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LSA / Latent Strain Analysis
Allows metagenomic read partitioning. LSA can cluster sequences in fixed memory by using covariance information. It is able to separate sequences from related genomes into different partitions. The tool can assemble accurately partitions with respect to known references. It can be useful to discover new genome or identify ecological relationships. LSA uses a hyperplane hashing function and streaming singular value decomposition (SVD) in order to find covariance relations between k-mers.
PanPhlAn / Pangenome-based Phylogenomic Analysis
Uses metagenomic data to achieve strain-level microbial profiling resolution. PanPhlAn recognized outbreak strains, produced the largest strain level population genomic study of human-associated bacteria and, in combination with metatranscriptomics, profiled the transcriptional activity of strains in complex communities. PanPhlAn’s ability for strain-tracking and functional analysis of unknown pathogens makes it an efficient tool for culture-free infectious outbreak epidemiology and microbial population studies.
Tracks individual strains across large set of samples. StrainPhlAn is a computational tool for performing metagenomic strain-level population genomics on large metagenomic datasets by profiling microbes from known species with strain level resolution and providing comparative and phylogenetic analyses of strains retrieved from metagenomic samples. It extracts the strain of a specific species by merging and concatenating all reads mapped against that species markers in the MetaPhlAn2 database. It comes with MetaPhlAn2 package.
A Java based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.
Sigma / Strain-level Inference of Genomes from Metagenomic Analysis
Identifies and quantifies genomes. Sigma uses the read mapping approach. It provides the statistical framework for hypothesis testing and confidence interval estimation. The tool permits maximum likelihood estimation (MLE) of the relative abundances of all genomes measured by the percentages of reads sampled from these genomes. It is able to determine the divergence of the actual genomes in the sample from the reference genomes in the database by variant calling.
A reference-based taxonomic profiler that introduces a novel top-down approach to analyze metagenomic NGS samples. Rather than predicting an organism presence in the sample based only on relative abundances, DUDes first identifies possible candidates by comparing the strength of the read mapping in each node of the taxonomic tree in an iterative manner. Instead of using the lowest common ancestor (LCA) we propose a new approach: the deepest uncommon descendent (DUD). We showed in experiments that DUDes works for single and multiple organisms and can identify low abundant taxonomic groups with high precision. Additionally, DUDes provides a strain identification method that can propose one or more strains presents in a sample.
HOMINID / HOst-Microbiome INteraction IDentification
A Python MPI program to identify associations between host genetic variation and microbiome taxonomic composition. HOMINID is a computational approach based on Lasso linear regression, that given host genetic variation and microbiome composition data, identifies host single nucleotide polymorphisms (SNPs) that are correlated with microbial taxa abundances. By using HOMINID on data from the Human Microbiome Project, we identified 2158 human SNPs in which genetic variation is correlated with microbiome taxonomic composition in 15 body sites.
Identifies microbial organisms in metagenomic sequencing data, estimates their abundance, and quantifies their distances to known reference genomes. MicrobeGPS is a bioinformatics software for the analysis of metagenomic sequencing data. It profiles the composition of metagenomic communities as accurately as possible and presents the results to the user in a convenient manner. It also calculates quality metrics for the estimated candidates and allows to identify false candidates.
Tests the main effect or the single nucleotide polymorphism (SNP) environment interaction for each SNP. MicrobiomeGWAS is a software package for identifying host genetic variants associated with microbiome distance matrix or beta-diversity. The computational complexity depends on the number of subjects, the number of SNPs, and the number of microbiome distance matrices. It takes microbiomeGWAS about 12 h to analyze data in a Genome-wide association study (GWAS) with 2,000 subjects, 500,000 SNPs, and 14 microbiome distance matrices using a single core.
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