Bioinformatics and Personalized Medicine Nicholas A. Shackel 1 A.W. Morrow Gastroenterology and Liver Centre Royal Prince Alfred Hospital 2 Liver Laboratory, Centenary Institute Sydney, NSW, Australia 3 Medicine University of Sydney Sydney, NSW, Australia.
36
Embed
Dr Nicholas Shackel - Bioinformatics and Personalised Medicine
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Bioinformatics and Personalized Medicine
Nicholas A. Shackel
1 A.W. Morrow Gastroenterology and Liver Centre Royal Prince Alfred Hospital
2 Liver Laboratory, Centenary InstituteSydney, NSW, Australia
3 Medicine University of SydneySydney, NSW, Australia.
Overview
• Genome / Transcriptome
• Understanding disease
– mRNA Expression
– miRNA Expression
• Personalised medicine
• New technologies
3
Bioinformatics
• A long term goal of Bioinformatics is to discover the causal processes among genes, proteins, and other molecules in cells
• This can be achieved by using data from High Throughput experiments, such as microarrays, deep-sequencing and proteomics
3
4
Functional Genomics
Cell Nucleus
Chromosome
Protein
Graphics courtesy of the National Human Genome Research Institute
Gene (DNA)Gene (mRNA), single strand
Systems Biology
New Paradigm
“ The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration..."
(Sauer Science April 2007)
Genome
• 3 billion bases (x2)
• 1.5% protein encoding = 23,000 unique proteins
• >100,000 alternate splicing and post translation protein variants
• 1.5-8% of the genome has regulatory elements– UTRs, Promoters etc
• Single Nucleotide Polymorphism (SNP) 1:100 – 1:1000
• 90% “Junk” DNA– Unrecognized regulatory elements?– Entropy rate for coding and non-coding regions different
• Transcription without translation
Transcriptomes
• Total transcriptome (mRNA pool)– SAGE ~ 100 000 (www.sagenet.org)