- Introduction
- Other langauges
- Installation
- Usage
- Methods
- Citing this package
- Contributing
- Info for educators
- Contact me!
gen10 is a package for genomics data analysis and visualization, providing the right tools integrated into your Python code. The original goal of this package is to provide support and tools for education in high school or college, but it can be used for any applicable purpose.
The gen10 package allows to you perform a great and increasing variety of tasks from translating DNA into RNA or protein sequences, to retrieving alphafold structure predictions, simulating mutations and much more! gen10is a powerful tool and at the same time easy-to-use for students to experiment with, learn from and create their own code. it doesn't need any software installation and can be used directly from a web browser.
If you are an educator you can see why you need to incorporate this into you class in the info for educators section.
- Read the documentation in your language
- Lee la documentación en tu lenguaje
- Lesen Sie das Dokument in ihrer Sprache
- Llegeix la documentació en el seu idioma
You can install the package using pip:
pip install gen10Alternatively, you can use a browser-based notebook to interact with the package and run your code by cells. This is a very useful tools for education. We have prepared a step-by-step tutorial in a Google Colab Notebook here.
gen10 works as any other pip package. You can import it to your code by adding
import gen10and then use the methods provided by the package. Remember to use a method from a package in Python you should write:
output = gen10.method_name(arguments)If you are completely new to coding or to Python, you can start with the above mentioned tutorial in Google Colab Notebook.
The currently available methods are the following. Note that we are always updating the methods and adding new ones!
| # | Name | Description | Arguments | Outputs |
|---|---|---|---|---|
| 1 | dna2rna() | Transcribes the provided DNA string into a RNA string by changing the bases (A->U, T-> A, C->G, G->C). | string | string |
| 2 | rna2amino() | Transcribes the provided DNA string into an aminoacid string by reading codons (3x bases) and using the catalog. | string | string |
| 3 | dna2amino() | Transcribes DNA strings directly into aminoacids strings, it's a merge of the dna2rna and rna2amino methods. | string | string |
| 4 | rna2dna() | Transcribes RNA strings back into DNA strings. | string | string |
| 5 | compare() | Compares the strings (regardless if DNA, RNA, or aminoacids), it always returns a boolean and a string. True if both strings are identical, or False and where do the string differ. | string1, string2 | boolean, string |
| 6 | check() | It checks if the provided string is a valid DNA or RNA string. It does not check for aminoacid strings. | string | string |
| 7 | read_input() | Used to open files. The full path to the file must be saved in the same folder as this file and can have only 1 sequence. | string | string |
| 8 | create_mutation() | Returns a new string with a mutation (only 1 per run). The mutation can change a base, erase a base or add a new one in any position. | string | string |
| 9 | iterate() | By inputting a list of inputs and a list of functions it returns a table with all the results for each functions and input. | list, list | dataframe (table) |
| 10 | tosingle() | Transcribes an aminoacid string from three-letter code to single-letter code. | string | string |
| 11 | alphafold_prediction() | By inputting a UniProt ID pbd file of the predicted protein's structure. |
string | dictionary |
| 12 | generate_protein() | By inputing the resulting dictionary of alphafold_prediction() it returns a visualization of the predicted protein's strucutre. |
dictionary | None |
| 13 | cut_dna(string, integer) | Cuts the DNA string into two parts at the specified position. | string and integer | string Original DNA with a marked cut |
| 14 | repair_dna(string, string, integer, string) | Repairs a cut DNA string by either deleting a base (NHEJ) or adding specific bases at the specified location (HDR). | string DNA string, string type of repair (NHEJ or HDR), integer Optional: cut position, string Optional: string to insert by HDR repair | string Repaired DNA |
| 15 | find(string, sequence) | Finds a local sequence in a larger, global sequence. | string, string (global, local) | [(int, int)] indexes of the found position |
| 16 | check_codon(string) | Checks for non-existing codons in a dna or rna sequence. | string | ['ABC'] list of non-existing codons |
| 17 | reverse_complement(dna) | Computes the reverse complement of a given DNA sequence. | string | string |
| 18 | gc_content(dna) | Calculates the GC content (percentage) of a given DNA sequence. | string | float |
| 19 | melting_temperature(dna) | Calculates the melting temperature (Tm) of a short DNA sequence using the Wallace rule. | string | float |
| 20 | mutate_site(sequence, pos, new_base) | This function mutates a specific site in a DNA sequence. | string, int, string | string |
| 21. | simulate_pcr(sequence, fwd_primer, rev_primer) | This function simulates a PCR reaction using the provided sequence, forward and reverse primers. | string, string, string | string |
