Bio SCIENCE Kuliah Perdana

8/18/2019 Bio SCIENCE Kuliah Perdana

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Paradigm shift in Life sciences


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Background information

experimental sciences• There is a tendency to look ever deeper in: Matter e.g. Physics Universe e.g. stronomy Life e.g. Life sciences

• !nstrumental conse"uences are increase in detector:

#esolution $ sensitivity utomation $ ro%oti&ation

• Therefore experiments change in nature $ %ecomeincreasingly more complex


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'ne part of the information explosion (.

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2.00E+09

4.00E+09

6.00E+09

8.00E+09

1.00E+10

1.20E+10

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Various microbial genomes

Yeast genome (14 b!"

#. elegans genome (97 b!"

$roso!%ila genome (137 b!"

&uman c%r. 22 (34.5 b!"

'rabio!sis (125.4 b!"

&uman com!lete ra) t ( 3.1 * b!"

Moro)it&


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!mpact in the life sciences

• !mpact of high throughput methods e.g. 'micsexperimentationgenome ***+ genomics


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'mics impact


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!mpact in the life sciences

• !mpact of high throughput methods e.g. 'micsexperimentationgenome ***+ genomics

• !nstrumentation %eing used in omicsexperimentation: Transcriptomics via among others1 micro2arrays3 #,

se"uencing Proteomics via among others1 Mass -pectroscopy 4M-5

Meta%olomics via among others1 M- $ ,uclear Magnetic#esonance 4,M#5


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#esults in Paradigm shift in Lifesciences

• Past experiments )here hypothesisdriven6valuate hypothesis

7omplement existing kno)ledge

• Present experiments are data driven

/iscover kno)ledge from large amountsof data


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The kno)ledge cycle 4traditional5

8ypothesis

!dea9

6xperiment

/ata

Pu%lication

Literature


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The kno)ledge cycle 4extended5

8ypothesis

!dea9

6xperiment

/ata

Pu%lication

(e-)Literature

/ata%ases


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Life sciences research: from gene to function

Gene /,

,8

7''8

Protein

Genome-wide micro-array analysis

;High-throughput


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/evelopments to)ards Bio2informatics $ e2-cience

• 6xperiments %ecome increasingly more complex

• /riven %y increase of detector developments

• #esults in an increase in amount and complexityof data

• -omething has to %e done to harness thisdevelopment

Bio2informatics to translate data into useful %iological3medical3 pharmaceutical $ agricultural kno)ledge


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The )hat of Bioinformatics

Bioinformatics is redefining rules andscientific approaches3 resulting in the

>ne) %iology?. @ithin this ne) paradigmthe traditional scientific %oundaries are%lurred3 leaving no clear line %et)een>dry or computational? and >)et2%ased?approaches


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Role of bioinformatics

cell

/ a t a

g e n e r a t i o n 0 v a l i d

a t i o n

/ a t a i n t e g r a t i o n 0 f u s i o n

/ a t a u s a g e 0 u s e r i n t e r

f a c i n g

enomics

Transcriptomics

Proteomics

Meta%olomics

!ntegrative0-ystem Biology

#,

protein

meta%olites

/,

methodology Bioinformatics


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7onclusions• 'mics experiments change the face of life sciences

• Bioinformatics can %e considered to %e an essentialena%ler and is a form of e2-cience

• @ill help to reali&e necessary paradigm shift in Life-cience experimentation

• Better support of experimentation $ optimal use of !7T

infrastructure re"uires rationali&ation experimentationprocess

• !nformation management essential technology

• Bioinformatics can not %e decoupled from e2Bio2scienceapplications

• e2Bioscience also has to comprise %iomedical applications


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Paradigm -hift in Biosciences

• -o far3 %iologists have focused certainphenotypes and hunted the genes

responsi%le3 one at a time• ,e) trend is7atalog all the parts: genes and proteinsUnderstand ho) each part )orksModel $ simulate the collective %ehavior of

the parts

*enomics ,roteomics

-unctional*enomics

/stemsiolog/


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DNA RNA protein

Central dogma of molecular biology

genome transcriptome proteome

Central dogma of bioinformatics and genomics


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'mics data

• !n the 'mics era3 )e see proliferation ofgenome0proteome2)ide high throughput datathat are availa%le in pu%lic archives7omparative genome se"uences-e"uence variation $ phenotypes

6pigenetics $ chromatin structure

#egulatory elements $ gene expressionProtein expression3 modification $ locali&ation

Protein domain3 structure3 interaction

Meta%olic3 signal3 regulatory path)ays

/rug3 toxicogenomics3 toxicoproteomics


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>-anger se"uencing? has %een the only /,se"uencing method for A years %ut(

(hunger for even greater se"uencing throughputand more economical se"uencing technology(

,- has the a%ility to process millions ofse"uence reads in parallel rather than CD at a

time 4=0D of the cost5

'%Eections: fidelity3 read length3 infrastructurecost3 handle large volum of data

.


