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Background Identification of germline variation and somatic mutations is a major issue in human genetics. However, due to the limitations of DNA sequencing technologies and computational algorithms, our understanding of genetic variation and somatic mutations is far from complete. Methods In the present study, we performed whole-genome sequencing using long-read sequencing technology (Oxford Nanopore) for 11 Japanese liver cancers and matched normal samples which were previously sequenced for the International Cancer Genome Consortium (ICGC). We constructed an analysis pipeline for the long-read data and identified germline and somatic structural variations (SVs). Results In polymorphic germline SVs, our analysis identified 8004 insertions, 6389 deletions, 27 inversions, and 32 intra-chromosomal translocations. By comparing to the chimpanzee genome, we correctly inferred events that caused insertions and deletions and found that most insertions were caused by transposons and Alu is the most predominant source, while other types of insertions, such as tandem duplications and processed pseudogenes, are rare. We inferred mechanisms of deletion generations and found that most non-allelic homolog recombination (NAHR) events were caused by recombination errors in SINEs. Analysis of somatic mutations in liver cancers showed that long reads could detect larger numbers of SVs than a previous short-read study and that mechanisms of cancer SV generation were different from that of germline deletions. Conclusions Our analysis provides a comprehensive catalog of polymorphic and somatic SVs, as well as their possible causes. Our software are available at https://github.com/afujimoto/CAMPHOR and https://github.com/afujimoto/CAMPHORsomatic.

20Microsatellites are repeats of 1-6bp units and ~10 million microsatellites have been 21 identified across the human genome. Microsatellites are vulnerable to DNA mismatch 22 germline variation of the microsatellites suggested that the amount of germline variations 34 and somatic mutation rates were correlated. Lastly, analysis of mutations in mismatch 35 repair genes showed that somatic SNVs and short indels had larger functional impact than 36 germline mutations and structural variations. Our analysis provides a comprehensive 37 picture of mutations in the microsatellite regions, and reveals possible causes of mutations, 38as well as provides a useful marker set for MSI detection. 39 40Introduction 41Recent large-scale whole genome sequencing studies have revealed the complexity of the 42 mutational landscape of the cancer genome (1-4). In cancer genomes, various types of 43 mutations, such as SNVs (single nucleotide variants), short indels (insertions and 44 deletions), genomic rearrangements, copy number alterations, insertion of 45 retrotransposons, and virus genome integrations, have been identified, and their 46 oncogenic roles have been characterized (1-5). Additionally, genome sequencing studies 47 have revealed the molecular basis of somatic mutations (6-9). However, somatic 48 mutations in microsatellites or repeat sequences have not been well-characterized in a 49 large whole genome sequencing cohort due to difficulties in accurately detecting 50 mutations using presently available short-read sequencing technologies. 51A microsatellite is defined as a tract of repetitive DNA motif composed of short 52 repeating units (10). The mutation rate of microsatellites has been known to be higher 53 than other genomic regions due to DNA polymerase slippage during DNA replication and 54 repair (10). Due to their fragility, microsatellites are used as markers of genomic 55 instability in cancer (11). In cancer genetics studies, microsatellite instability (MSI) has 56 been used for molecular diagnosis of Lynch syndrome and cancers with mismatch repair 57 deficiency (11). Furthermore, MSI-positive tumors are generally burdened with higher 58 numbers of somatic mutations and present many mutation-associated neo-antigens, which 59 might be recognized by the immune system. Presently, MSI can also be used as a marker 60 to predict the effect of immune therapy (12). The MSI phenotype is most common in 61 colorectal cancers, stomach cancers and uterine endometrial cancers (10-15%), although 62 it has also been observed across many tumor types at a few % (11). The MSI phenotype 63 is defined by the presence of somatic indels of the 2-5 microsatellite makers, whereby 64 BAT25/26 mononucleotide microsatellites are widely used to establish MSI status (11). 65Irrespective of the clinical importance of microsatellite, large-scale analysis of 66 somatic changes in microsatellites across various type of cancers is limited for whole 67 genome sequencing (WGS) data (13, 14). In the current study, we analyzed indels in 68 microsat...