Skip to content

Latest commit

 

History

History
executable file
·
131 lines (79 loc) · 6.08 KB

README.md

File metadata and controls

executable file
·
131 lines (79 loc) · 6.08 KB
Raw

Overview

OMICS studies attribute a new role to the noncoding genome in the production of novel peptides. The widespread transcription of noncoding regions and the pervasive translation of the resulting RNAs offer a vast reservoir of novel peptides to the organisms.

ORFmine1, 2 is an open-source package that aims at extracting, annotating, and characterizing the sequence and structural properties of all Open Reading Frames (ORFs) of a genome (including coding and noncoding sequences) along with their translation activity. ORFmine consists of several independent programs that can be used together or independently:

  • ORFtrack
  • ORFold
  • ORFribo
  • ORFdate

Built with

  • python 3.6
  • miniconda 3
  • pyHCA 3
  • R
  • bash
  • Docker

All programs and dependencies are listed here.

Getting started

Prerequisites

Installation

Simply pull the ORFmine image from Dockerhub.

For docker:

# pull the ORFmine docker image from Dockerhub
docker pull lopesi2bc/orfmine:latest

For singularity:

# create a directory that will host the singularity image of ORFmine (adpat the location and directory name)
mkdir ~/orfmine

# build a singularity image named orfmine_latest.sif that will be located in ~/orfmine (to adapt)
singularity build ~/orfmine/orfmine_latest.sif docker://lopesi2bc/orfmine:latest

If you have any error, it might come from a permissions problem so you should try using these commands with sudo as prefix.

Usage

For usage examples, please check the Quick start section of our documentation page.

Documentation

Our full documentation is accessible here.

Issues

If you have suggestions to improve ORFmine or face technical issues, please post an issue here.

Contact

Anne Lopes - [email protected]

Citing

If you use only ORFtrack

Please cite:

Papadopoulos, C., Chevrollier, N., Lopes, A. Exploring the peptide potential of genomes. Meth. Mol. Biol. (2022).

If you use only ORFfold with HCA, IUPred and Tango

Please cite:

Papadopoulos, C., Chevrollier, N., Lopes, A. Exploring the peptide potential of genomes. Meth. Mol. Biol. (2022)

Bitard-Feildel, T. & Callebaut, I. HCAtk and pyHCA: A Toolkit and Python API for the Hydrophobic Cluster Analysis of Protein Sequences. bioRxiv 249995 (2018).

Mészáros, B., Erdős, G. & Dosztányi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic acids research 46, W329–W337 (2018).

Linding, R., Schymkowitz, J., Rousseau, F., Diella, F. & Serrano, L. A comparative study of the relationship between protein structure and β-aggregation in globular and intrinsically disordered proteins. Journal of molecular biology 342, 345–353 (2004).

Otherwise, if you use ORFold with a combination of HCA, IUPred and Tango

Please cite:

Papadopoulos, C., Chevrollier, N., Lopes, A. Exploring the peptide potential of genomes. Meth. Mol. Biol. (2022)

For HCA, cite:

Bitard-Feildel, T. & Callebaut, I. HCAtk and pyHCA: A Toolkit and Python API for the Hydrophobic Cluster Analysis of Protein Sequences. bioRxiv 249995 (2018).

For IUPred, cite:

Mészáros, B., Erdős, G. & Dosztányi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic acids research 46, W329–W337 (2018).

For Tango, cite:

Linding, R., Schymkowitz, J., Rousseau, F., Diella, F. & Serrano, L. A comparative study of the relationship between protein structure and β-aggregation in globular and intrinsically disordered proteins. Journal of molecular biology 342, 345–353 (2004).

If you use ORFribo or ORFdate

Please cite:

Papadopoulos, C., Arbes, H., Chevrollier, N., Blanchet, S., Cornu, D., Roginski, P., Rabier, C., Atia, S., Lespinet, O., Namy, O., Lopes, A. (submitted).

Licence

The ORFmine project is under the MIT licence. Please check here for more details.

References

  1. Papadopoulos, C., Chevrollier, N., Lopes, A. Exploring the peptide potential of genomes. Meth. Mol. Biol. (2022).
  2. Papadopoulos, C., Arbes, H., Chevrollier, N., Blanchet, S., Cornu, D., Roginski, P., Rabier, C., Atia, S., Lespinet, O., Namy, O., Lopes, A. (submitted).
  3. Bitard-Feildel, T. & Callebaut, I. HCAtk and pyHCA: A Toolkit and Python API for the Hydrophobic Cluster Analysis of Protein Sequences. bioRxiv 249995 (2018).
  4. Dosztanyi, Z., Csizmok, V., Tompa, P. & Simon, I. The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. Journal of molecular biology 347, 827–839 (2005).
  5. Dosztányi, Z. Prediction of protein disorder based on IUPred. Protein Science 27, 331– 340 (2018).
  6. Mészáros, B., Erdős, G. & Dosztányi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic acids research 46, W329–W337 (2018).
  7. Fernandez-Escamilla, A.-M., Rousseau, F., Schymkowitz, J. & Serrano, L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nature biotechnology 22, 1302–1306 (2004).
  8. Linding, R., Schymkowitz, J., Rousseau, F., Diella, F. & Serrano, L. A comparative study of the relationship between protein structure and β-aggregation in globular and intrinsically disordered proteins. Journal of molecular biology 342, 345–353 (2004).
  9. Rousseau, F., Schymkowitz, J. & Serrano, L. Protein aggregation and amyloidosis: confusion of the kinds? Current opinion in structural biology 16, 118–126 (2006).