| 22 | get_identifier(sequence) | Generates a unique identifier for the sequence by checking if it is DNA, RNA, or protein. | string | string |
| 23 | write_fasta(sequences, identifiers=None, filename="output.fasta") | Writes one or multiple sequences to a FASTA file, separated by an empty line. | string or list of strings, string or list of strings (optional), string (optional) | None (writen file) |
| 24 | read_fasta(filename) | Reads a FASTA file and returns lists of sequence identifiers and sequences. | string | identifiers (list), sequences (lists) |
| 25 | genbank_parser(filename) | Parses the data from a GenBank file into a usable dictionary. | filename (str) | dictionary |
- Official UniProt website: https://www.uniprot.org
- For genes: https://www.ensembl.org/Multi/Tools/Blast
- UniProt are available in the alpahfold website itself: https://alphafold.ebi.ac.uk
If you make use of this code, please cite it:
@software{joanalnu_2025,
author = [Alcaide-Núñez, Joan],
title = {GEN10 package},
month = {April},
year = {2025},
publisher = {Zenodo},
version = {1.4.2},
doi = {10.5281/zenodo.15251890},
url = {https://github.com/joanalnu/gen10},
}Feel free to submit any issues or send pull requests to the repository!
A package is a Python code that provides functions (i.e. methods) to be used directly into your code only by calling them, without having to write any further. Notebooks are easily usable by students and, since they are browser-based, they do not require any installations, making it ideal for school-managed devices.
First, identify in your curriculum where you can integrate the software, which is already built aligned with the general education guidelines. Then you should start by explaining the fundamental concepts of genomics in your biology or science class, as you would do normally. Then you can introduce this tool to students and explain how to use it.
You can use the software to design problem solving challenges that require students to use critical thinking and coding skills. For example, a scenario where a gene mutation causes a disease, and ask students to write code that identifies and corrects the mutation. This type of activities foster creativity and problem-solving skill and led further to more science like CRIPSR-Cas9.
Also, perform planned activities where students apply what they've learned in real life. Create assignments where students write simple code using the pre-established functions to emulate genetic processes such as transcription and translation.
By providing step-by-step instructions students will have better chances of understanding the biological content and a better usage of the full potential of this tool. Moreover, providing by integrating real-world examples and application in genomics and biotechnology can increase student motivation and interest, and show and discuss modern research tools.
Finally, you can also adopt a flipped classroom approach by assigning software tutorials as homework and use class time for interactive and applied learning. This allows for maximized classroom engagement and allows for more personalized instruction.
Encouraging collaboration by planning group projects, students can work together to solve more complex problem. And collaborative projects fosters teamwork and allow students to learn from each other.
By incorporating these strategies, you can effectively use this software to enhance your biology curriculum, engage students, and foster a deeper understanding of both genomics and coding.
This is a useful resource for students to learn both genomics and basic coding. On the one hand, this is a powerful tool that enables students to apply what they have learned regarding biology. It is made to be interactive and customizable and anyone can run their own code without knowledge of coding. On the other hand, students will learn and get first-hand experience with bioinformatics and computation. Coding is an essential skill for future workers, regardless their field.
Further, the fact that it is web-based and does not need any installation makes it perfect for school managed devices and enables usage regardless of operating system. It also fosters a teamwork and communication skills, as projects can be done in collaboration.
Additionally, the features of the software are aligned with the scholar curriculum and it shows practical applications of classroom content right away. It also promotes critical thinking by allowing students to write their own code to solve problems and engage actively. And prior knowledge of coding is not required at all, as students will use the pre-established functions that enable a wide range of possibilities. Further, students can adapt their code to their problems or write new functions. The code is easily scalable and has endless possibilities!
If you have further doubts, comments, or suggestions, please reach out to me.
Please note that translations to other langauges (of the package, the notebook tutorials, the README and other documentation) are welcome. I will be happy to translate them to any language under request.