2026-07-21 –, Room 1.19 (Ground Floor, Shannon)
Background
Modern genomic research increasingly relies on diverse computational analyses, ranging from differential expression studies to single-cell and spatial transcriptomics. However, laboratory scientists often encounter significant hurdles navigating scattered documentation, heterogeneous tool ecosystems, and inconsistent workflows across platforms.
Objective
We present PyCabinet, a comprehensive Python-based analysis platform designed to consolidate the fragmented genomics tool landscape into a unified, accessible framework. PyCabinet aims to provide a “transcriptomics toolbox” that guides researchers seamlessly from raw FASTQ files to publication-ready results.
Approach
PyCabinet offers:
End-to-end workflow integration: A seamless progression from raw sequencing data to advanced downstream analyses.
Modular tool selection: Multiple algorithmic options for each analysis step, allowing researchers to select methods tailored to their data and scientific questions.
Comprehensive analysis coverage: Support for differential gene expression (DEG), gene regulatory network (GRN) analysis using graph neural networks (GNNs), single-cell RNA-seq, spatial transcriptomics, and a continually expanding set of analytical capabilities.
Unified interface: Consistent Python API and comprehensive documentation across all modules, minimizing the learning curve and technical barriers for laboratory scientists.
I am a PhD student, in cancer omics, and as I dive deep into analysis I constantly realize how difficult it would be for people in the field to work with tools scattered all over place. Hence I decided to work on this package. The thought of having everything organized complied in one place, is what it an easier and usable option for to start with the analysis, with a tool like this to guide. Also with the age of LLMs, the extension of this would be the bot trained on the common questions that we come across when starting in the field.
My name is Ishita, and I am a PhD student at the Technical University of Darmstadt. Alongside my doctoral research, I am independently developing BioCabinet as a personal project, separate from my PhD work.
I started this project with the goal of making genomic data analysis more accessible to wet-lab scientists, particularly those who face steep technical barriers when working with complex, multi-modal datasets. BioCabinet is designed to bring multiple analytical tools together in one place, offering users the flexibility to compare methods, explore results across modalities, and access integrated documentation directly within the analysis workflow.
A key motivation behind this project is to continuously evolve the platform alongside emerging technologies. This includes incorporating LLM-based chatbots trained on extensive biological and technical documentation, enabling researchers to ask questions, navigate tools, and better understand their data in an intuitive way.
Through this project, I aim to contribute a community-driven, up-to-date analysis framework that lowers the barrier to entry for advanced genomics research while remaining adaptable to future developments in the field.