Brings together many aspects of today’s cutting-edge genomic, metagenomic, and metatranscriptomic analysis practices to address a wide array of needs. Anvi’o is an advanced analysis and visualization platform that offers automated and human-guided characterization of microbial genomes in metagenomic assemblies, with interactive interfaces that can link ‘omics data from multiple sources into a single, intuitive display. It empowers researchers without extensive bioinformatics skills to perform and communicate in-depth analyses on large ‘omics datasets.
Allows users to record omics data from multiple heterogeneous datasets (Bacteriome, RegulonDB, STRING, BioGRID, MPIDB, GEO, and IntAct). The goal of ARepA is to process these contents and to standardize them. It aims to make the files uniform by saving them as a tab-delimited text format.
Aims to simplify and automate real-time data integration and interoperability. i2x was developed to enable the automated real-time, reactive or event-driven analysis of data through the deployment of intelligent agents. It also improves data delivery to heterogeneous destinations using a template-based approach, allowing transmitting and transforming data. This platform is suitable to several other research problems within and beyond the life sciences.
Is developed to perform large-scale, reproducible and automated integrative reference free analysis of metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker.
Realizes quantification by combining metagenome and metatranscriptome data. AQMM is made for performing absolute quantification (AQ) of parallel metagenome and metatranscriptome dataset. The AQMM purpose is to obtain the AQ of genes/transcripts/taxa in samples and to detect differential expression genes (DEGs) in metatranscriptome data.