Abstract: This disclosure provides a computational framework with related methods and systems to enhance the analysis of genomic information. More specifically, the disclosure provides for a graph-based reference genome framework, referred to as a GNOmics Graph Model (GGM), which represents genomic sequence information in edges with nodes representing transitions between edges. The disclosed GGM framework can represent all known polymorphisms simultaneously, including, SNPs, indels, and various rearrangements, in a data-efficient manner. The edges can contain weights to reflect the likelihood of a path within the GGM incorporating any particular edge. The disclosure also provides for systems and methods for using the GGM as a reference model for the rapid assembly of short sequence reads and analysis of DNA sequence variation with enhanced computational efficiency.

Abstract: This disclosure provides a computational framework with related methods and systems to enhance the analysis of genomic information. More specifically, the disclosure provides for a graph-based reference genome framework, referred to as a GNOmics Graph Model (GGM), which represents genomic sequence information in edges with nodes representing transitions between edges. The disclosed GGM framework can represent all known polymorphisms simultaneously, including, SNPs, indels, and various rearrangements, in a data-efficient manner. The edges can contain weights to reflect the likelihood of a path within the GGM incorporating any particular edge. The disclosure also provides for systems and methods for using the GGM as a reference model for the rapid assembly of short sequence reads and analysis of DNA sequence variation with enhanced computational efficiency.

Abstract: This disclosure provides a computational framework with related methods and systems to enhance the analysis of genomic information. More specifically, the disclosure provides for a graph-based reference genome framework, referred to as a GNOmics Graph Model (GGM), which represents genomic sequence information in edges with nodes representing transitions between edges. The disclosed GGM framework can represent all known polymorphisms simultaneously, including, SNPs, indels, and various rearrangements, in a data-efficient manner. The edges can contain weights to reflect the likelihood of a path within the GGM incorporating any particular edge. The disclosure also provides for systems and methods for using the GGM as a reference model for the rapid assembly of short sequence reads and analysis of DNA sequence variation with enhanced computational efficiency.

Abstract: This disclosure provides a computational framework with related methods and systems to enhance the analysis of genomic information. More specifically, the disclosure provides for a graph-based reference genome framework, referred to as a GNOmics Graph Model (GGM), which represents genomic sequence information in edges with nodes representing transitions between edges. The disclosed GGM framework can represent all known polymorphisms simultaneously, including, SNPs, indels, and various rearrangements, in a data-efficient manner. The edges can contain weights to reflect the likelihood of a path within the GGM incorporating any particular edge. The disclosure also provides for systems and methods for using the GGM as a reference model for the rapid assembly of short sequence reads and analysis of DNA sequence variation with enhanced computational efficiency.