UMRAD: Universal Multiomics Reference and Alignment Database

This script compiles the data from databases 1-3 below, plus functional information from UniProt and other public databases, to annotate the UniRef100 set of deduplicated proteins. Collectively these annotations enable the unification of various meta-omics studies: metabolomes, metaproteomes, metatranscriptomes, and metagenomes. This is #4 of a series of pipelines that comprise this Universal Reference for various 'omics data analysis.
1. Universal Taxonomy Database: found here
2. Universal Compounds Database: found here
3. Universal Reactions Database: found here
4. Universal Protein Alignment Database: this repository
5. Universal ncRNA Alignment Database: found here

Goal

Ideally there should be a Universal Reference Database that:

• can be used on any NGS/MS data type
• is comprised of unified compounds, proteins, reactions and pathway reference data
• has as complete as possible functional annotations and cross-linked ids
• has all kingdoms of life eukaryotes, bacteria, archaea and MoNA (mobile nucleic acids: viruses, phage, plamids, IS, constructs)
• and thus relies on a well curated and standardized taxonomy database

UMRAD usage:

1. With this data you can align any NGS reads or proteomics data using Diamond alignment software, and get simultaneous whole community phylogenetic and functional outputs.
2. A centralized repo allows cross-study comparisons, since they would have the same compound, function, pathway, protein, and taxon IDs.
3. By containing all sequenced organisms, it can be used on either environmental and/or host-associated data sets.

• For primary metagenome analysis: https://github.com/TealFurnholm/Strain-Level_Metagenome_Analysis
• For primary metatranscriptome analysis: https://github.com/TealFurnholm/Strain-Level_Metatranscriptome_Analysis
• For primary proteomics analysis: TBD
• To integrate NGS and metabolomics: TBD - use compounds and reactions databases to link the two data sets in the meantime
• For secondary pathway/functional analysis: https://github.com/TealFurnholm/Meta-omics_Functional_Analysis

Get Started HERE