Abstract: Systems and methods for assembly of metagenomic sequences are described herein. In one embodiment, a plurality of metagenomic sequences is represented in three dimensional space to obtain a plurality of sequence vectors. Based on plurality of the sequence vectors, a cuboid having a plurality of grids is defined in the three dimensional space such that it encompasses the plurality of metagenomic sequences. Further, the plurality of metagenomic sequences is assembled into one or more contigs based on traversal of the plurality of grids. In one implementation, the one or more contigs are assembled such that a contig includes metagenomic sequences probably originating from the same genome.

Abstract: Method(s) for identifying rDNA sequences from a sample containing plurality of unknown DNA sequences are described herein. The method includes selecting one or more target clusters, from a plurality of reference clusters, corresponding to the query sequence. The target clusters are selected based on a composition based analysis. A proportion of probable rDNA clusters from the target clusters is identified. Based on the proportion of the probable rDNA clusters, the query sequence is identified as an rDNA.

Abstract: Method(s) and system(s) for identifying horizontally transferred genes are described herein. The method includes defining a cuboid in a three dimensional space, wherein the cuboid includes fragment points corresponding to the genomic fragments belonging to a plurality of sequenced microbial genomes, and dividing the cuboid into a plurality of grids. The method further includes selecting one or more grids corresponding to a selected genome and classifying each of the selected grids as one of majority, minority, and mixed grids, based on number of fragment points corresponding to the selected genome in each of the selected grids. Further, at least one genomic fragment from the minority and the mixed grids is identified as the horizontally transferred gene based on a distance ratio assessment.

Abstract: Method(s) and system(s) for identifying marker features of various subsets of a multi-dimensional data are provided. Each subset includes various data points associated with various features. Each of the data points are defined by feature values corresponding to the associated features. The method includes identifying feature pairs based on a matrix of the data points and the features, and computing correlation distances between features in each of the feature pairs. The method includes generating a non-linear pattern of the plurality of features in a two-dimensional plane. Additionally, the method includes calculating a threshold feature value for the associated features of the data points of a particular subset and representing the threshold feature value as a threshold non-linear pattern in the two-dimensional plane. The method includes determining the marker features based on a relative position of the features with respect to the threshold feature value in the two-dimensional plane.

Abstract: Systems and methods for analyzing community structures within a plurality of environmental samples are described herein. The method includes obtaining taxa data corresponding to taxonomic groups within the plurality of the environmental samples. Based on the taxa data, an abundance value for each of the taxonomic groups with respect to each of the plurality of environmental samples is determined. Further, based on abundance values, an interaction factor for each pair of the taxonomic groups in the plurality of environmental samples is computed. The interaction factor is indicative of a degree of interaction between a pair of taxonomic groups from among the taxonomic groups. Based in part on interaction factors and abundance values, the plurality of the environmental samples is clustered.

Abstract: Method(s) and system(s) for identifying marker features of various subsets of a multi-dimensional data are provided. Each subset includes various data points associated with various features. Each of the data points are defined by feature values corresponding to the associated features. The method includes identifying feature pairs based on a matrix of the data points and the features, and computing correlation distances between features in each of the feature pairs. The method includes generating a non-linear pattern of the plurality of features in a two-dimensional plane. Additionally, the method includes calculating a threshold feature value for the associated features of the data points of a particular subset and representing the threshold feature value as a threshold non-linear pattern in the two-dimensional plane. The method includes determining the marker features based on a relative position of the features with respect to the threshold feature value in the two-dimensional plane.

Abstract: Systems and methods for assembly of metagenomic sequences are described herein. In one embodiment, a plurality of metagenomic sequences is represented in three dimensional space to obtain a plurality of sequence vectors. Based on plurality of the sequence vectors, a cuboid having a plurality of grids is defined in the three dimensional space such that it encompasses the plurality of metagenomic sequences. Further, the plurality of metagenomic sequences is assembled into one or more contigs based on traversal of the plurality of grids. In one implementation, the one or more contigs are assembled such that a contig includes metagenomic sequences probably originating from the same genome.

Abstract: Method(s) and system(s) for identifying horizontally transferred genes are described herein. The method includes defining a cuboid in a three dimensional space, wherein the cuboid includes fragment points corresponding to the genomic fragments belonging to a plurality of sequenced microbial genomes, and dividing the cuboid into a plurality of grids. The method further includes selecting one or more grids corresponding to a selected genome and classifying each of the selected grids as one of majority, minority, and mixed grids, based on number of fragment points corresponding to the selected genome in each of the selected grids. Further, at least one genomic fragment from the minority and the mixed grids is identified as the horizontally transferred gene based on a distance ratio assessment.

Abstract: Systems and methods for analyzing community structures within a plurality of environmental samples are described herein. The method includes obtaining taxa data corresponding to taxonomic groups within the plurality of the environmental samples. Based on the taxa data, an abundance value for each of the taxonomic groups with respect to each of the plurality of environmental samples is determined. Further, based on abundance values, an interaction factor for each pair of the taxonomic groups in the plurality of environmental samples is computed. The interaction factor is indicative of a degree of interaction between a pair of taxonomic groups from among the taxonomic groups. Based in part on interaction factors and abundance values, the plurality of the environmental samples is clustered.

Abstract: Method(s) for identifying rDNA sequences from a sample containing plurality of unknown DNA sequences are described herein. The method includes selecting one or more target clusters, from a plurality of reference clusters, corresponding to the query sequence. The target clusters are selected based on a composition based analysis. A proportion of probable rDNA clusters from the target clusters is identified. Based on the proportion of the probable rDNA clusters, the query sequence is identified as an rDNA.

Abstract: Method(s) for identifying a taxon corresponding to a query sequence are described herein. The method includes selecting a target cluster, from amongst a plurality of reference clusters, corresponding to the query sequence. The target cluster may be selected based on a composition based analysis. A similarity based analysis of the query sequence is performed with respect to the target cluster. From the target cluster, the taxon corresponding to the query sequence is identified based on the similarity based analysis.