Bioinformatics for High School

8/8/2019 Bioinformatics for High School

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An Introduction toAn Introduction to

BioinformaticsBioinformatics(high-school version)(high-school version)

Ying XuYing Xu

Institute of Bioinformatics, and Biochemistry andInstitute of Bioinformatics, and Biochemistry and

Molecular Biology DepartmentMolecular Biology DepartmentUniversity of GeorgiaUniversity of Georgia

[email protected]@bmb.uga.edu


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The BasicsThe Basics

genes

cell chromosome

ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgat

cgtgtgggtagtagctgatatgatgcgaggtaggggataggatag

caacagatgagcggatgctgagtgcagtggcatgcgatgtcgatg

atagcggtaggtagacttcgcgcataaagctgcgcgagatgattg

caaagragttagatgagctgatgctagaggtcagtgactgatgatc

gatgcatgcatggatgatgcagctgatcgatgtagatgcaataagt

cgatgatcgatgatgatgctagatgatagctagatgtgatcgatggt

aggtaggatggtaggtaaattgatagatgctagatcgtaggta

genome andsequencing

protein

metabolicpathway/network


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BioinformaticsBioinformatics(or computational biology)(or computational biology)

This interdisciplinary science is aboutThis interdisciplinary science is aboutproviding computational support toproviding computational support to

studies onstudies on linking the behavior of cells,linking the behavior of cells,organisms and populations toorganisms and populations to thetheinformation encoded in the genomesinformation encoded in the genomes

Temple Smith

ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgat

cgtgtgggtagtagctgatatgatgcgaggtaggggataggatag

caacagatgagcggatgctgagtgcagtggcatgcgatgtcgatg

atagcggtaggtagacttcgcgcataaagctgcgcgagatgattg

caaagragttagatgagctgatgctagaggtcagtgactgatgatc

gatgcatgcatggatgatgcagctgatcgatgtagatgcaataagt

cgatgatcgatgatgatgctagatgatagctagatgtgatcgatggt

aggtaggatggtaggtaaattgatagatgctagatcgtaggta


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Information Encoded inInformation Encoded in

GenomesGenomes

What information? And how to find and interpretWhat information? And how to find and interpretit?it?

Working molecules (proteins, RNAs) in our cellsWorking molecules (proteins, RNAs) in our cells

ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcga

ggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggta

ggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtga

ctgatgatcgatgcatgcatggatgatgcagctgatcgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta

bacterial

cell


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GenomesGenomes

How to find where protein-encoding genes are in a genome?How to find where protein-encoding genes are in a genome?

A genome is like a book written in words consisting of 4A genome is like a book written in words consisting of 4letters (A, C, G, T), and each protein-encoding gene is likeletters (A, C, G, T), and each protein-encoding gene is like

an instruction about how the protein is madean instruction about how the protein is made

People have found that the six-letter words (e.g., AAGTGC)People have found that the six-letter words (e.g., AAGTGC)have different frequencies in genes from non-gene regionshave different frequencies in genes from non-gene regions

ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagcggatgctgagtgcagtggcatgc

gatgtcgatgatagcggtaggtagacttcgcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgctagaggtcagtgactgatgatcgatgcatgcatggatgatgcagctgat

cgatgtagatgcaataagtcgatgatcgatgatgatgctagatgatagctagatgtgatcgatggtaggtaggatggtaggtaaattgatagatgctagatcgtaggta


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GenomesGenomes

Frequency in genes (AAA ATT) = 1.4%; Frequency in non-genes (AAA ATT) = 5.2%Frequency in genes (AAA GAC) = 1.9%; Frequency in non-genes (AAA GAC) = 4.8%

Frequency in genes (AAA TAG) = 0.0%; Frequency in non-genes (AAA TAG) = 6.3%

.

AAAATTAAAATTAAAGACAAAATTAAAGACAAACACAAAATTAAATAGAAATAGAAAATT ..

Is this a gene or non-gene region if you have to makea bet?


