Snps and microarray
- 1. SNPs and Gene expression Sucheta Tripathy
- 2. schedule • 6 classes • 2 for SNP and 4 for gene expression • 2 per group and will present in each class. • Subjects and groups will be chosen randomly. • Todays class is introductory, we will discuss fundamental aspects.
- 3. Terminologies • Forward genetics – understanding a genotype by understanding the phenotype. • Reverse Genetics -
- 4. How often SNPs occur? • One in 300 bases – 10 M. Not all single-nucleotide changes are SNPs, though. To be classified as a SNP, two or more versions of a sequence must each be present in at least one percent of the general population.
- 6. Each combination is a haplotype!!!! Not necessarily all 8 haplotypes exist!!!!
- 8. dbSNP and Hapmap project • dbSNP: 2.5 million variations • http://www.ncbi.nlm.nih.gov/SNP/ • Haplotypes are blocks – hapmap focuses on those blocks • http://hapmap.ncbi.nlm.nih.gov/thehapmap. html.en – 2002 – Nigeria, Japan, China, USA
- 9. Gene Expression • Yeast 1997.
- 10. Cy3: 570 Cy5: 670 Two Channel and single Channel microarray Two channel – two conditions Spike-in control probes are there Used for Normalization -Agilent dual mode; Eppendorf with dualchip Single channel: One condition at a time. Abundance of a transcript will not be known only relative abundance. Affymetrix: Genechip; Illumina BeadChip
- 11. Microarray and Bioinformatics • Experimental design. • Standardization. • Statistical data analysis. • Data storage and visualization.
- 12. Contd… • Experimental design: – Biological replicates. – Technical replicate. – Randomization • Standardization: – Difficult – cant be easily replicated. – Minimum Information About a Microarray Experiment" (MIAME); 2001, nature genetics • http://fged.org/projects/miame/
- 13. Contd.. • Data Analysis: – Image Analysis – gridding of the spots – Data processing: • Background correction • Visualization (MA Plot) – M is log transformation and A is mean average scale Most gene should not change -> Y is 0
- 14. Contd.. • Data Processing: – Normalization (Remove non-biological variation) • Simplest way: Assume all arrays have same median gene expression • Subtract median from each array • Quantile normalization: – Order values in each array – Take average across probes – Substitute probe intensity with average – Change the original order
- 16. Contd.. • Class discovery – Unsupervised methods. – Supervised methods • Draw hypothesis
- 17. Papers Group1: http://www.ncbi.nlm.nih.gov/pmc/articles/P MC3830805/ -> ABO allele – international journal of evolutionary biology Group5: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC 3990764/ -> Semantic modelling associated with SNPs from hapmap data – Genomics and informatics
- 18. Papers • Group 2: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC399 2869/ Group 3: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC321 9629/ Group 4: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC302 2823/
- 19. Next class… • We will discuss this paper. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC 2805859/