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<p><strong>Lecture 3 - Genomics</strong></p>
<p>* What is <u>Genomics</u>?<br />
► We should remember two things.<br />
- 'T' = 7 billion persons (species diversity) + 7 billion bases (variability in one person)<br />
<img alt="" src="/ckfinder/userfiles/images/%EA%B7%B8%EB%A6%BC1.png" style="height:300px; width:400px" /><br />
► Every life or species have genome, and variation of each genome is different.</p>
<p> </p>
<p>* GEP/T graph.<br />
► Geno + Enviro -> Pheno/Trait (almost cases are disease)<br />
<img alt="" src="/ckfinder/userfiles/images/%EA%B7%B8%EB%A6%BC2.png" style="height:286px; width:500px" /><br />
- Prediction is conducted by experiment, bioinformatics, etc.<br />
<br />
► The challenge : As life is so complicated, prediction is so hard. We have to consider environmetal complexity with genomic complexity.<br />
- Possible solution : using AI based on big data.<br />
<br />
► Solutions<br />
(1) Exact large amount of data.<br />
- It is so important.<br />
- typing (cheap and efficient) : simple test, approximation (not 100% prediction).<br />
- sequencing (ultimate) : almost 100% prediction, digital device, it can have error (we don't know reason of error), ex) hardware.<br />
(2) Need principle, law, and algorithms.<br />
- We need to know related principle when we face to some facts.<br />
(3) Big computer.<br />
</p>
<p>* Genome sequencing.<br />
► 2<sup>nd</sup> generation : multiple molecule -> approximation<br />
► 3<sup>rd</sup> generation : single molecule<br />
► 4<sup>th</sup> generation : nanopore + membrane<br />
<br />
► Base calling : computer notifies types of bases.<br />
► NSG Data analysis : mapping based on reference gene.</p>
<hr />
<p>* Close species comparative genomics.<br />
► If we sequencing genome of new species, we have to find and get right right samples.<br />
- we should pick sample which has interesting phenotype.<br />
- And then, set up experimental design.<br />
<br />
► Gene assembly : assemble gene fragments after putting the known gene between the intereted gene fragments.<br />
- expensive<br />
- After assmbling genome of one species, we can use it as reference genome.<br />
<br />
► MRCA : most recent common ancestor -> we can predict </p>
<p>* What is <u>Genomics</u>?<br />
► We should remember two things.<br />
- 'T' = 7 billion persons (species diversity) + 7 billion bases (variability in one person)<br />
<img alt="" src="/ckfinder/userfiles/images/%EA%B7%B8%EB%A6%BC1.png" style="height:300px; width:400px" /><br />
► Every life or species have genome, and variation of each genome is different.</p>
<p> </p>
<p>* GEP/T graph.<br />
► Geno + Enviro -> Pheno/Trait (almost cases are disease)<br />
<img alt="" src="/ckfinder/userfiles/images/%EA%B7%B8%EB%A6%BC2.png" style="height:286px; width:500px" /><br />
- Prediction is conducted by experiment, bioinformatics, etc.<br />
<br />
► The challenge : As life is so complicated, prediction is so hard. We have to consider environmetal complexity with genomic complexity.<br />
- Possible solution : using AI based on big data.<br />
<br />
► Solutions<br />
(1) Exact large amount of data.<br />
- It is so important.<br />
- typing (cheap and efficient) : simple test, approximation (not 100% prediction).<br />
- sequencing (ultimate) : almost 100% prediction, digital device, it can have error (we don't know reason of error), ex) hardware.<br />
(2) Need principle, law, and algorithms.<br />
- We need to know related principle when we face to some facts.<br />
(3) Big computer.<br />
</p>
<p>* Genome sequencing.<br />
► 2<sup>nd</sup> generation : multiple molecule -> approximation<br />
► 3<sup>rd</sup> generation : single molecule<br />
► 4<sup>th</sup> generation : nanopore + membrane<br />
<br />
► Base calling : computer notifies types of bases.<br />
► NSG Data analysis : mapping based on reference gene.</p>
<hr />
<p>* Close species comparative genomics.<br />
► If we sequencing genome of new species, we have to find and get right right samples.<br />
- we should pick sample which has interesting phenotype.<br />
- And then, set up experimental design.<br />
<br />
► Gene assembly : assemble gene fragments after putting the known gene between the intereted gene fragments.<br />
- expensive<br />
- After assmbling genome of one species, we can use it as reference genome.<br />
<br />
► MRCA : most recent common ancestor -> we can predict </p>