The novel coronavirus (SARS-COV-2) is generally referred to as Covid-19 virus has spread to 213 countries with nearly 7 million confirmed cases and nearly 400,000 deaths. Such major outbreaks demand classification and origin of the virus genomic sequence, for planning, containment, and treatment. Motivated by the above need, we report two alignment-free methods combing with CGR to perform clustering analysis and create a phylogenetic tree based on it. To each DNA sequence we associate a matrix then define distance between two DNA sequences to be the distance between their associated matrix. These methods are being used for phylogenetic analysis of coronavirus sequences. Our approach provides a powerful tool for analyzing and annotating genomes and their phylogenetic relationships. We also compare our tool to ClustalX algorithm which is one of the most popular alignment methods. Our alignment-free methods are shown to be capable of finding closest genetic relatives of coronaviruses.
PeCan Knowledgebase on St. Jude Cloud (pecan.stjude.cloud), initially developed as a resource of curated genomic variants of pediatric cancer (PeCan), is now significantly expanded to comprise a hub of interconnected data facets for over 9,000 hematologic (heme), CNS, and non-CNS solid (solid) tumor patient samples from around the world. The new data facets, which include gene expression, mutational signatures, and histology, can be explored alongside our existing variants data facet inspiring new hypothesis generation. Variants shows a genomic landscape view (i.e. oncoprint view) and gene- or genome-level view (i.e. ProteinPaint view). Expression data, generated from normalized RNA-seq of ~2,500 samples, can be explored via interactive 2-dimensional maps, revealing distinct subtypes relevant for patient stratification for precision therapy. Mutational signatures, identified from ~2,000 WGS samples, are presented as a heatmap across subtypes, in addition to a summary view for a user-defined cohort or individual sample for which we also display a mutation profile frequency plot together and identified signatures. Histology enables review of histological slide images and associated clinical notes for ~3,000 solid tumors via a searchable interface. As all samples on PeCan have been mapped to a WHO pediatric cancer classification based ontology, the user can customize the view of each data facet presented in PeCan by selecting a specific cancer subtype. Integrative analysis between the data facets has enabled new insights into pediatric cancer biology as demonstrated in the following two examples. First is the discovery of two potential subtypes of adamantinomatous craniopharyngioma. These were initially identified via expression analysis which revealed two distinct groups that were confirmed by examination of associated histology slide data which revealed delineation by brain invasion. Second is an analysis that links germline and somatic alterations in genes involved in DNA Damage Response with mutational signatures associated with homologous recombination deficiency (HRD). This provided new insights on the applicability of therapies targeting HRD in the pediatric cancer population. These examples demonstrate the potential value of PeCan in advancing clinical diagnosis classifications of pediatric cancer and exploration of new therapeutic opportunities. PeCan is an evolving knowledgebase, as we are continuously expanding the platform and adding data over time to foster scientific discovery for the global research community, with the goal of improving treatments for pediatric cancer. Citation Format: Alexander M. Gout, Stephanie Sandor, Delaram Rahbarinia, Jobin Sunny, James Madson, Lucian Vacaroiu, Wentao Yang, Ben Lansdell, Michael Macias, Samuel W. Brady, David Finkelstein, Victor Pastor, Kevin Benton, Andrew Frantz, Mark R. Wilkinson, Cynthia Cline, Brent A. Orr, Abbas Shirinifard, Elizabeth Stewart, Michael Rusch, Xin Zhou, Michael Dyer, David A. Wheeler, Clay McLeod, Jinghui Zhang. Ontology guided navigation of somatic variants, mutational signatures, gene expression and histology images for pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3163.
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