Abstract: This disclosure relates generally to a method and a system for profiling of metagenome samples. Most state of-art techniques for metagenomic profiling use homology-based, curated database of identified marker sequences generated after complex and costly pre-processing. The disclosed method and system for profiling of metagenome samples are a non-homology based, a non-marker based and an alignment free strain level profiling tools for microbe profiling. The disclosure works with a several k-mer based indexing techniques for constructing a compact and comprehensive multi-level indexing, wherein the multi-level indexing includes a L1-Index and a L2-Index. The multi-level indexing is used for profiling metagenomics by abundance estimation, wherein the abundance estimation includes a relative abundance and an absolute abundance.

Abstract: This disclosure relates generally to a method and a system for profiling of metagenome samples. Most state of-art techniques for metagenomic profiling use homology-based, curated database of identified marker sequences generated after complex and costly pre-processing. The disclosed method and system for profiling of metagenome samples are a non-homology based, a non-marker based and an alignment free strain level profiling tools for microbe profiling. The disclosure works with a several k-mer based indexing techniques for constructing a compact and comprehensive multi-level indexing, wherein the multi-level indexing includes a L1-Index and a L2-Index. The multi-level indexing is used for profiling metagenomics by abundance estimation, wherein the abundance estimation includes a relative abundance and an absolute abundance.

Abstract: This disclosure relates generally to method and system for graph convolution based gene prioritization on heterogeneous networks. The method includes obtaining a set of entities for human rare diseases from one or more sources containing rare diseases, genes, phenotypes for rare diseases and biological pathways and constructing an initial heterogeneous network using each of an entity from the set of entities. the initial heterogeneous network applying Graph Convolution-based Association Scoring (GCAS) to the initial heterogeneous network to derive inferred associations and creating a Heterogeneous Association Network for Rare Diseases (HANRD) by adding the inferred associations to the initial heterogeneous network and generating a prioritized set of genes for an input query being received in the HANRD.

Abstract: There is a demand for low-cost efficient robust method for mapping read sequences with genome variation graph in genomic study. This disclosure herein relates to a method and system for mapping read sequences with genome variation graph by constructing a subgraph using a novel combination of graph embedding and graph winnowing techniques. The system processes the obtained plurality of read sequences and a genome variation graph for constructing the subgraph by computing an embedding for the genome variation graph utilizing a graph embedding technique. Further, graph index is generated for the genome variation graph based on the embedding and the genome variation graph utilizing the graph winnowing technique. Then computes gapped alignment score for read sequence (r) with its corresponding subgraph. Thus, enables a reliable method for read sequence with accurate, memory efficient and scalable system for mapping read sequences with genome variation graph.

Abstract: Diagnosis of rare human diseases using DNA sequencing is a fast growing area of research. Conventional methods carries a risk of incorrect phenotype interpretation. However, obtaining a correct genotype and phenotype matching is challenging. A system for matching phenotype descriptions and pathogenic variants provides a one to one mapping of the phenotype and genotypes of a plurality of subjects under test. Initially, a plurality of phenotypes and a plurality of genome sequences are segmented based on metadata. A phenotype driven gene prioritization and a variant prioritization is applied on the segmented data method. A similarity score is calculated between the phenotype driven gene prioritization output and the variant prioritization output. The similarity score is further utilized to obtain a one to one matching of the plurality of phenotypes and the plurality of genotype sequences of the plurality of subjects under test.

Abstract: This disclosure relates generally to method and system for graph convolution based gene prioritization on heterogeneous networks. The method includes obtaining a set of entities for human rare diseases from one or more sources containing rare diseases, genes, phenotypes for rare diseases and biological pathways and constructing an initial heterogeneous network using each of an entity from the set of entities. the initial heterogeneous network applying Graph Convolution-based Association Scoring (GCAS) to the initial heterogeneous network to derive inferred associations and creating a Heterogeneous Association Network for Rare Diseases (HANRD) by adding the inferred associations to the initial heterogeneous network and generating a prioritized set of genes for an input query being received in the HANRD.

Abstract: Systems and methods for automated creation of a dictionary of scientific terms are described herein. Initially, input data is filtered to obtain a primary file having a plurality of term-ID pairs with each term-ID pair having a unique term ID and a scientific term. Further, a remove-term file is generated based on one or more term-ID pairs identified from the primary file such that the scientific terms of each term-ID pair corresponds to one of additional terms, frequent scientific terms, and undesirable terms. At least one term-ID pair from among the one or more term-ID pairs is altered to obtain a modified term-ID pair based on modification rules. The modified term-ID pair is added to an add-term file and a modified file is obtained based on the remove-term file and the add-term file. Duplicate term-ID pairs present in the modified file are removed to obtain the dictionary of scientific terms.