Motivation: The advent of clinical exome sequencing will require new tools to handlecoverage data and making it relevant to clinicians. That means genes over targets, smartsoftware over BED-files, and full stack, automated solutions from BAM-files to genetic testreport. Fresh ideas can also provide new insights into the factors that cause certain regionsof the exome to receive poor coverage.Results: A novel coverage analysis tool for analyzing clinical exome sequencing data has beendeveloped. Named Chanjo, it’s capable of converting between different elements such astargets and exons, supports custom annotations, and provides powerful statistics andplotting options. A coverage investigation using Chanjo linked both extreme GC content andlow sequence complexity to poor coverage. High bait density was shown to increasereliability of exome capture but not improve coverage of regions that had already proventricky. To improve coverage of especially very G+C rich regions, developing new ways toamplify rather than enrich DNA will likely make the biggest difference.

Massive parallel sequencing (NGS) is useful in detecting and later classifying somatic driver mutations in cancer tumours. False-positive variants occur in the NGS workflow and they may be mistaken for low frequency somatic cancer mutations in a patient sample. This pushes the need for decreasing the noise rate in the NGS workflow since it may improve the detection of rare allele frequency variants, in particular cancer mutations. In this project, the aim was to reduce the level of false-positive variants in an NGS workflow. The scope was limited to looking at substitution errors and their neighbouring nucleotides. Alongside this, it was also a way to understand how different types of substitution errors are distributed in the data, if their frequencies are affected by neighbouring nucleotides and how data processing may affect these substitution rates. A bioinformatic pipeline was set up where a commercially available genomic DNA sample with known variants was subjected to different trimming and filtering settings. The goal was to reduce the substitution error rate as much as possible, without removing any true variants from the data. The optimised settings were trimming the sequencing reads with 5 bp from the tail and filtering sequencing reads that contained 5 or more substitutions. Three additional samples, whereof two were clinical and the third commercial, were tested with these settings. The results showed that in all samples, C:G>T:A substitutions were of a higher frequency compared to the rest of the substitution types. For all samples, A:T>C:G substitutions, where the neighbouring nucleotide was a C or a G on each side, had a higher frequency compared to A:T>C:G substitutions with other neighbouring nucleotides on both sides. Those substitution types were especially targeted by the trimming. For the two commercial samples, substitutions that resulted in the nucleotide combinations >XAA or >XTT were of a higher frequency compared to the same substitution types that did not result in those nucleotide combinations. Filtering reads with 5 or more substitutions particularly targeted these substitution types. Consequently, filtering had a greater effect on the commercial samples, compared to the clinical samples. Overall, trimming and filtering helped reduce transversions more than the transitions, increasing the transition/transversion ratio after processing the data. The results suggest that trimming and filtering can be a useful method to computationally reduce the transversion errors introduced in an NGS workflow, but transition errors to a lesser extent, in particular A:T>G:C transitions. To confirm these findings, more samples should be tested using this methodology. To better understand the effect of trimming and filtering on variant calling, the scope could in the future be expanded to also look at small insertions and deletions.