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
A framework for analyzing computer vision models with simulated data

3DB: A framework for analyzing computer vision models with simulated data Paper Quickstart guide Blog post Installation Follow instructions on: https:

3DB 112 Jan 01, 2023
Differentiable scientific computing library

xitorch: differentiable scientific computing library xitorch is a PyTorch-based library of differentiable functions and functionals that can be widely

98 Dec 26, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
This repository contains the code for our fast polygonal building extraction from overhead images pipeline.

Polygonal Building Segmentation by Frame Field Learning We add a frame field output to an image segmentation neural network to improve segmentation qu

Nicolas Girard 186 Jan 04, 2023
The offcial repository for 'CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos', SIGIR2022

CharacterBERT-DR The offcial repository for CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos, Sh

ielab 11 Nov 15, 2022
Awesome-google-colab - Google Colaboratory Notebooks and Repositories

Unofficial Google Colaboratory Notebook and Repository Gallery Please contact me to take over and revamp this repo (it gets around 30k views and 200k

Derek Snow 1.2k Jan 03, 2023
People movement type classifier with YOLOv4 detection and SORT tracking.

Movement classification The goal of this project would be movement classification of people, in other words, walking (normal and fast) and running. Yo

4 Sep 21, 2021
A code implementation of AC-GC: Activation Compression with Guaranteed Convergence, in NeurIPS 2021.

Code For AC-GC: Lossy Activation Compression with Guaranteed Convergence This code is intended to be used as a supplemental material for submission to

Dave Evans 2 Nov 01, 2022
Random-Afg - Afghanistan Random Old Idz Cloner Tools

AFGHANISTAN RANDOM OLD IDZ CLONER TOOLS Install $ apt update $ apt upgrade $ apt

MAHADI HASAN AFRIDI 5 Jan 26, 2022
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

Rishabh Anand 184 Dec 12, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
A semismooth Newton method for elliptic PDE-constrained optimization

sNewton4PDEOpt The Python module implements a semismooth Newton method for solving finite-element discretizations of the strongly convex, linear ellip

2 Dec 08, 2022
PyTorch implementation of Higher Order Recurrent Space-Time Transformer

Higher Order Recurrent Space-Time Transformer (HORST) This is the official PyTorch implementation of Higher Order Recurrent Space-Time Transformer. Th

13 Oct 18, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.

Adelaide Intelligent Machines (AIM) Group 3k Jan 02, 2023
TorchX: A PyTorch Extension Library for More Efficient Deep Learning

TorchX TorchX: A PyTorch Extension Library for More Efficient Deep Learning. @misc{torchx, author = {Ansheng You and Changxu Wang}, title = {T

Donny You 8 May 28, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
This repository contains the code for the paper Neural RGB-D Surface Reconstruction

Neural RGB-D Surface Reconstruction Paper | Project Page | Video Neural RGB-D Surface Reconstruction Dejan Azinović, Ricardo Martin-Brualla, Dan B Gol

Dejan 406 Jan 04, 2023
Probabilistic Gradient Boosting Machines

PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch/Numba, developed by Air

Olivier Sprangers 112 Dec 28, 2022