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• Many years of hard )ork• More than . B7 clones• 6ach containing a%out =k% fragment

• Together provided a tiling path through each humanchromosome

• mplification in %acterial culture• !solation3 select pieces a%out 2A k%• -u%cloned into plasmid vectors3 amplification3 isolation

• recreate contigs• #efinement3 gap closure3 se"uence "uality improvement• 4less = error0 F. %ases5• B7 %ased approaches to)ard @-


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• #oche0FGF HLI: F

• !llumina -olexa enome naly&er: D• pplied Biosystems -'Li/TM -ystem: J

• 8elicos 8eliscopeTM : recently availa%le

• Pacific Biosciencies -M#T: launching =

Roche 454 technology


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Roche 454 technology


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Illumina Solexa


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FGF vs -olexa

• 8omopolymers 4..5

• #ead length: F %p

• ,um%er of reads: F.

• Per2%ase cost greater

• ,ovo assem%ly3 metagenomics

• #ead length: F %p

• ,um%er of reads: millions

• Per2%ase cost cheaper

• !deal for application re"uiring short reads: nc#,


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'!!lications o) et*eneration euencing


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• ncient /,

• /, mixtures from diverse ecosystems3 metagenomics• #ese"uencing previously pu%lished reference strains• !dentification of all mutations in an organism• 6rrors in pu%lished literature• 6xpand the num%er of availa%le genomes

• 7omparative studies• /eciphering cell?s transcripts at se"uence level )ithout kno)ledge of the genome se"uence• -e"uencing extremely large genomes3 crop plants• /etection of cancer specific alleles avoiding traditional

cloning• 7hip2se": interactions protein2/,• 6pigenomics• /etecting nc#,

• enetic human variation : -,P3 7,K 4diseases5


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• Degraded state of the sample mitDNA sequencing

• Nuclear genomes of ancient remains ca!e bear" mommoth"

Neanderthal #$%& bp '

(roblems contamination modern humans and coisolation bacterial


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• )ey part in regulating geneexpression

• Chip technique to studyDNA*protein interaccions

• Recently genome*+ide ChI(*based studies of DNA*protein

interactions

• Readout of ChI(*deri!ed DNAsequences onto N,Splatforms

• Insights into transcription

factor-histone binding sitesin the human genome

• .nhance our understandingof the gene expression in thecontext of specific

en!ironmental stimuli


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• nc#, presence in genome difficult to predict %ycomputational methods )ith high certainty %ecause theevolutionary diversity

• /etecting expression level changes that correlate )ithchanges in environmental factors3 )ith disease onset

and progression3 complex disease set or severity• 6nhance the annotation of se"uenced genomes 4impact

of mutations more interpreta%le5


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• 6xtreme example:multiplexing the amplificationof = human exons usingprimers from a programma%lemicroarray and se"uencing

them using ,-.


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• Characteri/ing the biodi!ersity found on .arth

• 0he gro+ing number of sequenced genomes enables us to interpretpartial sequences obtained by direct sampling of specif en!ironmentalniches1

• .xamples ocean" acid mine site" soil" coral reefs" human microbiome+hich may !ary according to the health status of the indi!idual


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• 7ommon variants have not yetcompletly explained complexdisease geneticsrare alleles also

contri%ute

• lso structural variants3 large andsmall insertions and deletions

• ccelerating %iomedical research


38/54Metagenome of @anagama -oil


39/54Metagenome of @anagama -oil


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• 6na%le of genome2)ide patternsof methylation and ho) thispatterns change through thecourse of an organism?sdevelopment.

• 6nhanced potential to com%inethe results of different

experiments3 correlative analysesof genome2)ide methylation3histone %inding patterns and geneexpression3 for example.


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• 6pigenetics: %eyond the se"uence. The maEor pro%lem3 ! think3 is chromatin. @hatdetermines )hether a given piece of /, along the chromosome is functioning3

since its covered )ith the histonesN @hat is happening at the level of methylationand epigeneticsN Oou can inherit something %eyond the /, se"uence. Thats)here the real excitement of genetics is no). 4ames /. @atson5. 7hromatin is

defined as the dynamic complex of /, and histone proteins that makes upchromosomes.

• 6pigenetics is defined as the chemical modification of /, that affects geneexpression %ut does not involve changes to the underlying /, se"uence. s the

emphasis in %iology is s)itching a)ay from genetic se"uence and to)ards themechanisms %y )hich gene activity is controlled3 epigenetics is %ecoming

increasingly popular.

