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Lecture notes of 2 pages for the course Genes And Bioinformatics at QMUL

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Introduction to bioinformatics
Learning objectives:

1. Understand the scale of biological information and the need for bioinformaticians
2. Make information and instructions computable
3. Describe important biological databases
4. Chose bioinformatics software
5. Use
- FASTA format
- Genes names
- Protein families databases (pfam)
- Gene ontologies
- NCBI databases
- NCBI BLAST (web portal)

The scale of biological information

 Human body
- 10 trillion cells
- Brain has 15-33 billion cells
- 23 chromosome pair in every body cell
- 4 types of bases, which equals ~3.2 billion bases in the human body

What is bioinformatics?

 The science of collecting and analysing complex biological data such as genetic codes.

Computability of information

 Computer systems think/deal differently to the human brain
- Computer systems cannot understand ambiguity
- Therefore, need to be accurate and precise
- Computers think in a binary manner
 Human brain thinks differently
- Human brain has 33 billion cells that have multiple connection
- multiple connections allows brain to not think in a binary manner
- Computer has large number of switches that occur on or off in the computer chip = computer thinks binary.
- More switches = faster computer
 Moore’s Law
- prediction that the transistors / switches on a computer chip doubles every 2 years
- computers getting faster
- white line
another way of thinking about it is the cost of computer switches is halving every 2 years
- blue line
costs of generating DNA sequence data
initially the same as Moore’s law
but then decrease in cost
due to increase biological data compared to computer chip capability
This brings challenges i.e. placing data in suitable place to access.

Brief overview of ‘omics

 Omics refers to information flow inside the cell
 DNA gives genome hence genomics
 RNA gives transcript, DNA is transcribed into RNA hence transcriptonomics
 DNA = genomics
 RNA = transcriptonomics
 Protein = proteomic
 Metabolites = metabolomics

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