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Genomic rearrangements are common in cancer, with demonstrated links to disease progression and treatment response. These rearrangements can be complex, resulting in fusions of multiple chromosomal fragments and generation of derivative chromosomes. Although methods exist for detecting individual fusions, they are generally unable to reconstruct complex chained events. To overcome these limitations, we adopted a new optical mapping approach, allowing megabase-length genome maps to be reconstructed and rearranged genomes to be visualized without loss of integrity. Whole-genome mapping (Bionano Genomics) of a well-studied highly rearranged liposarcoma cell line resulted in 3338 assembled consensus genome maps, including 72 fusion maps. These fusion maps represent 112.3 Mb of highly rearranged genomic regions, illuminating the complex architecture of chained fusions, including content, order, orientation, and size. Spanning the junction of 147 chromosomal translocations, we found a total of 28 Mb of interspersed sequences that could not be aligned to the reference genome. Traversing these interspersed sequences using short-read sequencing breakpoint calls, we were able to identify and place 399 sequencing fragments within the optical mapping gaps, thus illustrating the complementary nature of optical mapping and short-read sequencing. We demonstrate that optical mapping provides a powerful new approach for capturing a higher level of complex genomic architecture, creating a scaffold for renewed interpretation of sequencing data of particular relevance to human cancer.

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

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The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.

A computational application to predict, probe and interpret the activities of a series of congeneric compounds inhibiting extracellular signal-regulated kinase 2 protein kinase is presented. The study shows that molecular dynamics coupled with molecular mechanics Poisson-Boltzmann solvent accessible surface area free energy estimation is a suitable tool for investigating the experimental binding activities of ligands to protein kinases. Computed and experimental binding activities were found to be significantly correlated. Moreover, the interpretation of the X-ray co-crystal structure in conjunction with computational results shows that the hinge region of the protein insure the principal binding site via multiple hydrogen bonding interactions, whereas fine-modulation of biological activities along the series is accomplished through the combination of weak and strong interactions that compete with water. These are located in the substituent moieties of the ligands interfacing with the DFG motif, the sugar region and the hydrophobic pocket of extracellular signal-regulated kinase 2. The study suggests that a wider interaction framework that is well beyond the hinge region is required to predict and rationalize at molecular level the experimental biological activities of congeneric compound series.

Schizophrenia is a multifactorial complex disease with a large impact on society. Many hypotheses have been proposed over the years to explain its causes, and genomics and functional genomic approaches may shed light on the reason behind these controversies and discrepancies. We give an overview of several approaches that have been used to identify the genetic causes and molecular phenotypes of the disease. We focus on a recent microarray analysis by Torkamani and colleagues on the evolution of regulatory networks in normal and schizophrenic brains. Combining the conclusion of that study with the prevalent hypotheses of schizophrenia, we suggest that the schizophrenic brain might resemble a juvenile brain.