OMAMO: orthology-based model organism selection

Related tags

Deep Learningomamo
Overview

OMAMO: orthology-based model organism selection

workflow diagram

OMAMO is a tool that suggests the best model organism to study a biological process based on orthologous relationship between a species and human.

The user can consider several species as potential model organisms and the algorithm will rank them and report the output for a given biological process (searched as a GO term or a GO ID) is produced in the dataframe format.

Pipeline

Firstly, download the OMA dataset:

wget  https://omabrowser.org/All/OmaServer.h5  -O data/OmaServer.h5  #caution: 94GB

Secondly, using the file data/oma-species.txt find the five-letter UniProt code for species of interest. For example, consider three species Dicdyostelium discodeium , Neurospora crassa and Schizosaccharomyces pombe. Their UniProt codes are DICDI, NEUCR and SCHPO, respectively.

Then, run the code omamo_base.py for each species code (DICDI, NEUCR and SCHPO):

species="DICDI"
mkdir output; cd output

python3 ../omamo_base.py ../data/OmaServer.h5 ../data/go_positive_annotations.tsv ${species}

Once the code finished running, the outputs include ${species}2.csv files which should be combined to create a final dataframe using the code omamo_dataframe.py:

python3 omamo_dataframe.py output

where output is the name of the directory where the user wishes to save the output.

Finally, the output data frame is ready as a CSV file omamo_output_df.csv.

OMAMO Website

You can also visit the OMAMO website, where you can browse biological processes to study in 50 unicellular species.

Change log

Version 0.0.1

  • Initial release

Citation

Alina Nicheperovich, Adrian M Altenhoff, Christophe Dessimoz, Sina Majidian, "OMAMO: orthology-based model organism selection", submitted to Bioinformatics journal.

License

OMAMO is a free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

OMAMO is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with OMAMO. If not, see http://www.gnu.org/licenses/.

Owner
Dessimoz Lab
Dessimoz Lab
Fake videos detection by tracing the source using video hashing retrieval.

Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos 🎉️ 📜 Directory Introduction VTL Trace Samples and Acc of Hash

56 Dec 22, 2022
Toward Spatially Unbiased Generative Models (ICCV 2021)

Toward Spatially Unbiased Generative Models Implementation of Toward Spatially Unbiased Generative Models (ICCV 2021) Overview Recent image generation

Jooyoung Choi 88 Dec 01, 2022
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-Supervised Learning for Domain Adaptation on Point-Clouds Introduction Self-supervised learning (SSL) allows to learn useful representations from

Idan Achituve 66 Dec 20, 2022
NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

NOD (Night Object Detection) Dataset NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset, BM

Igor Morawski 17 Nov 05, 2022
An unofficial implementation of "Unpaired Image Super-Resolution using Pseudo-Supervision." CVPR2020

UnpairedSR An unofficial implementation of "Unpaired Image Super-Resolution using Pseudo-Supervision." CVPR2020 turn RCAN(modified) -- xmodel(xilinx

JiaKui Hu 10 Oct 28, 2022
OBBDetection is a oriented object detection library, which is based on MMdetection.

OBBDetection news: We are now updating OBBDetection to new vision based on MMdetection v2.10, which has more advanced models and more efficient featur

jbwang1997 401 Jan 02, 2023
TimeSHAP explains Recurrent Neural Network predictions.

TimeSHAP TimeSHAP is a model-agnostic, recurrent explainer that builds upon KernelSHAP and extends it to the sequential domain. TimeSHAP computes even

Feedzai 90 Dec 18, 2022
This repository contains the code for the paper in EMNLP 2021: "HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression".

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression This repository contains the code for the paper in EM

Chenhe Dong 2 Mar 24, 2022
CONditionals for Ordinal Regression and classification in PyTorch

CONDOR pytorch implementation for ordinal regression with deep neural networks. Documentation: https://GarrettJenkinson.github.io/condor_pytorch About

7 Jul 25, 2022
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an

0 Nov 10, 2021
level1-image-classification-level1-recsys-09 created by GitHub Classroom

level1-image-classification-level1-recsys-09 ❗ 주제 설명 COVID-19 Pandemic 상황 속 마스크 착용 유무 판단 시스템 구축 마스크 착용 여부, 성별, 나이 총 세가지 기준에 따라 총 18개의 class로 구분하는 모델 ?

6 Mar 17, 2022
zeus is a Python implementation of the Ensemble Slice Sampling method.

zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl

Minas Karamanis 197 Dec 04, 2022
Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal"

Patch-wise Adversarial Removal Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal

4 Oct 12, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
A Python module for the generation and training of an entry-level feedforward neural network.

ff-neural-network A Python module for the generation and training of an entry-level feedforward neural network. This repository serves as a repurposin

Riadh 2 Jan 31, 2022
Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Real2CAD-3DV Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ) Group Member: Yue Pan, Yuanwen Yue, Bingxin Ke, Yujie He

24 Jun 22, 2022
Rlmm blender toolkit - A set of tools to streamline level generation in UDK straight from Blender

rlmm_blender_toolkit A set of tools to streamline level generation in UDK straig

Rocket League Mapmaking 0 Jan 15, 2022
Powerful unsupervised domain adaptation method for dense retrieval.

Powerful unsupervised domain adaptation method for dense retrieval

Ubiquitous Knowledge Processing Lab 191 Dec 28, 2022
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting

Tencent YouTu Research 9 Oct 11, 2022
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

Hong-Jia Chen 91 Dec 02, 2022