Massively parallel sequencing technology has a wide range of
applications. Its use in SNP detection is already widespread and
promises results of high accuracy. The aim is to validate data
generated with the Genome Analyser II. SNPs, detected by using
different mapping and SNP estimation parameters implemented in the
bioinformatics tool CLC were compared to SNPs that are detected by
using the method of conventional Sanger sequencing. Lines of D.
mauritiana were used. As reference genome in CLC I was bound to use
the genome of D. simulans, because there is no available genome of
D. mauritiana until now. Using a stringent parameter set (short
parameter set) that allows few mismatches of reads when mapping
against the reference genome, only few regions are recovered which
show divergence to the reference. 60%-81% SNPs detected with this
parameter set are false positive ones compared to Sanger
sequencing. If a less stringent parameter set is used this results
in a very high number of false positive SNPs. Two times more SNPs
were recovered with this parameter set, thereof 70-80% are false
positive ones. Based on my results I drew conclude that the best
approach for SNP detection is to make one first run permitting a
high number of mismatches. The next step should be the use of more
stringent values to reduce the high number of false positive SNPs.
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