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# Ideas List, Google Summer of Code 2025


Welcome to the Ideas Page for the GSoC 2025, here you will find a list of possible GSoC projects for the netZoo
organization.
- [Ideas List, Google Summer of Code 2025](#ideas-list-google-summer-of-code-2025)
- [FAQ](#faq)
- [What is the netZoo project?](#what-is-the-netzoo-project)
- [Who should apply to be a contributor?](#who-should-apply-to-be-a-contributor)
- [Who is gonna mentor the project?](#who-is-gonna-mentor-the-project)
- [How have we chosen these projects?](#how-have-we-chosen-these-projects)
- [Ideas](#ideas)
- [Idea #1: Standardization of interfaces, inputs and outputs](#idea-1-standardization-of-interfaces-inputs-and-outputs)
- [Idea #2: Unit tests](#idea-2-unit-tests)
- [Idea #3: Migrate GRAND database to secure and updated technology stacks.](#idea-3-migrate-grand-database-to-secure-and-updated-technology-stacks)
- [Idea #4: Implement graph visualization strategies for inferred networks](#idea-4-implement-graph-visualization-strategies-for-inferred-networks)

**What is the netZoo project?**
Welcome to the Ideas Page for the GSoC 2025, here you will find a list of possible GSoC projects for the netZoo project.

## FAQ

### What is the netZoo project?



The **Network Zoo (netZoo)** is a project spearheaded by the team of **Prof. John Quackenbush** at the **Harvard T. Chan School of
Public Health**. The lab focuses on computational methods and analyses of how transcription factors (TFs) and genes

The Network Zoo (netZoo) is a project spearheaded by the team of Prof. John Quackenbush at the Harvard T. Chan School of
Public Health. The lab focuses on computational methods and analyses of how transcription factors (TFs) and genes
interact to regulate homeostasis and are disrupted in complex diseases, such as cancer or COPD. The netZoo is a
collection of open-source methods to infer gene regulatory network, conduct differential network analyses, estimate
community structure, and explore the transitions between biological states. The methods hosted in the netZoo have been
developed by the lab or close collaborators during the years and have been used in dozens of scientific publications.
The software is mostly written in R (netZooR) and Python (netZooPy), with some exceptions in C++ and Matlab, and
it is maintained by the lab with the goal of allowing dissemination, reproducibility, and educational purposes.

To have an idea of what is in the netZoo you can check the main paper:
To have an idea of what is in the netZoo you can check the **main paper**:
> The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Ben Guebila et al, 2023. Genome Biology. [doi:10.1186/s13059-023-02877-1](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02877-1)

To look into the code you can refer to the main GitHub [netZoo page](https://github.com/netZoo), and navigate into the
To look into the **code** you can refer to the main GitHub [netZoo page](https://github.com/netZoo), and navigate into the
[netZooR](https://github.com/netZoo/netZooR) and [netZooPy](https://github.com/netZoo/netZooPy) repos.

**Who should apply to be a contributor?**
### Who should apply to be a contributor?

We welcome all types of contributors, with a preference for students who want to dive into the computational biology
field with a hands on approach. Working on the netZoo software requires
**We welcome all types of contributors, with a preference for students who want to dive into the computational biology
field** with a hands on approach. Working on the netZoo software requires
at least a basic level of interest and curiosity for biological questions, such that all methods are still relevant
and helpful for anyone who wants to run graph analysis on patient molecular biology data.

**Who is gonna mentor the project?**
### Who is gonna mentor the project?

Each project will be mentored by one of the maintainers of the netZoo packages and supervised by Prof. Quackenbush.
On top of direct mentoring, the contributor will have access to a cutting-edge learning environment at the Harvard
Biostatistics department. The Quackenbush lab is a highly collaborative environment that has hosted multiple highschool, undergraduate, and
graduate students during the years. We hold weekly lab meetings where you will hear from all students and postdocs and
will be able to ask questions and give updates on your progress.