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GenomesGenomes

Preference model: for each 6-letter word X (e.g., AAA AAA), calculate its frequencies in

gene and non-gene regions, FC(X), FN(X) calculate Xspreference value P(X) = log (FC(X)/FN(X))

Properties: P(X) is 0 if X has the same frequencies in gene and non-gene regions P(X) has positive score if X has higher frequency in gene than in non-

gene region; the larger the difference, the more positive the score is P(X) has negative score if X has higher frequency in non-gene than in

gene region; the larger the difference, the more negative the score is

Gene prediction: given a DNA region, calculate the sum of P(X)values for all 6-letter words X in the region; if the sum is larger than zero, predict gene otherwise predict non-gene


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GenomesGenomes

You just learned your first bioinformatics methodYou just learned your first bioinformatics methodfor gene prediction for gene prediction congratulationscongratulations!!


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GenomesGenomes

Ok, we now have learned how to find genes encodedOk, we now have learned how to find genes encodedin a genomein a genome

How do we find out what they do (their biologicalHow do we find out what they do (their biologicalfunctions, e.g. sensors, transportors, regulators,functions, e.g. sensors, transportors, regulators,

enzymes)?enzymes)?


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GenomesGenomes

People have observed that similar protein sequences tend toPeople have observed that similar protein sequences tend tohave similar functionshave similar functions

Over the years, many genes have been thoroughly studied indifferent organisms,e.g.,human, mouse, fly, ., rice,

their biological functions have been identified and documented

For a new protein, scientists can possibly predict its function by

identifying well-studied proteins in other organisms, that havehigh sequence similarities to it

This works for ~60% of genes in a newly sequenced genome


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GenomesGenomes

Scientists have developed computationalScientists have developed computationaltechniques fortechniques for identifying regulatory signals that controls geneidentifying regulatory signals that controls gene

transcriptiontranscription

predicting protein-protein interactionspredicting protein-protein interactions

elucidating biological networks for a particular functionelucidating biological networks for a particular function ... and elucidating many other information... and elucidating many other information


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GenomesGenomes

E. Coli O157 and O111 are human pathogenic while E. ColiK12 is not;

Can we tell why? Which genes or pathways in E. coli O157

and O111 are responsible for the pathogenicity?


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GenomesGenomes

E.coliK-12

E.coliO157

B

.pseudomallei

P.furiosus

Randomseq

humanchromosome#1


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GenomesGenomes

Red: prokaryotes

Blue: eukaryotes

Green: plastids

Orange: plasmids

Black: mitochondria

x-axis: average of variations of the K-mer

frequencies,

y-axis: average barcode similarity among

fragments of a genome


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GenomesGenomes

Yes, biologists can derive a lot of information fromYes, biologists can derive a lot of information from

genomes nowgenomes now

but we are far from fully understanding any genomebut we are far from fully understanding any genomeyet, even for the simplest living organisms, bacteriayet, even for the simplest living organisms, bacteria

We can clearly use new ideas from bright young mindsWe can clearly use new ideas from bright young minds interested in doing bioinformatics? interested in doing bioinformatics?


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Linking Genome Information toLinking Genome Information to

Biological Systems BehaviorsBiological Systems Behaviors

To fully understand cellular behaviors, we need toTo fully understand cellular behaviors, we need to elucidate information encoded in the genome, andelucidate information encoded in the genome, and

understand working molecules, encoded by the genome,understand working molecules, encoded by the genome,

behaves according to the physical laws on earth!behaves according to the physical laws on earth!

ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgggtagtagctgatatgatgcgaggtaggggataggatagca

acagatgagcggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttcgcgcataaag

gene

protein


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Key Drivers ofKey Drivers of

BioinformaticsBioinformatics Human genome project has fundamentallyHuman genome project has fundamentallychanged biological sciencechanged biological science

A key consequence of the genome project isA key consequence of the genome project isscientists learned that they can producescientists learned that they can produce

biological data massivelybiological data massively genome sequencesgenome sequences

microarray data for gene expression levelsmicroarray data for gene expression levels yeast two hybrid systems for protein-protein interactionsyeast two hybrid systems for protein-protein interactions

and other high-throughput biological dataand other high-throughput biological dataThese data reflect the cellular states,molecular structures and functions, incomplex ways


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BioinformaticsBioinformatics

and let bioinformaticians to (help to) decipherand let bioinformaticians to (help to) decipher

the meaning of these data, like in genomethe meaning of these data, like in genomesequencessequences

Together, high-throughput probing technologiesTogether, high-throughput probing technologiesand bioinformatics are transforming biologicaland bioinformatics are transforming biological

science into a new science more like physicsscience into a new science more like physics


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BioinformaticsBioinformatics

Like physics, whereLike physics, where general rules and lawsgeneral rules and laws areare

taught at the start,taught at the start, biology will surely bebiology will surely bepresented to future generations of students as apresented to future generations of students as a

set of basic systemsset of basic systems ....... duplicated and....... duplicated and

adapted to a very wide range of cellular andadapted to a very wide range of cellular and

organismic functions,organismic functions, following basic evolutionaryfollowing basic evolutionary

principles constrained by Earths geologicalprinciples constrained by Earths geological

history.history. Temple SmithTemple Smith,, Current Topics in Computational Molecular BiologyCurrent Topics in Computational Molecular Biology


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Biomarker IdentificationBiomarker Identification

Our goal is to identify markers in blood that canOur goal is to identify markers in blood that cantell if a person has a particular form of cancertell if a person has a particular form of cancer

in a similar fashion to doingpregnancy test using a test kit,

possibly at home


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Microarray gene expression data allow comparativeMicroarray gene expression data allow comparativeanalyses of gene expression patterns in canceranalyses of gene expression patterns in cancer versusversusnormal tissuesnormal tissues

on cancertissues

on normaltissues

Finding genes showing

maximum difference in theirexpression levels betweencancer and normal tissues


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proteins A, , Zhighly expressed incancer


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QuestionQuestion:: Can we predict which of these tissue markerCan we predict which of these tissue markerproteins can get secreted into blood circulation so we canproteins can get secreted into blood circulation so we canget markers in blood?get markers in blood?

Through literature search, we found over proteins beingThrough literature search, we found over proteins beingsecreted into blood circulation due to various physiologicalsecreted into blood circulation due to various physiologicalconditionsconditions

We then trained a classifier to identify features thatWe then trained a classifier to identify features that

distinguish between proteins that can be secreted into blooddistinguish between proteins that can be secreted into bloodand proteins that cannotand proteins that cannot


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We have developed a classifier to distinguish blood-We have developed a classifier to distinguish blood-secretory proteins and other proteinssecretory proteins and other proteins

On a test set with 52 positive data and 3,629 negative data,On a test set with 52 positive data and 3,629 negative data,our classifier achievesour classifier achieves

89.6% sensitivity, 98.5% specificity and 94% AUC89.6% sensitivity, 98.5% specificity and 94% AUC


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The predicted marker proteins can be validatedThe predicted marker proteins can be validatedusing mass spectrometry experimentusing mass spectrometry experiment


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If successful, it will be possible to test for cancerIf successful, it will be possible to test for cancerusing a test-kit like pregnancy test-kitsusing a test-kit like pregnancy test-kits


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Take-Home MessageTake-Home Message

Biological science is under rapid transformation because ofBiological science is under rapid transformation because ofhigh-throughput measurement technologies andhigh-throughput measurement technologies and

bioinformaticsbioinformatics

As an emerging field, bioinformatics is about usingAs an emerging field, bioinformatics is about usingcomputational techniques to solve biological problems, andcomputational techniques to solve biological problems, and

represents the future of biologyrepresents the future of biology


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THANK YOU!

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Bioinformatics for High School