Variant discovery

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BIT 815: Analysis of Deep Sequencing Data
Variant discovery
• Different approaches: With or without a reference?
• With a reference
– Limiting factors are CPU time and memory required
– Crossbow – a cluster-based cloud computing approach
• Without a reference
– CPU time and RAM requirements are still limiting
– Now error rate and distribution become limiting also
– Statistical methods for estimating probability that a
putative SNP is a true SNP are still developing
– Some analytical methods require experimental designs
specifically for the variant discovery objective
Structural variants in 7 flavors
Figure from Alkan et al, Nature Reviews Genetics 2011 doi:10.1038/nrg2958
Different technologies have different resolutions
Figure from Alkan et al, Nature Reviews Genetics 2011 doi:10.1038/nrg2958
Four strategies for
discovery of structural
variation using parallel
sequencing
technologies
All are affected by the
repetitive sequence
content of the genome
and by sampling error
Assembly de novo of a
complete genome
sequence is the most
expensive but most
complete approach
Figure from Alkan et al, Nature Reviews Genetics 2011 doi:10.1038/nrg2958
BIT 815: Deep Sequencing
Comparison of various methods
for variant discovery, based on
analysis of 185 human genomes
(a) Two microarray-based methods
compared with Sanger sequencing of
fosmid ends (40 kb inserts) – counts
include only variants > 5 kb
(b) Three parallel-sequence-based
methods compared.
• The numbers of variants discovered
is several-fold higher than in part (a)
• There is relatively little overlap
among the variants discovered using
different methods
Figure from Alkan et al, Nature Reviews Genetics 2011 doi:10.1038/nrg2958
Small indels create problems
for SNP-calling programs
Incorrectly aligned
Correctly aligned
Incorrectly aligned
Artifactual SNP calls
Figure from http://samtools.sourceforge.net/mpileup.shtml
Base Alignment Quality (BAQ)
is one approach to dealing with the problem
Figure from http://samtools.sourceforge.net/mpileup.shtml
Small indels create problems
for SNP-calling programs
Incorrectly aligned
Correctly aligned
Incorrectly aligned
BAQ downgrades quality scores of these
bases so they are not considered reliable
by SNP-calling programs
Figure from http://samtools.sourceforge.net/mpileup.shtml
Short Read Multiple Aligner (SRMA)
is another approach – actually re-aligns reads
Figure from Homer and Nelson, Genome Biology 2010, 11:R99
Short Read Multiple Aligner (SRMA)
The downside is computational intensity
From Homer and Nelson Genome Biology 2010, 11:R99
What about rare alleles?
Efficient screening in pooled samples
• Druley TE, et al. (2009) Quantification of rare allelic variants from
pooled genomic DNA. Nat Methods 6(4):263-5.
• Vallania FL, et al. (2010) High-throughput discovery of rare
insertions and deletions in large cohorts. Genome Res
20(12):1711-8.
• Bansal V, et al (2010) Accurate detection and genotyping of SNPs
utilizing population sequencing data.Genome Res 20(4):537-45.
• Bansal V. (2010) A statistical method for the detection of variants
from next-generation resequencing of DNA pools. Bioinformatics
26(12):i318-24.
• Bansal V, et al. (2011) Efficient and cost effective population
resequencing by pooling and in-solution hybridization. PLoS One
6(3):e18353.
• Altmann A, et al (2011) vipR: variant identification in pooled DNA
using R. Bioinformatics 27(13):i77-84.
What about rare alleles?
Comparing across pools adds to power
(a) Five alternate base calls in one of four pooled samples is unlikely to arise by
chance sequencing errors alone – the p-value from the contingency table is 0.002
(b) Five of nine alternate base calls in one pool, with one call in each of the other
three, is a pattern that cannot be distinguished from that expected of sequencing
errors – the contingency table p-value is 0.24
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