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Whole genome sequencing enables a high resolution view of the humangenome and enables unique insights into copy number variations in anunprecedented scale. Numerous tools and studies have already been introduced that provide confirmatory and new genomic variability datain individuals and across populations. We investigate two such methods, CNV-seq and FREEC and compare their outputs when applied to five whole genome sequences representing four populations. We focus onthe ability of these tools to detect segments from two sets of segments known to vary across populations, identify their strengths and drawbacks and discuss the direction and the challenges of tools thatdetect copy number variation in sets of human genomes.

The use of a priori knowledge in aligning targeted sequencing data is investigated using computational experiments. With conventional aligners such as Bowtie, BWA or MAQ, alignment is performed against the whole genome. Using an alignment method in which the genomic position information from the target capture is incorporated, alignment can be done to just the target region. Investigating the effect of realistic target size, read length, read redundancy, the amount of off-target reads and sequencing error rate, improvements of up to a factor of 8 +/- 0.3 in alignment speed are found using an implementation of the Needleman-Wunsch algorithm which makes use of direct stringcomparison. This results in a total alignment time in target sequencing of around 1 min.