The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a mutant variant with one or more InDels. Further, we compare how structural effects due to InDels differ from the effects of amino acid substitutions, which are another type of amino acid mutation. We finish by performing a correlation analysis between our rigidity-based metrics and wet lab data for their ability to infer the effects of InDels on protein fitness.

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Large-scale structural chromosomal rearrangements or structural variants, such as insertions, deletions, translocations, and inversions may result in the exchange of coding or regulatory DNA/RNA sequences between genes, which can lead to gene fusion or the loss/gain of genetic material. Gene fusion events are common in multiple types of cancer. High-throughput DNA and RNA sequencing methods produce large amounts of genomic data. Due to the massive amounts of data and the fact that structural variants account for just a small fraction of the data, efficient and accurate search methods are required for the detection of chromosomal breakpoints and structural variations. Robust identification of structural variants remains paramount for accurate inference of long-range interactions from high-throughput chromosome conformation capture (Hi-C) data. This chapter is a survey of computational methods based on paired end reading, efficient search techniques and parallel computing to detect structural variants in both whole genome and transcriptome sequences, as well as Hi-C data.

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