17.05.26

Lecture 3 - Genomics

* What is Genomics?
   ► We should remember two things.
       - 'T' = 7 billion persons (species diversity) + 7 billion bases (variability in one person)
          
   ► Every life or species have genome, and variation of each genome is different.

 

* GEP/T graph.
   ► Geno + Enviro -> Pheno/Trait (almost cases are disease)
      
      - Prediction is conducted by experiment, bioinformatics, etc.

   ► The challenge : As life is so complicated, prediction is so hard. We have to consider environmetal complexity with genomic complexity.
      - Possible solution : using AI based on big data.

   ► Solutions
       (1) Exact large amount of data.
           - It is so important.
           - typing (cheap and efficient) : simple test, approximation (not 100% prediction).
           - sequencing (ultimate) : almost 100% prediction, digital device, it can have error (we don't know reason of error), ex) hardware.
       (2) Need principle, law, and algorithms.
           - We need to know related principle when we face to some facts.
       (3) Big computer.
   

* Genome sequencing.
   ► 2^nd generation : multiple molecule -> approximation
   ► 3^rd generation : single molecule
   ► 4^th generation : nanopore + membrane

   ► Base calling : computer notifies types of bases.
   ► NSG Data analysis : mapping based on reference gene.

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* Close species comparative genomics.
   ► If we sequencing genome of new species, we have to find and get right right samples.
       - we should pick sample which has interesting phenotype.
       - And then, set up experimental design.

   ► Gene assembly : assemble gene fragments after putting the known gene between the intereted gene fragments.
       - expensive
       - After assmbling genome of one species, we can use it as reference genome.

   ► MRCA : most recent common ancestor -> we can predict