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Hierarchical clustering organizes high-dimensional biological matrices and is commonly used for visualization rather than statistical inference. Most enrichment-based analyses of such matrices are confined to gene expression data and fixed workflows. We introduce Hierarchical Matrix Layout and Annotation Software (HiMaLAYAS), a framework for post hoc enrichment-based annotation of hierarchically clustered matrices. HiMaLAYAS treats clusters as statistical units, evaluates annotation enrichment, and renders significant annotations alongside the matrix. HiMaLAYAS supports biological and non-biological domains. 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.Competing Interest StatementThe authors have declared no competing interest.Funder Information DeclaredCanada Research Chairs, CRC-2022-00215Canadian Institutes of Health Research, CIHR; grant 497277

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, . 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.

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.

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.

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 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. MashDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash.Competing Interest StatementThe authors have declared no competing interest.

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 are required for normal sphingolipid-dependent signaling. 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 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 signaling controls cell growth and the cell cycle.