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.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan–Meier plots and hazard ratio analysis on the three types of cancer—GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

DNA methylation plays a critical role in emergence and advancement of many cancer types. However, studies that use DNA methylation data primarily focus on DNA methylation of CpG sites. The role of gene body (GB) methylation in cancer remains rather unclear. Particularly, integrating methylation data with gene expression, in order to improve the classification of disease subtypes, remains under-explored. In this study, we use SCClust (Sparse Canonical Correlation analysis with Clustering), a method that combines high-dimensional omics data using sparse canonical correlation analysis (sCCA), to gene expression and DNA methylation data for two cancer genomics datasets from The Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on colon cancer and kidney cancer data. Our findings demonstrate that integrating both types of DNA methylation data with gene expression results in similar subtypes and outperforms existing methods. The underlying information revealed as a result of data integration is different depending on what type of methylation data is integrated with gene expression. The significance of this study lies in the advancements in understanding the epigenetics of cancer as well as categorization of cancer data that incorporates epigenome with transcriptome.