HoloFood is a 'hologenomic' approach that will improve the efficiency of food production systems by understanding the biomolecular and physiological processes affected by incorporating feed additives and novel sustainable feeds in farmed animals.
The HoloFood consortium will showcase the potential of an innovative solution that holds enormous potential for optimising modern food production. Specifically, HoloFood is a framework that integrates a suite of recent analytical and technological developments, that is applicable to any major animal food production system, spanning the full production line.
Thus it is as relevant for the farmers producing livestock, as it is to the associate industries such as those producing the feed and feed additives upon which the animal’s growth, quality, health and wellbeing depends.
This space contains the computational workflows used in the processing of HoloFood's hologenomic datasets.
Web page: https://www.holofood.euFunding codes:
This project has received funding from the European Unionʼs Horizon 2020 research and innovation programme under grant agreement No 817729
MGnify is EMBL-EBI's metagenomics resource. EMBL-EBI are one of the 11 HoloFood partners, and are responsible for the analysis of metagenomic and microbial datasets from the project.
Authors: Daria Shafranskaya, Varsha Kale, Rob Finn, Alla L. Lapidus, Anton Korobeynikov, Andrey D. Prjibelski
Date Published: 28th Oct 2022
Publication Type: Journal
Citation: Front. Microbiol. 13,981458
Pipeline to recover eukaryotic MAGs using CONCOCT, metaBAT2 and EukCC's merging algorythm.
Needs paired end shotgun metagenomic reads.
Eukrecover requires an environment with snakemake and metaWRAP.
Define your samples in the file
This file needs to have the columns project and run to identify each metagenome.
This pipeline does not support co-binning, but feel free to change it.
Clone this repro wherever you want to run the pipeline:
Submitter: Varsha Kale
MGnify genomes analysis pipeline
MGnify A pipeline to perform taxonomic and functional annotation and to generate a catalogue from a set of isolate and/or metagenome-assembled genomes (MAGs) using the workflow described in the following publication:
Gurbich TA, Almeida A, Beracochea M, Burdett T, Burgin J, Cochrane G, Raj S, Richardson L, Rogers AB, Sakharova E, Salazar GA and Finn RD. (2023) [MGnify Genomes: A Resource for Biome-specific Microbial Genome ...
Submitter: Martin Beracochea
Assembly and quantification metatranscriptome using metagenome data.
Version: see VERSION
MetaGT is a bioinformatics analysis pipeline used for improving and quantification metatranscriptome assembly using metagenome data. The pipeline supports Illumina sequencing data and complete metagenome and metatranscriptome assemblies. The pipeline involves the alignment of metatranscriprome assembly to the metagenome assembly with further extracting CDSs, which are covered by ...
Module for integrative Mobilome prediction
Bacteria can acquire genetic material through horizontal gene transfer, allowing them to rapidly adapt to changing environmental conditions. These mobile genetic elements can be classified into three main categories: plasmids, phages, and integrons. Autonomous elements are those capable of excising themselves from the chromosome, reintegrating elsewhere, and potentially modifying the host's physiology. Small integrative elements like insertion ...
MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline ...
Type: Common Workflow Language
Creator: Alex L Mitchell, Alexandre Almeida, Martin Beracochea, Miguel Boland, Josephine Burgin, Guy Cochrane, Michael R Crusoe, Varsha Kale, Simon C Potter, Lorna J Richardson, Ekaterina Sakharova, Maxim Scheremetjew, Anton Korobeynikov, Alex Shlemov, Olga Kunyavskaya, Alla Lapidus, Robert D Finn
Submitter: Martin Beracochea
VIRify is a recently developed pipeline for the detection, annotation, and taxonomic classification of viral contigs in metagenomic and metatranscriptomic assemblies. The pipeline is part of the repertoire of analysis services offered by MGnify. VIRify’s taxonomic classification relies on the detection of taxon-specific profile hidden Markov models (HMMs), built upon a set of 22,014 orthologous protein domains and referred to as ViPhOGs. VIRify was implemented in CWL. What do I need? The ...