Identifying and Analyzing Indel Variants in the Human Genome Using Computational Approaches

Insertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. Despite this importance and being the second most abundant variant type in the human genome, indels have not been studied as much as the single nucleotide polymorphism (SNP). With the advance of next-generation sequencing technology, many indel calling tools have been developed. However, performance comparison of commonly used tools has shown that (1) the tools have limited power in identifying indels and there are significant number of indels undetected, and (2) there is significant disagreement among the indel sets produced by the tools. These findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results.

Two indels are biologically equivalent if the resulting sequences are the same. Storing biologically equivalent indels as distinct entries in databases causes data redundancy and misleads downstream analysis. It is thus desirable to have a unified system for identifying and representing equivalent indels. This dissertation describes UPS-indel, a utility tool that creates a universal positioning system for indels so that equivalent indels can be uniquely determined by their coordinates in the new system. Results show that UPS-indel identifies more redundant indels than existing algorithms.

While mapping short reads to the reference genome, a significant number of short reads are unmapped and excluded from downstream analyses, thereby causing information loss in the subsequent variant calling. This dissertation describes Genesis-indel, a computational pipeline that explores the unmapped reads to identify missing novel indels. Results analyzing sequence alignment of 30 breast cancer patients show that Genesis-indel identifies many novel indels that also show significant enrichment in oncogenes and tumor suppressor genes, demonstrating the importance of rescuing indels hidden in the unmapped reads in cancer and disease studies.

Somatic mutations play a vital role in transforming healthy cells into cancer cells. Therefore, accurate identification of somatic mutations is essential. Many somatic mutations callers are available with different strengths and weaknesses. An ensemble approach integrating the power of the callers is warranted. This dissertation describes SomaticHunter, an ensemble of two callers, namely Platypus and VarDict. Results on synthetic tumor data show that for both SNPs and indels, SomaticHunter achieves recall comparable to the state-of-the-art somatic mutation callers and the highest precision, resulting in the highest F1 score.