CytoConverter: a web-based tool to convert karyotypes to genomic coordinates

The Digital World of Cytogenetic and Cytogenomic Web Resources

The dynamic growth of technological capabilities at the cellular and molecular level has led to a rapid increase in the amount of data on the genes and genomes of organisms. In order to store, access, compare, validate, classify, and understand the massive data generated by different researchers, and to promote effective communication among research communities, various genome and cytogenetic online databases have been established. These data platforms/resources are essential not only for computational analyses and theoretical syntheses but also for helping researchers select future research topics and prioritize molecular targets. Furthermore, they are valuable for identifying shared recurrent genomic patterns related to human diseases and for avoiding unnecessary duplications among different researchers. The website interface, menu, graphics, animations, text layout, and data from databases are displayed by a front end on the screen of a monitor or smartphone. A database front-end refers to the user interface or application that enables accessing tabular, structured, or raw data stored in the database. The Internet makes it possible to reach a greater number of users around the world and gives them quick access to information stored in databases. The number of ways of presenting this data by front-ends increases as well. This requires unifying the ways of operating and presenting information by front-ends and ensuring contextual switching between front-ends of different databases. This chapter aims to present selected cytogenetic and cytogenomic Internet resources in terms of obtaining the needed information and to indicate how to increase the efficiency of access to stored information. Through a brief introduction of these databases and by providing examples of their usage in cytogenetic analyses, we aim to bridge the gap between cytogenetics and molecular genomics by encouraging their utilization.

A cloud-based resource for genome coordinate-based exploration and large-scale analysis of chromosome aberrations and gene fusions in cancer

Cytogenetic analysis provides important information on the genetic mechanisms of cancer. The Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (Mitelman DB) is the largest catalog of acquired chromosome aberrations, presently comprising >70 000 cases across multiple cancer types. Although this resource has enabled the identification of chromosome abnormalities leading to specific cancers and cancer mechanisms, a large-scale, systematic analysis of these aberrations and their downstream implications has been difficult due to the lack of a standard, automated mapping from aberrations to genomic coordinates. We previously introduced CytoConverter as a tool that automates such conversions. CytoConverter has now been updated with improved interpretation of karyotypes and has been integrated with the Mitelman DB, providing a comprehensive mapping of the 70 000+ cases to genomic coordinates, as well as visualization of the frequencies of chromosomal gains and losses. Importantly, all CytoConverter-generated genomic coordinates are publicly available in Google BigQuery, a cloud-based data warehouse, facilitating data exploration and integration with other datasets hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC) Resource. We demonstrate the use of BigQuery for integrative analysis of Mitelman DB with other cancer datasets, including a comparison of the frequency of imbalances identified in Mitelman DB cases with those found in The Cancer Genome Atlas (TCGA) copy number datasets. This solution provides opportunities to leverage the power of cloud computing for low-cost, scalable, and integrated analysis of chromosome aberrations and gene fusions in cancer.

Lamin B1 deletion in myeloid neoplasms causes nuclear anomaly and altered hematopoietic stem cell function

Abnormal nuclear morphology is a hallmark of malignant cells widely used in cancer diagnosis. Pelger-Huët anomaly (PHA) is a common abnormality of neutrophil nuclear morphology of unknown molecular etiology in myeloid neoplasms (MNs). We show that loss of nuclear lamin B1 (LMNB1) encoded on chromosome 5q, which is frequently deleted in MNs, induces defects in nuclear morphology and human hematopoietic stem cell (HSC) function associated with malignancy. LMNB1 deficiency alters genome organization inducing in vitro and in vivo expansion of HSCs, myeloid-biased differentiation with impaired lymphoid commitment, and genome instability due to defective DNA damage repair. Nuclear dysmorphology of neutrophils in patients with MNs is associated with 5q deletions spanning the LMNB1 locus, and lamin B1 loss is both necessary and sufficient to cause PHA in normal and 5q-deleted neutrophils. LMNB1 loss thus causes acquired PHA and links abnormal nuclear morphology with HSCs and progenitor cell fate determination via genome organization.

RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

SUMMARY

Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology.

AVAILABILITY AND IMPLEMENTATION

Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS.

SUPPLEMENTARY INFORMATION

There is no supplementary data.

Myeloid neoplasms with germline predisposition: Practical considerations and complications in the search for new susceptibility loci

Genomic research in hematological malignancies has focused far more prominently on somatic mutations than on germline variants. Although increasing numbers of germline variants are being identified, a substantial proportion of familial myeloid malignancies have no causal allele pinpointed. Here we review the biological, technological, and clinical challenges that stand in the way of the goal of establishing, implementing, and interpreting a comprehensive panel of germline variants for testing. Achieving this goal would inform care for large numbers of myeloid malignancy patients. Furthermore, knowledge of germline susceptibility variants and their corresponding genes will shed light on disease processes, potentially suggesting therapeutic strategies tailored to specific variants.