Workflows
What is a Workflow?Filters
This workflow performs the scaffolding of a genome assembly using HiC data with YAHS. Can be used on any assembly with Hi-C data, and the assembly in the gfa format. You can generate a gfa from a fasta using the gfastat tool. Part of the VGP set of workflows, it is meant to be run after the contigging (workflows 3,4, or 5), optional purging step (Workflow 6 or 6b), and an optionnal scaffolding with Bionano data (Workflow 7). This workflow includes QC with Assembly statistics, Busco, and Hi-C maps. ...
Introduction
ebi-metagenomics/biosiftr is a bioinformatics pipeline that generates taxonomic and functional profiles for low-yield (shallow shotgun: < 10 M reads) short raw-reads using MGnify biome-specific genome catalogues
as a reference.
The biome selection includes all the biomes available in the MGnify genome catalogues
.
The main sections of the pipeline include the following ...
Mobilome Annotation Pipeline (former MoMofy)
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 integrative elements. Plasmids are mostly extrachromosmal; phages can be found extrachromosmal or as temperate phages (prophages); whereas integrons are stable inserted in the chromosome. Autonomous elements are ...
Type: Nextflow
Creators: Alejandra Escobar, Martin Beracochea
Submitters: Martin Beracochea, Alejandra Escobar
Assembly of metagenomic sequencing data
Associated Tutorial
This workflows is part of the tutorial Assembly of metagenomic sequencing data, available in the GTN
Features
- Includes Galaxy Workflow Tests
- Includes a [Galaxy Workflow ...
DeepAnnotation can be used to perform genomic selection (GS), which is a promising breeding strategy for agricultural breeding. DeepAnnotation predicts phenotypes from comprehensive multi-omics functional annotations with interpretable deep learning framework. The effectiveness of DeepAnnotation has been demonstrated in predicting three pork production traits (lean meat percentage at 100 kg [LMP], loin muscle depth at 100 kg [LMD], back fat thickness at 100 kg [BF]) on a population of 1940 Duroc ...
Type: Python
Creators: Wenlong Ma, Weigang Zheng, Shenghua Qin, Chao Wang, Bowen Lei, Yuwen Liu
Submitter: Ma Wenlong
High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, colour ...
Modified workflow to perform Voronoi segmentation, based on https://usegalaxy.eu/published/workflow?id=23030421cd9fcfb2.
Input requirements:
- RA: -- right ascension in degrees as a float number.
- Dec: -- Declination in degrees as a float number
- Radius: -- the radius of the cone to be queried in arcminutes as a float number
- Pixel size: -- the size of the pixel in arcseconds
- Band: -- the band (channel) of the image: g, r, z, i