sequencing technologies and human genetic variation
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Sequencing Technologies and Human Genetic Variation. By Alfonso Farrugio , Hieu Nguyen, and Antony Vydrin. Overview. Introduction Simulating genomic variation and sequencing Analyzing and comparing different sequencing technologies Algorithms for detecting human genetic variation. - PowerPoint PPT PresentationTRANSCRIPT
By Alfonso Farrugio, Hieu Nguyen, and Antony Vydrin
Sequencing Technologies and Human Genetic Variation
Overview
Introduction
Simulating genomic variation and sequencing
Analyzing and comparing different sequencing technologies
Algorithms for detecting human genetic variation
Introduction
Different people have different mutations in their genomes
A recent study was done (Nature 453, 56-64, 5/1/2008) where 8 human genomes were compared, and 1,695 structural variants were found
Whole-genome shotgun sequencing allows for fast and relatively cheap sequencing of human genomes
New technologies are being developed to allow for accurate detection of human genomic variation
Most of these technologies use short paired reads.
How long should the reads be in order to optimize the process of detecting human genomic variation ?
What algorithms can be used to detect variations in a new individual’s genome ?
Simulating Genomic Variation
Program to take a human genome and add randomly-distributed inversions, insertions, deletions, and SNPs
The number of mutations (and their mean lengths) can be controlled by the user
To simplify, no two mutations can overlap each other (the SNPs are an exception)
Inversions Insertions Deletions
“Intermediate” mutated genome
Original genome
Subtract Deletions
“Intermediate” mutated genome
“Intermediate” mutated genome
SNPs
“Intermediate” mutated genome
(output mutated genome)
Simulating Genomic Sequencing
Program to take a human genome and create paired reads (output read pairs to a file)
The read lengths are all identical, and the separation between reads in a pair is picked randomly based on a normal distribution
The program can simulate sequencing errors when creating the paired reads
Simulating Genomic Sequencing
The user can control the total number of reads, read lengths, the mean of the read separations, and sequencing error rate
Genome to be sequenced
Choose uniformly - distributed random locations
Genome to be sequenced
Create read pair at each location. Choose random direction for each read
L Ld1
L is a constant while d is random (normally distributed)
Read direction
L LRead direction
d2
L LRead direction d3
L Ld2
L Ld3
L Ld1
Resulting paired reads
L Ld2
L Ld3
L Ld1
Paired reads with simulated sequencing errors
program runtime ~window size
1
1
0
50 insertions
100 insertions
500 insertions