Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide.

Overview

SARS-CoV-2 processing requests

Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide.

Prerequisites

This automation system is set up to work with ARTIC-amplified paired-end Illumina sequence data, the most common type of SARS-CoV-2 sequencing data today.

Usage

  • Fork the repo and create a new file in the file_requests/ directory.
    • The file should contain a header line, followed by a list of web links to the files you want to analyze. See the example file provided.
    • Links need to be formatted as follows: <base_url>/<sample ID>_[12].<file_extension> (1 representing the forward strand and 2 the reverse strand of paired-end data). If your data is not accessible in this way, or unpublished, it's not a problem - just create an issue and describe what you need.
  • Create a PR with your changes. We will review and merge it as soon as possible.

Analysis of your data

  • After merging, the data will be uploaded to Galaxy Europe and processed by our collection of SARS-CoV-2 genomic sequence analysis workflows, which will produce highly-sensitive per-sample variant calls, per-batch variant reports and reliable consensus sequences for all your samples.

  • Depending on the amount of other jobs running on our server and on the size of your data batch, processing may take between a few hours and a day.

  • Once ready, the complete analysis will become available as a set of published histories on the server.

    💡 Hint: Your histories will carry the filename from your pull request in their name.

  • Key result files - BAM, VCF and consensus sequence FASTA files for each sample in your batch - will also be pushed automatically to a publicly readable FTP server hosted by BSC.

  • After a few days your results will also be included in the viral Beacon project dashboard.

Links


The analyses will be performed using the Galaxy platform and open source tools from BioConda and BioContainers. The workflows will run on the de.NBI-cloud and form part of the Galaxy COVID-19 efforts with partners around the world. For more information please visit https://github.com/galaxyproject/SARS-CoV-2.

Galaxy Project   European Galaxy Project   Australian Galaxy Project   bioconda   XSEDE   TACC   de.NBI   ELIXIR   PSC   Indiana University   Galaxy Training Network   Bio Platforms Australia   Australian Research Data Commons   VIB   ELIXIR Belgium   Vlaams Supercomputer Center   EOSC-Life   Datamonkey   IFB   CRG   BSC  

Owner
useGalaxy.eu
useGalaxy.eu
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Gengshan Yang 157 Nov 21, 2022
Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model. Designed sample dashboard with insights and recommendation for

Yash 2 Apr 07, 2022
Learnable Boundary Guided Adversarial Training (ICCV2021)

Learnable Boundary Guided Adversarial Training This repository contains the implementation code for the ICCV2021 paper: Learnable Boundary Guided Adve

DV Lab 27 Sep 25, 2022
Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library.

SymEngine Python Wrappers Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library. Installation Pip See License section

136 Dec 28, 2022
Simple object detection app with streamlit

object-detection-app Simple object detection app with streamlit. Upload an image and perform object detection. Adjust the confidence threshold to see

Robin Cole 68 Jan 02, 2023
"Segmenter: Transformer for Semantic Segmentation" reproduced via mmsegmentation

Segmenter-based-on-OpenMMLab "Segmenter: Transformer for Semantic Segmentation, arxiv 2105.05633." reproduced via mmsegmentation. We reproduce Segment

EricKani 22 Feb 24, 2022
Unofficial implementation of MUSIQ (Multi-Scale Image Quality Transformer)

MUSIQ: Multi-Scale Image Quality Transformer Unofficial pytorch implementation of the paper "MUSIQ: Multi-Scale Image Quality Transformer" (paper link

41 Jan 02, 2023
PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

PyZebrascope - an open-source Python platform for brain-wide neural activity imaging in behaving zebrafish

1 May 31, 2022
PyTorch implementation of Weak-shot Fine-grained Classification via Similarity Transfer

SimTrans-Weak-Shot-Classification This repository contains the official PyTorch implementation of the following paper: Weak-shot Fine-grained Classifi

BCMI 60 Dec 02, 2022
Pytorch Lightning Implementation of SC-Depth Methods.

SC_Depth_pl: This is a pytorch lightning implementation of SC-Depth (V1, V2) for self-supervised learning of monocular depth from video. In the V1 (IJ

JiaWang Bian 216 Dec 30, 2022
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021)

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021) Citation Please cite as: @inproceedings{liu2020understan

Sunbow Liu 22 Nov 25, 2022
some classic model used to segment the medical images like CT、X-ray and so on

github_project This is a project for medical image segmentation. This project includes common medical image segmentation models such as U-net, FCN, De

2 Mar 30, 2022
Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN-CLIP-GENERATOR Overview This is a package (with available notebook) for running VQGAN+CLIP locally, with a focus on ease of use, good documentat

Ryan Hamilton 98 Dec 30, 2022
Dynamic Attentive Graph Learning for Image Restoration, ICCV2021 [PyTorch Code]

Dynamic Attentive Graph Learning for Image Restoration This repository is for GATIR introduced in the following paper: Chong Mou, Jian Zhang, Zhuoyuan

Jian Zhang 84 Dec 09, 2022
Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

Patrick Kidger 909 Dec 30, 2022
Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

custom-cnn-fashion-mnist Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow. The following

Danielle Almeida 1 Mar 05, 2022
PyTorch implementation for View-Guided Point Cloud Completion

PyTorch implementation for View-Guided Point Cloud Completion

22 Jan 04, 2023
A system used to detect whether a person is wearing a medical mask or not.

Mask_Detection_System A system used to detect whether a person is wearing a medical mask or not. To open the program, please follow these steps: Make

Mohamed Emad 0 Nov 17, 2022
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

LMPBT Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"

1 Sep 29, 2022
A curated list of resources for Image and Video Deblurring

A curated list of resources for Image and Video Deblurring

Subeesh Vasu 1.7k Jan 01, 2023