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 If your data is not accessible in this way, or unpublished, it's not a problem - just create an issue and describe what you need.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
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Katherine Crowson 53 Dec 29, 2022
EfficientNetV2-with-TPU - Cifar-10 case study

EfficientNetV2-with-TPU EfficientNet EfficientNetV2 adalah jenis jaringan saraf convolutional yang memiliki kecepatan pelatihan lebih cepat dan efisie

Sultan syach 1 Dec 28, 2021
Spatial-Location-Constraint-Prototype-Loss-for-Open-Set-Recognition

Spatial Location Constraint Prototype Loss for Open Set Recognition Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for

Xia Ziheng 12 Jun 24, 2022
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Contents Local and Global GAN Cross-View Image Translation Semantic Image Synthesis Acknowledgments Related Projects Citation Contributions Collaborat

Hao Tang 131 Dec 07, 2022
JstDoS - HTTP Protocol Stack Remote Code Execution Vulnerability

jstDoS If you are going to skid that, please give credits ! ^^ ¿How works? This

apolo 4 Feb 11, 2022
Implementation of " SESS: Self-Ensembling Semi-Supervised 3D Object Detection" (CVPR2020 Oral)

SESS: Self-Ensembling Semi-Supervised 3D Object Detection Created by Na Zhao from National University of Singapore Introduction This repository contai

125 Dec 23, 2022
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022
Yolact-keras实例分割模型在keras当中的实现

Yolact-keras实例分割模型在keras当中的实现 目录 性能情况 Performance 所需环境 Environment 文件下载 Download 训练步骤 How2train 预测步骤 How2predict 评估步骤 How2eval 参考资料 Reference 性能情况 训练数

Bubbliiiing 11 Dec 26, 2022
All course materials for the Zero to Mastery Machine Learning and Data Science course.

Zero to Mastery Machine Learning Welcome! This repository contains all of the code, notebooks, images and other materials related to the Zero to Maste

Daniel Bourke 1.6k Jan 08, 2023
All supplementary material used by me while TA-ing CS3244: Machine Learning

CS3244-Tutorial-Material All supplementary material used by me while TA-ing CS3244: Machine Learning at NUS School of Computing. What is this? I teach

Rishabh Anand 18 Sep 23, 2022
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang 1.2k Dec 29, 2022
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning

LABES This is the code for EMNLP 2020 paper "A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised L

17 Sep 28, 2022
Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation.

DuoRec Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. Usage Download datasets fr

Qrh 46 Dec 19, 2022
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.

UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. UltraOpt is a simple and efficient library to minimize expensive

98 Aug 16, 2022
A Python wrapper for Google Tesseract

Python Tesseract Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded i

Matthias A Lee 4.6k Jan 05, 2023
Adabelief-Optimizer - Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"

AdaBelief Optimizer NeurIPS 2020 Spotlight, trains fast as Adam, generalizes well as SGD, and is stable to train GANs. Release of package We have rele

Juntang Zhuang 998 Dec 29, 2022
Model of an AI powered sign language interpreter.

TEXT AND SPEECH TO SIGN LANGUAGE. A web application which takes in text or live audio speech recording as input, converts and displays the relevant Si

Mark Gatere 4 Mar 30, 2022
Source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network

KaGRMN-DSG_ABSA This repository contains the PyTorch source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated

XingBowen 4 May 20, 2022
MoViNets PyTorch implementation: Mobile Video Networks for Efficient Video Recognition;

MoViNet-pytorch Pytorch unofficial implementation of MoViNets: Mobile Video Networks for Efficient Video Recognition. Authors: Dan Kondratyuk, Liangzh

189 Dec 20, 2022