Biology subjects

Kiourlappou, M.

Publications and source records attributed to Kiourlappou, M..

3 recordsLinked to original sources

bioRxiv2025

ChatMDV: Democratising Bioinformatics Analysis Using Large Language Models

BackgroundThe rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable of managing complex biological data. Tools such as Multi-Dimensional Viewer (MDV) offer comprehensive interfaces for data exploration, but still require manual configuration and computational expertise to generate visualisation outputs, limiting accessibility for many users. ResultsWe present ChatMDV, a natural language interface integrated with MDV that allows users to generate high-quality interactive visualisations through natural language commands. ChatMDV employs a retrieval-augmented generation (RAG) pipeline combined with large language models (LLMs) to translate user queries into reproducible Python code and interactive output. This approach enables exploratory and targeted analysis in diverse biological domains. We demonstrate ChatMDVs capabilities using three datasets of increasing complexity: the Peripheral Blood Mononuclear Cells 3K (PBMC3K) dataset, the lung cancer atlas dataset hosted at the Human Cell Atlas and the longitudinal TAURUS study single-cell RNA-sequencing (scRNA-seq) dataset. ConclusionsBy bridging the gap between natural language processing and bioinformatics visualisation, ChatMDV reduces technical barriers, enhances reproducibility, and supports more inclusive scientific inquiry. Its modular design and adherence to FAIR (Findability, Accessibility, Interoperability, and Reuse) principles make it a scalable and adaptable framework for accelerating biological data analysis. Key PointsO_LIChatMDV enables users to create interactive visualisations from biological datasets using natural language. C_LIO_LIThe system combines large language models with MDVs graphical platform to simplify data exploration. C_LIO_LIIt supports reproducibility, adaptability, and FAIR data practices, making it suitable for a wide range of users and use cases. C_LI

bioinformatics↗

bioRxiv2022

Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system

While post-transcriptional control is thought to be required at the periphery of neurons and glia, its extent is unclear. Here, we investigate systematically the spatial distribution and expression of mRNA at single molecule sensitivity and their corresponding proteins of 200 YFP trap protein trap lines across the intact Drosophila nervous system. 98% of the genes studied showed discordance between the distribution of mRNA and the proteins they encode in at least one region of the nervous system. These data suggest that post-transcriptional regulation is very common, helping to explain the complexity of the nervous system. We also discovered that 68.5% of these genes have transcripts present at the periphery of neurons, with 9.5% at the glial periphery. Peripheral transcripts include many potential new regulators of neurons, glia and their interactions. Our approach is applicable to most genes and tissues and includes powerful novel data annotation and visualisation tools for post-transcriptional regulation. Brief outlineA novel high resolution and sensitive approach to systematically co-visualise the distribution of mRNAs and proteins in the intact nervous system reveals that post-transcriptional regulation of gene expression is very common. The rich data landscape is provided as a browsable resource (link), using Zegami, a cloud-based data exploration platform (link). Our solution provides a paradigm for the characterisation of post-transcriptional regulation of most genes and model systems. HighlightsO_LI196/200 (98%) Drosophila genes show discordant RNA and protein expression in at least one nervous system region C_LIO_LI137/200 (68.5%) mRNAs are present in at least one synaptic compartment C_LIO_LINovel localised mRNA and protein discovered in periphery of glial processes C_LIO_LINew paradigm for analysis of post-transcriptional regulation and data exploration C_LI

cell biology↗

bioRxiv2020

Ribo-Pop: Simple, cost-effective, and widely applicable ribosomal RNA depletion

The measurement of RNA abundance derived from massively parallel sequencing experiments is an essential technique. Methods that reduce ribosomal RNA levels are usually required prior to sequencing library construction because ribosomal RNA typically comprises the vast majority of a total RNA sample. For some experiments, ribosomal RNA depletion is favored over poly(A) selection because it offers a more inclusive representation of the transcriptome. However, methods to deplete ribosomal RNA are generally proprietary, complex, inefficient, applicable to only specific species, or compatible with only a narrow range of RNA input levels. Here, we describe Ribo-Pop (ribosomal RNA depletion for popular use), a simple workflow and antisense oligo design strategy that we demonstrate works over a wide input range and can be easily adapted to any organism with a sequenced genome. We provide a computational pipeline for probe selection, a streamlined 20-minute protocol, and ready-to-use oligo sequences for several organisms. We anticipate that our simple and generalizable "open source" design strategy would enable virtually any lab to pursue full transcriptome sequencing in their organism of interest with minimal time and resource investment.

genomics↗