Biology subjects

Horecka, I.

Publications and source records attributed to Horecka, I..

5 recordsLinked to original sources

bioRxiv2026

Expanding TheCellMap.org to visualize a genome-scale genetic interaction network for a human cell line

Genetic interaction networks map functional connections between genes and their corresponding pathways and complexes. We previously developed TheCellMap.org as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) analysis in the budding yeast, Saccharomyces cerevisiae. We have expanded TheCellMap.org to include ~89,000 quantitative genetic interactions identified from genome-scale CRISPR-based analysis of ~4 million human gene pairs in the haploid cell line, HAP1. TheCellMap.org enables users to readily access, visualize and explore human HAP1 genetic interactions, as well as to extract and reorganize sub-networks, applying data-driven network layouts in an intuitive and interactive manner.

systems biology↗

bioRxiv2026

HiMaLAYAS: enrichment-based annotation of hierarchically clustered matrices

Summary: Hierarchical clustering reveals structure in high-dimensional biological matrices, but the resulting dendrogram-defined clusters are commonly visualized rather than tested for annotation enrichment. Existing workflows often require separate tools to cluster data, test enrichment, and display heatmaps. Here, we introduce Hierarchical Matrix Layout and Annotation Software (HiMaLAYAS), a Python framework for post hoc enrichment-based annotation and visualization of hierarchically clustered matrices. HiMaLAYAS treats dendrogram-defined clusters as statistical units, tests categorical annotations for enrichment, controls multiple testing, and renders significant annotations alongside clusters in a single workflow. Applied to a Saccharomyces cerevisiae genetic interaction profile similarity matrix, HiMaLAYAS annotated biological-process enrichment at parent-cluster and zoomed-cluster levels. Parent-level annotations persisted across clustering-parameter sweeps, exceeded null expectations from randomized cluster membership and annotation labels, and remained stable under matrix perturbation. HiMaLAYAS also annotated clusters in a non-biological country-sector input-output matrix, extending its application beyond biology. Availability and Implementation: HiMaLAYAS is a Python package available via pip, distributed under the BSD 3-Clause License at https://github.com/himalayas-base/himalayas, and archived on Zenodo at https://doi.org/10.5281/zenodo.18610373.

systems biology↗

bioRxiv2025

A global genetic interaction map of a human cell reveals conserved principles of genetic networks

We generated a genome-scale, genetic interaction network from the analysis of more than 4 million double mutants in the haploid human cell line, HAP1. The network maps [~]90,000 genetic interactions, including thousands of extreme synthetic lethal and genetic suppression interactions. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes, pathways, biological processes, and cellular compartments. Comparative analyses showed that general principles of genetic networks are conserved from yeast to human cells. A genetic interaction network mapped in a single genetic background complements the DepMap gene co-essentiality network, recapitulating many of the same biological connections and also capturing unique functional information to reveal roles of uncharacterized genes and molecular determinants of specific cancer cell line genetic dependencies.

systems biology↗

bioRxiv2024

MassDash: A Web-based Dashboard for Targeted Mass Spectrometry Visualization

With the increased usage, diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform, DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity providing additional insights into the data. The modular framework is easily extendable enabling rapid algorithm development of novel peak picker techniques, such as deep learning based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

bioinformatics↗

bioRxiv2017

A conserved signaling network monitors delivery of sphingolipids to the plasma membrane in budding yeast

In budding yeast, cell cycle progression and ribosome biogenesis are dependent upon plasma membrane growth, which ensures that events of cell growth are coordinated with each other and with the cell cycle. However, the signals that link the cell cycle and ribosome biogenesis to membrane growth are poorly understood. Here, we used proteome-wide mass spectrometry to systematically discover signals associated with membrane growth. The results suggest that membrane trafficking events required for membrane growth generate sphingolipid-dependent signals. A conserved signaling network plays an essential role in signaling by responding to delivery of sphingolipids to the plasma membrane. In addition, sphingolipid-dependent signals control phosphorylation of protein kinase C (Pkc1), which plays an essential role in the pathways that link the cell cycle and ribosome biogenesis to membrane growth. Together, these discoveries provide new clues to how growth-dependent signals control cell growth and the cell cycle.

cell biology↗