6pigenetic processes are essential for packaging and interpreting the genome3 are

fundamental to normal development and are increasingly recogni&ed as %einginvolved in human disease. 6pigenetic mechanisms include3 among other things3

histone modification3 positioning of histone variants3 nucleosome remodelling3 /,methylation3 small and non2coding #,s. 4Nature3 J ug Q5.

http://en.wikipedia.org/wiki/Epigeneticshttp://en.wikipedia.org/wiki/Epigenetics


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• #educed se"uencing

error

• !ncrement read length

• /eveloping ne)%ioinformatic tools

lign: MR3 -'P

ssem%ly: --S6

Base caller: PyroBayes

Kariant detection: MR3 6M

• 7ost reduction: = forpersonal genomics


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The gro)th pattern of Streptomyces sp. MO= $M# on nutrient %roth medium at Ao7

Comparison of the genomes of marine sediment*

deri!ed strain #,23%$' +ith those of terrestrial

origin #,2R' +ill pro!ide insight into theen!ironmental adaptation and e!olution of

Streptomyces species1

pplications of micro%iology genome


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pplications of micro%iology genomese"uencing

http:00%giamericas.com


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enome ssem%ly $ nnotation


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enome ssem%ly $ nnotation4Streptomyces sp. MO= $ M#5

The reads )ere produced from a single lane of!llumina !!x se"uencing machine.

The %reakdo)ns of the analysis are:

• -hort reads "uality filtering and trimming.• /e novo assem%ly using !llumina short reads.• Heatures annotation of the assem%led contigs3

including 7/-3 t#,3 r#,3 ri%osome %inding site3signal peptide cleave site3 transmem%rane helix3

repeat regions 4inverted and tandem5.• Hunctional annotation of the assem%led contigs

using -66/ su%system annotation4http:00))).theseed.org05.

A i


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eatures Annotation

• 7/-3 using #-T gene prediction

• t#,3 using t#,2-can -6

• r#,3 using rnammer

• #B-3 using r%sfinder

• -ignal peptide3 using -ignalP

• Transmem%rane helix3 using TM8MM

• !nverted repeat region3 using !#H

• Tandem repeat region3 using T#H


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The num%ers of feature found in the assem%ledgenome using its respective tools are:

eatures ,23%$ #Count' ,2R #Count'

enome -i&e J3CJ3FQJ %p ==3DAJ3AJF %p N

#$ 6420=3 N

t' 65 186

r' 3 12

5107

ransmembrane &eli 1 3312

ignal ,e!tie 42 801

n:erte e!eats 204 318

anem e!eats 3777 3320


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6


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,23%$

,2R

6 s m o t i c s

t r e s s

6 x i d a t i i !

e s t r e

s s

N

unctional Abundance

Analysis


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7hloro%iBasidiomycotaLentisphaeraeunclassified 4derived from other se"uences57hrysiogenetes,itrospirae ctino%acteriaProteo%acteria

Bacteroidetes2-treptophyta

rthropoda7yano%acteria7hordataHirmicutesunclassified 4derived from Bacteria5Kerrucomicro%ia cido%acteria scomycota-pirochaetesPlanctomycetes7hloroflexi

,23%$,2R

enome Plasticity and 6volution

777 enome re2se"uencing at ,!T6

8o+est Common Ancestor


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6utline of the pipeline for genomic analysis of secondary metabolites1

2edema 2 9 et al1 Nucl1 Acids Res1 %$$:nar1g;r4&&

The uthor4s5 ==. Pu%lished %y 'xford University Press.antiS2AS9 antibiotics < Secondary 2etabolite Analysis S9ell

,enomic analysis of secondary metabolites of Streptomyces in this

http://antismash.secondarymetabolites.org/http://antismash.secondarymetabolites.org/http://antismash.secondarymetabolites.org/


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,23%$ Number ,2R Number

erpene 4 erpene $&

$/8S = $/8S 4&

2ctoine$

2ctoine

07tyrolactone 07tyrolactone

1pks 1pks &=

1pks:t4pks $ 1pks:t4pks

0acteriocin > 0acteriocin $$

$rps:b7tyrolactones $ $rps:b7tyrolactones %

@pks $ @pks >

$rps:t1pks $ $rps:t1pks

antipeptide antipeptide >

Siderophore Siderophore &

'ther 'ther $4

*pks $ *pks

4pks $ 4pks $5

4pks:t1pks $ 4pks:t1pks 4

"ndole $

)serlactone

@pks:bacteriocin $

0acteriocin:lantipeptide

*pks:nrps $

'ligosaccaride:

t@pks

$

0otal ? %$

,enomic analysis of secondary metabolites of Streptomyces in thisstudy

MO=

M#

http:00antismash.seconda

rymeta%olites.org0upload0cdf%D%2eQDG2Faa%2%G=G2F%fJeGGa%deD0index.html

http://antismash.secondarymetabolites.org/upload/b1e575df-4a88-447f-875e-106c0eefbc06/index.htmlhttp://antismash.secondarymetabolites.org/upload/18c45ea5-e246-43cf-a342-8651f861a282/index.htmlhttp://antismash.secondarymetabolites.org/upload/18c45ea5-e246-43cf-a342-8651f861a282/index.htmlhttp://antismash.secondarymetabolites.org/upload/b1e575df-4a88-447f-875e-106c0eefbc06/index.html


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,enomic comparison of secondary metabolites gene

clusters bet+een Streptomyces ,23%$ < .1$4

=AA homology

@=*$%%

=D

Basp symbiont

enome si&e: J.CA M%2arine sediment*deri!ed

enome si&e: J.C= M%

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