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 If your data is not accessible in this way, or unpublished, it's not a problem - just create an issue and describe what you need.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
Simple Dynamic Batching Inference

Simple Dynamic Batching Inference 解决了什么问题? 众所周知,Batch对于GPU上深度学习模型的运行效率影响很大。。。 是在Inference时。搜索、推荐等场景自带比较大的batch,问题不大。但更多场景面临的往往是稀碎的请求(比如图片服务里一次一张图)。 如果

116 Jan 01, 2023
StyleGAN2 Webtoon / Anime Style Toonify

StyleGAN2 Webtoon / Anime Style Toonify Korea Webtoon or Japanese Anime Character Stylegan2 base high Quality 1024x1024 / 512x512 Generate and Transfe

121 Dec 21, 2022
Solver for Large-Scale Rank-One Semidefinite Relaxations

STRIDE: spectrahedral proximal gradient descent along vertices A Solver for Large-Scale Rank-One Semidefinite Relaxations About STRIDE is designed for

48 Dec 20, 2022
Code from the paper "High-Performance Brain-to-Text Communication via Handwriting"

High-Performance Brain-to-Text Communication via Handwriting Overview This repo is associated with this manuscript, preprint and dataset. The code can

Francis R. Willett 306 Jan 03, 2023
Code for HodgeNet: Learning Spectral Geometry on Triangle Meshes, in SIGGRAPH 2021.

HodgeNet | Webpage | Paper | Video HodgeNet: Learning Spectral Geometry on Triangle Meshes Dmitriy Smirnov, Justin Solomon SIGGRAPH 2021 Set-up To ins

Dima Smirnov 61 Nov 27, 2022
Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

5 Nov 21, 2022
Neural Dynamic Policies for End-to-End Sensorimotor Learning

This is a PyTorch based implementation for our NeurIPS 2020 paper on Neural Dynamic Policies for end-to-end sensorimotor learning.

Shikhar Bahl 47 Dec 11, 2022
Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

THU模式识别2021春 -- Jittor 医学图像分割 模型列表 本仓库收录了课程作业中同学们采用jittor框架实现的如下模型: UNet SegNet DeepLab V2 DANet EANet HarDNet及其改动HarDNet_alter PSPNet OCNet OCRNet DL

48 Dec 26, 2022
Python version of the amazing Reaction Mechanism Generator (RMG).

Reaction Mechanism Generator (RMG) Description This repository contains the Python version of Reaction Mechanism Generator (RMG), a tool for automatic

Reaction Mechanism Generator 284 Dec 27, 2022
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构

BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址:https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M

MEGVII Research 28 Dec 23, 2022
CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

CDGAN CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation CDGAN Implementation in PyTorch This is the imple

Kancharagunta Kishan Babu 6 Apr 19, 2022
Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN).

arXiv, porject page, paper Blind Image Decomposition (BID) Blind Image Decomposition is a novel task. The task requires separating a superimposed imag

64 Dec 20, 2022
CondenseNet: Light weighted CNN for mobile devices

CondenseNets This repository contains the code (in PyTorch) for "CondenseNet: An Efficient DenseNet using Learned Group Convolutions" paper by Gao Hua

Shichen Liu 690 Nov 30, 2022
MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project developed by MMLab.

OpenMMLab 3.2k Jan 05, 2023
Reinforcement-learning - Repository of the class assignment questions for the course on reinforcement learning

DSE 314/614: Reinforcement Learning This repository containing reinforcement lea

Manav Mishra 4 Apr 15, 2022
Human Pose estimation with TensorFlow framework

Human Pose Estimation with TensorFlow Here you can find the implementation of the Human Body Pose Estimation algorithm, presented in the DeeperCut and

Eldar Insafutdinov 1.1k Dec 29, 2022
A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

img_sussifier A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196 Examples How to use install python pip i

41 Sep 30, 2022
Optimizers-visualized - Visualization of different optimizers on local minimas and saddle points.

Optimizers Visualized Visualization of how different optimizers handle mathematical functions for optimization. Contents Installation Usage Functions

Gautam J 1 Jan 01, 2022
Source code for our paper "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash"

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash Abstract: Apple recently revealed its deep perceptual hashing system NeuralHash to

<a href=[email protected]"> 11 Dec 03, 2022
Manipulation OpenAI Gym environments to simulate robots at the STARS lab

Manipulator Learning This repository contains a set of manipulation environments that are compatible with OpenAI Gym and simulated in pybullet. In par

STARS Laboratory 5 Dec 08, 2022