**How have we chosen these projects?**
### How have we chosen these projects?

We have looked into issues and enhancement features that have come up while developing the software.
The network zoo has started as a harmonization project, where many individual computational methods have been
grouped under R or Python packages. Hence, project #1 and #2 will focus on refactoring and reorganizing code
to make it more efficient and less redundant. Secondly, we recognize that scientific software, while addressing novel
and important questions, is often lacking of the robustness of properly developed packages. With project #3 we
encourage contributors who are interested in writing unit tests and CI scripts.
We have looked into issues and enhancement features that have come up while developing the software. For instance, with
Idea #1 and Idea #2 (refactoring and testing)
we focus on refactoring, reorganizing, and appropriately testing code
to make it more efficient and less redundant. Similarly, we also need to upgrade the backend of the database we use to
share our networks, which is the focus of Idea #3.
Finally, to those who are more interested in working on graph inference and visualization, we suggest to look into
project #4. Indeed, we would like to expand and improve the used experience, with more meaningful and helpful
project #4.

Indeed, we would like to expand and improve the user experience, with more meaningful and helpful
visualization methods, more appropriate IO interfaces, and animal-to-animal interfaces that allow seamless integration
of the various analysis steps.

## Idea #1: Refactor code
While we already have a plan on how to carry out each of these projects, we welcome any expertise that could
help and enable us to improve the code and platform. So, if you have some ideas on how to do this, we welcome your input
and would love to hear more about it!

We need a programmer who would clean all I/O functions of netZooPy and/or netZooR,
reconciling the different input formats and
## Ideas

**Time allocation**: ~90 hour, ~175 hours or ~350
**Prerequisites**: ~90 hour, ~175 hours or ~350
**Programming skills**: ~90 hour, ~175 hours or ~350
**Difficulty**: easy medium hard
### Idea #1: Standardization of interfaces, inputs and outputs

## Idea #2: Unit tests
The network zoo has started as a harmonization project to bring together tools that were developed around a common
conceptual framework, but were written and maintained by different individuals. These methods can use
different file formats for both input data and output networks. As a result, not only this increases the risk of errors
for the users, but also serial analyses are hampered in some cases because output files from one method cannot be readily fed to
another. Similarly, the interfaces are not standardized and minor inconsistencies can lead to problems with usability,
interoperability, and documentation.

For this reason, we need a programmer who would clean all I/O functions of netZooPy and/or netZooR, reconciling the
different input/output formats and all interfaces. This way we not only lower the risk for errors, but we also simplify
the user experience and the ability to extend and improve methods in the future.

- **Time allocation**: 175
- **Prerequisites**: OOP and functional programming
- **Programming skills**: R and/or Python
- **Difficulty**: easy
- **Mentors**: Viola Fanfani, Tara Eicher, Derrick DeConti, Marouen Ben Guebila

Description

**Time allocation**: ~90 hour, ~175 hours or ~350
**Prerequisites**: ~90 hour, ~175 hours or ~350
**Programming skills**: ~90 hour, ~175 hours or ~350
**Difficulty**: easy medium hard
### Idea #2: Unit tests

## Idea #3: Unit tests
Both netZooR and netZooPy are packages originally built by harmonizing and reorganizing previously published software
and computational methods. Currently, we require all new methods to include unit tests, and we have CI steps in place
for testing. However, not all
functions and methods have consistent and complete tests and should be expanded and refined.

Both netZooR and netZooPy include unit tests in their CI, however in both cases the tests do not include all
functions and should be expanded and refined. For this project you could do one or multiple of the following
For this project you could do one or multiple of the following
(depending on time committment and skill set of the contributor):
- Write tests for all animals' IO
- Write tests for all internal functions, which are the animals' engines
Expand All @@ -82,25 +110,53 @@ igraph-related [active issue]()https://github.com/netZoo/netZooPy/issues/320.

