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Biology%20and%20Bioinformatics

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Biology and Bioinformatics Gabor T. Marth Department of Biology, Boston College marth_at_bc.edu How do we find polymorphisms? SNP discovery -- Methods SNP discovery ... – PowerPoint PPT presentation

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Title: Biology%20and%20Bioinformatics


1
Biology and Bioinformatics
BI820 Seminar in Quantitative and Computational
Problems in Genomics
Gabor T. Marth
Department of Biology, Boston College marth_at_bc.edu
2
The animal cell
3
DNA the carrier of the genetic code
4
DNA organization chromosomes
5
Translation of genetic information
6
DNA sequencing informatics
DNA sequencing informatics
7
DNA organization
8
Genome annotation
9
De novo gene prediction
10
Similarity-based gene prediction
11
Gene localization
12
Genetic mapping
13
Gene function
14
Expression analysis
15
Protein structure
16
RNA structure
17
Protein structure prediction
18
RNA structure prediction
19
DNA evolution
20
Evolution of chromosome organization
21
Evolution of gene structure
22
Evolution of DNA sequence
23
Comparative genomics
24
Phylogenetics
25
Mechanisms of molecular evolution
26
Sequence variations
  • Human Genome Project produced a reference genome
    sequence that is 99.9 common to each human being

27
Why do we care about variations?
phenotypic differences
28
How do we find polymorphisms?
  • look at multiple sequences from the same genome
    region

29
SNP discovery -- Methods
30
SNP discovery Computer tools
31
SNP discovery Mining Projects
30,000 clones
gtCloneX ACGTTGCAACGT GTCAATGCTGCA
gtCloneY ACGTTGCAACGT GTCAATGCTGCA
25,901 clones (7,122 finished, 18,779 draft with
basequality values)
21,020 clone overlaps (124,356 fragment overlaps)
ACCTAGGAGACTGAACTTACTG
ACCTAGGAGACCGAACTTACTG
32
SNP databases and characteristics
  • access to variation data
  • SNP properties
  • reliability of information

33
Where do variations come from?
  • sequence variations are the result of mutation
    events

TAAAAAT
34
Mutation rate
  • higher mutation rate (µ) gives rise to more SNPS

35
Recombination
accgttatgtaga
accgttatgtaga
accgttatgtaga
36
Demographic history
small (effective) population size N
  • different world populations have varying
    long-term effective population sizes (e.g.
    African N is larger than European)

37
Modeling
stationary
expansion
collapse
bottleneck
past
history
present
MD (simulation)
AFS (direct form)
38
Ancestral inference
modest but uninterrupted expansion
bottleneck
39
The signatures of selection
  • selective mutations influence the genealogy
    itself in the case of neutral mutations the
    processes of mutation and genealogy are decoupled

40
Association and haplotype structure
linkage disequilibrium
41
Computer simulations the Coalescent
42
Medical utility?
?
clinical phenotype
molecular markers
43
Mapping disease-causing loci
genetic linkage
44
Forensic applications
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