This README will contain at least a section in English (main) and another in French.
This project is of academic scope and individually mantained. It may not follow best practices or suit your particular purpose of use. Therefore use it at your own risk.
If it were helpful in any way, please let me know! If you would like to contribute to it, hit me up (PR welcome!).
A lot of things? None of them?
This project has radically changed. At first I thought it would be a good idea to explore the tools related to my "analyse de séquences" introductory course. Afterwards I found out that other courses of my first semester were closely related (what a surprise huh? Bioinformatics lectures are related? Who would have thougt!) I decided to use it as a centralised repository for various things, all bioinformatics related.
I should probably remove or modify the table of contents, given that it is no longer true. Note that each course will probably have its own branch. This seems to be the most sensible approach, for now.
Exploration, use and implementation of Python and R tools for DNA sequencing. (Course : Analyse de Séquences)
The repository contains both R and Python code. These are the package / virtual environment managers that I use, along with their respective config file. Experimentally I'm adding support for Julia.
I chose these tools because they enable many things which I consider desirable in a project:
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Reproducibility: unlike conda, poetry virtual environments are reproducible).
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Isolation: No more cluttering your global Python/R libraries.
- Introduction genomes / sequence databases
- Sequence Alignment
- Motif Lookup
- Phylogenetic trees
- Annotation
If you plan accessing any service provided online by the NIH (like BLAST), you should consider getting an API key. You can find more info [here](NCBI Insights : New API Keys for the E-utilities).
Exploration, emploi et développement d'outils dans les langages de programmation Python et R pour l'analyse des séquences. (Cours : M1 Analyse de Séquences)
Ce dépôt contient du code en R et Python. J'utilise deux gestionnaires de paquets / environnements virtuels , un pour chacun. Ce sont les suivants, accompagnés de leurs fichiers de configuration principaux (C'est-à-dire que si vous avez les gestionnaires installés et vous avez les fichiers, vous saurez capables de recréer les environnements afin de pouvoir utiliser les codes trouvés dans ce dépôt).
Je les ai choisis sur d'autres outils car ils possèdent des caractéristiques que je considère désirables dans un projet, notamment:
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Reproductibilité: les environnements virtuels de poetry sont reproductibles, pas comme ceux de conda.
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Isolation: Pas de saturation de vos librairies globales (niveau système ou utilisateur). Chaque projet est contenu dans un dossier séparé.