Reference mapping and variant detection

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Reference mapping and variant
detection
Peter Tsai
Bioinformatics Institute, University of Auckland
Reference mapping
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The mapping is the process of comparing each read
with the reference genome.
There are many different software available to perform
reference mapping
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Multiple placement of reads (multi-hits)
Allow gaps
Don’t allow gaps at all
Limits on number of mis-matches
Assess your mapping results
◦ % of total reads mapped
◦ % of uniquely mapped reads
◦ Coverage statistics, variance in depth
Mapped read depth
Variant detection


Identification of point mutation, short insertion and
deletion.
We go thought every column of the alignment and see
how many alleles are found and how many are
different to the reference genome.
Reference: ACGAAACGTAGTGAGGAC-GTA
sample: ACCAAACGTAGAGAGGACCGTA
SNP
SNP
indels
Complexity of variant detection
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
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2nd generation sequencing is NOT single molecule sequencing
Due to the PCR amplification, some DNA fragments will be
sequenced more often than others => results in uneven
coverage across the genome.
This would provide false support in variant detection, as we
are usually more confident in variants that has higher
coverage support.
Solution: Mark or remove exact duplicate reads when doing
variant detection.
Complexity of variant detection
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Cloning process artifacts (e.g. PCR induced mutations).
Error rate associated with the sequence reads.
Error rate associated with the mapping.
Reliability of the reference genome.
Calling a variant

A (ref): 0%
G: 100%
A (ref): 7%
T: 93%
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A hard cut-off in percentage
of difference to reference
base.
75% as minimum threshold
for a variant to be call
homozygous variant.
Percentage based cut-off
assumes you have sufficient
coverage.
When to call a variant ?
A: 18%
C: 0%
G: 55%
T: 27%
Alignment considerations

Perform local realignment and calculate mapping score
to determine which one is better.
What depth do I need ?
Factors to consider
Read length
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Sequencing depth
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Require sufficient depth, ~30x
Base call quality for each supporting bases
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Longer reads are more likely to be mapped with high confidences
Use high quality bases, Q30
Mapping quality
Local realignment to improve variant calling
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