1 / 24

Outline to SNP bioinformatics lecture

Outline to SNP bioinformatics lecture. Brief introduction SNPs in cell biology SNP discovery SNP assessment SNP databases SNPs in genome browsers. Single Nucleotide Polymorphisms. Must be present in at least 1% of the population

roz
Download Presentation

Outline to SNP bioinformatics lecture

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Outline to SNP bioinformatics lecture • Brief introduction • SNPs in cell biology • SNP discovery • SNP assessment • SNP databases • SNPs in genome browsers

  2. Single Nucleotide Polymorphisms • Must be present in at least 1% of the population • Most (90%) of the sequence variation between two genomes • Two humans differ 0.1% • 1/300 bp in the human genome • Lower in coding regions • 10 million in the human genome

  3. Categories of SNPs • Missense/Non-synonymous • Changes an amino acid • About half of the SNPs in coding sequence • Can alter function and or structure of the protein • Cause of most monogenetic diseases • Hemochromatosis (HFE) • Cystic fibrosis (CFTR) • Hemophilia (F8) • Nonsense • Introduces a stop codon • Same consequences as non-synonymous

  4. Categories of SNPs • Synonymous • Does not alter the coding sequence • May alter splicing • Non-coding • Can be located in promoter or regulatory regions • Can impact the expression of the gene • All SNPs can be used as markers

  5. Use to cell biologist • Association studies • Use SNPs as markers to find regions associated with phenotype • Causative SNPs • Altered protein • Altered expression • Regions of altered conservation between strains/species/individuals • Evolutionary analyses • Etc…

  6. SNP discovery • Discovery of SNPs usually from sequencing • Discovery is based on separating sequencing errors from ’real’ differences and assessing the frequency in the sequenced population • Separation of parologous sequences • Validation, genotyping

  7. SNP discovery resources • Polybayes • SNP discovery in redundant sequences • Polyphred • SNP discovery based on phred/phrap/consed • NovoSNP • Graphical identification of SNPs

  8. Example: PolyPhred • Detects heterozygotes from chromatograms • Runs together with phred/phrap/consed • Command line

  9. SNP assessment • Assess SNPs for functional effects • Non-synonymous SNPs • Conservation across species • Amino acid properties • Protein structure • Transmembrane regions, signal peptides etc.

  10. SNP assessment resources • SIFT • PolyPhen • Pmut • SNPs3D • PANTHER PSEC • TopoSNP • MAPP • Etc

  11. Example: SIFT • Sorting Intolerant From Tolerant • Builds an alignment of similar sequences • Calculates a score based on the aa in the alignment • Takes the environment into account • Takes the properties of the aa into account • Does not use structure

  12. SNP databases • Maps of SNPs in human, mouse, etc • Haplotype maps • Functional SNPs • Disease databases

  13. SNP databases • dbSNP • F-SNP • HGVBase • PolyDoms • OMIN • Etc…

  14. Example: dbSNP • 50 million submissions • 18 million clusters • 7 million in genes • 44 organisms • 91 million SNPs submitted

  15. dbSNP • Search for SNPs, location, etc • Information submitted on method, flanking sequence, alleles, population, sample size, validation etc • Information computed on SNPs at same location including functional analysis, population diversity etc

  16. SNPs in genome browsers • Ensembl • UCSC

  17. Example: UCSC

  18. HapMap • Aim: a haplotype map of the human genome describing common patterns of sequence variation • A haplotype map is based on alleles of SNPs close together are inherited together • HapMap will identify which SNPs are informative in mapping, reducing the number of SNPs to genotype by a magnitude • Populations from Asia, Europe and Africa • 2nd generation map with over 3.1 million SNPs

  19. Ng PC, Henikoff S. Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet. 2006;7:61-80. Review. Bhatti P, Church DM, Rutter JL, Struewing JP, Sigurdson AJ. Candidate single nucleotide polymorphism selection using publicly available tools: a guide for epidemiologists. Am J Epidemiol. 2006 Oct 15;164(8):794-804. Epub 2006 Aug 21. Clifford RJ, Edmonson MN, Nguyen C, Scherpbier T, Hu Y, Buetow KH. Bioinformatics tools for single nucleotide polymorphism discovery and analysis. Ann N Y Acad Sci. 2004 May;1020:101-9. Review. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851-861. 2007.

More Related