledgm ents
Joaquin Dopazo
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Group leader
Lucia Conde
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ISACGH developer
Tutorial
Slides
Ignacio Medina
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Support, guidance and advice
David Montaner
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Support, guidance and advice
Francisco Garcia
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Support, guidance and advice