Description

**Time allocation**: ~90 hour, ~175 hours or ~350
**Prerequisites**: Python and/or R, unit tests
**Programming skills**: easy
**Difficulty**: easy
**Mentors**:
- **Time allocation**: 175 hours
- **Prerequisites**: unit tests
- **Programming skills**: Python and/or R
- **Difficulty**: easy
- **Mentors**: Viola Fanfani, Tara Eicher, Derrick DeConti, Marouen Ben Guebila

### Idea #3: Migrate GRAND database to secure and updated technology stacks.

## Idea #4: Implement graph visualization strategies for inferred networks
Our research team is dedicated to reproducible science. While hosting, sharing, and maintaining code online is
relatively easy and inexpensive, sharing large datasets and networks requires an appropriate space and infrastructure.
Much of our work involves creating large-scale networks for modeling the process of gene regulation, but there are no
national databases for storing such networks, rendering much of our work potentially difficult to reproduce. To overcome
the limitations of public databases, we created [GRAND](https://grand.networkmedicine.org/), an online repository of
gene regulatory networks that have been derived from human tissues, cancer, and cell lines, as well as networks that
capture cellular response to small molecule drugs. GRAND has grown into a vast repository containing over 200,000
networks and associated sample metadata, operating on a server infrastructure originally built for a much smaller scale.
As we continue to grow our analyses and expand the data we make available to other researchers, there is a critical need
to refactor the code and improve our the architecture supporting this important project.

Description
We plan to redesign the GRAND webserver and resource and we would need help implementing a newer and more secure version of the database. We would like to move this locally developed SQLite database to a more robust managed relational database, allowing us to increase security, encryption, backups, and uptime reliability.

- **Time allocation**: 350
- **Prerequisites**: databases, cloud storage, managed relational databases`*`
- **Programming skills**: Python, SQL, MongoDB`*`
- **Difficulty**: hard
- **Mentors**: Derrick DeConti, Marouen Ben Guebila, Viola Fanfani

`*`Preferred but not required.


### Idea #4: Implement graph visualization strategies for inferred networks

The netZoo methods infer or analyze gene regulatory networks that are represented by weighted graphs with more than 20,000 nodes and many more edges. For many analyses, visually representing these networks (and color-coding the nodes based on the properties of the genes) can provide new insights into the processes driving health and disease and comparing graphs between conditions or over time can lead to a deeper understanding of how diseases develop and progress. Effective visualization of such large graphs can be challenging and netZoo does not yet have a dedicated interface or tools for creating robust, scalable visualizations.

We would like to build an appropriate interface to interrogate and visualize the output of the netZoo methods. First, one would need to refactor and standardize the outputs of all the individual methods (see Idea #1). Then one would have to develop graph visualization that address key questions, including sub-setting the nodes/edge and appropriately grouping or coloingr entities.

- **Time allocation**: 350
- **Prerequisites**: Data viz
- **Programming skills**: R and/or Python
- **Difficulty**: medium
- **Mentors**: Viola Fanfani, Tara Eicher, Derrick DeConti

**Time allocation**: ~90 hour, ~175 hours or ~350
**Prerequisites**: ~90 hour, ~175 hours or ~350
**Programming skills**: ~90 hour, ~175 hours or ~350
**Difficulty**: easy medium hard

<!--
Brief descriptions of projects that can be completed in ~90 hour, ~175 hours or ~350 hours of your GSoC contributor’s time (and labeled appropriately).
For each project, a list of prerequisites, description of programming skills needed and estimation of difficulty level (easy, medium, hard).
If your organization plans to focus on mostly student level potential GSoC contributors for all projects that is fine, but please state it explicitly on your Ideas Page. Or if a given project idea is geared more toward a student level or a more advanced developer please state it clearly in the project idea so there is no confusion for applicants.
A list of potential mentors.
It must NOT be a link to your bug tracker. -->