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
Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

🔍 Watermarking Images in Self-Supervised Latent-Spaces PyTorch implementation and pretrained models for the paper. For details, see Watermarking Imag

Meta Research 32 Dec 13, 2022
Duke Machine Learning Winter School: Computer Vision 2022

mlwscv2002 Welcome to the Duke Machine Learning Winter School: Computer Vision 2022! The MLWS-CV includes 3 hands-on training sessions on implementing

Duke + Data Science (+DS) 9 May 25, 2022
Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Kento Nishi 22 Jul 07, 2022
GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices

GyroSPD Code for the paper "Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices" accepted at NeurIPS 2021. Re

Federico Lopez 12 Dec 12, 2022
GAN-based Matrix Factorization for Recommender Systems

GAN-based Matrix Factorization for Recommender Systems This repository contains the datasets' splits, the source code of the experiments and their res

Ervin Dervishaj 9 Nov 06, 2022
Code for Boundary-Aware Segmentation Network for Mobile and Web Applications

BASNet Boundary-Aware Segmentation Network for Mobile and Web Applications This repository contain implementation of BASNet in tensorflow/keras. comme

Hamid Ali 8 Nov 24, 2022
The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines.

The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines. It includes tools for downloading pipelines and their dependencies and tools for measuring their performace

8 Dec 04, 2022
Apollo optimizer in tensorflow

Apollo Optimizer in Tensorflow 2.x Notes: Warmup is important with Apollo optimizer, so be sure to pass in a learning rate schedule vs. a constant lea

Evan Walters 1 Nov 09, 2021
Pixel-level Crack Detection From Images Of Levee Systems : A Comparative Study

PIXEL-LEVEL CRACK DETECTION FROM IMAGES OF LEVEE SYSTEMS : A COMPARATIVE STUDY G

Manisha Panta 2 Jul 23, 2022
We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Overview This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which

NEU-StatsML-Research 21 Sep 08, 2022
StyleGAN2-ADA - Official PyTorch implementation

Need Help? If you’re new to StyleGAN2-ADA and looking to get started, please check out this video series from a course Lia Coleman and I taught in Oct

Derrick Schultz 217 Jan 04, 2023
FreeSOLO for unsupervised instance segmentation, CVPR 2022

FreeSOLO: Learning to Segment Objects without Annotations This project hosts the code for implementing the FreeSOLO algorithm for unsupervised instanc

NVIDIA Research Projects 253 Jan 02, 2023
Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)

Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)- Emirhan BULUT

Emirhan BULUT 102 Nov 18, 2022
ShapeGlot: Learning Language for Shape Differentiation

ShapeGlot: Learning Language for Shape Differentiation Created by Panos Achlioptas, Judy Fan, Robert X.D. Hawkins, Noah D. Goodman, Leonidas J. Guibas

Panos 32 Dec 23, 2022
Code repo for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper.

InterpretableMDE A PyTorch implementation for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper. arXiv link: https://arxiv.or

Zunzhi You 16 Aug 12, 2022
Gif-caption - A straightforward GIF Captioner written in Python

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

3 Apr 09, 2022
Reaction SMILES-AA mapping via language modelling

rxn-aa-mapper Reactions SMILES-AA sequence mapping setup conda env create -f conda.yml conda activate rxn_aa_mapper In the following we consider on ex

16 Dec 13, 2022
Vision-Language Pre-training for Image Captioning and Question Answering

VLP This repo hosts the source code for our AAAI2020 work Vision-Language Pre-training (VLP). We have released the pre-trained model on Conceptual Cap

Luowei Zhou 373 Jan 03, 2023
Cross-modal Retrieval using Transformer Encoder Reasoning Networks (TERN). With use of Metric Learning and FAISS for fast similarity search on GPU

Cross-modal Retrieval using Transformer Encoder Reasoning Networks This project reimplements the idea from "Transformer Reasoning Network for Image-Te

Minh-Khoi Pham 5 Nov 05, 2022
Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach

Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach Thanh Luan Nguyen, Tri Nhu Do, Georges Kaddoum

Thanh Luan Nguyen 2 Oct 10, 2022