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Uncovering mitochondrial activity by transcriptome data with mitology

Uncovering mitochondrial activity by transcriptome data with mitology Author(s): Stefania Pirrotta,Laura Masatti,Paolo Martini,Massimo Bonora,Enrica Calura Affiliation(s): Biology Department, University of Padova Mitochondria are dynamic organelles that play crucial roles in energy transformation, biosynthesis, and cellular signaling. They actively process biological information, detecting and reacting to both internal and external stimuli. Through intricate physical interactions and diffusion mechanisms within cellular networks, mitochondria integrate diverse inputs and generate signals that finely adjust cellular functions and overall physiology.

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TEKRABber: A software for comparative analysis of gene regulatory networks including transposable elements

TEKRABber: A software for comparative analysis of gene regulatory networks including transposable elements Author(s): Yao-Chung Chen,Arnaud Maupas,Katja Nowick Affiliation(s): Human Biology and Evolution, Institute of Bioinformatics, Freie Universität Berlin, Germany Transposable elements (TEs), also known as “jumping genes”, are DNA fragments that can move within a genome and have paradoxically been seen as both a potentially deleterious genomic phenomenon and a potent driving force behind evolution. The genome-protecting KRAB zinc finger (KRAB-ZNF) proteins play a critical role in repressing TE expression within mammalian genomes, engaging in a dynamic interplay.

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Synaptome.db: a bioconductor package for analysis of synaptic proteomics data

Synaptome.db: a bioconductor package for analysis of synaptic proteomics data Author(s): Dr Oksana Sorokina,Anatoly Sorokin,J Douglas Armstrong Affiliation(s): University of Edinburgh The neuronal synapse is underpinned by a large and diverse proteome with the molecular evidence spread across many primary datasets. Recently, we curated them into a single database describing a landscape of ∼8000 proteins found in studies of mammalian synapses [1]. We provide the programmatic access to the database via package Synaptome.

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spatialFDA - a tool for spatial multi-sample comparisons

spatialFDA - a tool for spatial multi-sample comparisons Author(s): Martin Emons,Mark Robinson Affiliation(s): University of Zurich With the more widespread use of spatial omics, scientists have been able to analyse cells in their tissue environment. Often diseases show changes in cellular interactions compared to healthy tissue and the comparison of such changes in space are of interest. There are various metrics to quantify cellular interactions, but most of them do not incorporate a mechanism to have them compared across samples.

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sosta: a framework to analyse spatial structures from spatial omics data

sosta: a framework to analyse spatial structures from spatial omics data Author(s): Samuel Gunz,Mark Robinson Affiliation(s): Department of Molecular Life Sciences, University of Zurich, Switzerland Understanding the organization of tissue at a molecular level is important for studying the complexity of biological systems. Recent technological advances have made it possible to quantify the abundance of genes, transcripts and proteins not only at cellular resolution but also in their spatial context.

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Pedixplorer: A modern R BioConductor package for efficient kinship analysis to draw and request complex pedigrees

Pedixplorer: A modern R BioConductor package for efficient kinship analysis to draw and request complex pedigrees Author(s): Louis Le Nézet,Jason Sinnwell,Pascale Quignon,Catherine André Affiliation(s): 1 CNRS - UR1, ERL Inserm U1305 - UMR6290 IGDR (Institut de Génétique et Développement de Rennes) - 35000 Rennes, France Understanding kinship relationships is fundamental in genetic studies, particularly in pedigree analysis in genetic linkage studies and population genetics. The legacy “kinship2” CRAN package has been a cornerstone in this area; however, with its unmaintained status and evolving needs in bioinformatics, arises a necessity for an updated, flexible toolset and user-friendly application.

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msqrob2SCP: a flexible workflow for addressing the hierarchical correlation in SCP data

msqrob2SCP: a flexible workflow for addressing the hierarchical correlation in SCP data Author(s): Christophe Vanderaa,Stijn Vandenbulcke,Lieven Clement Affiliation(s): Ugent The field of mass spectrometry (MS)-based single-cell proteomics (SCP) is gaining momentum, with recent studies demonstrating the application of SCP by measuring thousands of proteins across hundreds to thousands of cells. However, the presence of hierarchical correlations (HC) in real-life SCP experiments poses a significant challenge to reliable data analysis and biomarker identification.

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Mass spectrometry-based proteomics/metabolomics and Bioconductor: from the early days to 2024

Mass spectrometry-based proteomics/metabolomics and Bioconductor: from the early days to 2024 Author(s): Laurent Gatto,Sebastian Gibb,Johannes Rainer Affiliation(s): de Duve Institute, UCLouvain, Belgium The Bioconductor project has always been best known for its state-of-the-art infrastructure for genomics data analysis and comprehension. Starting with packages for microarrays, and later RNA Sequencing, transcriptomics has been the most visible part of the Bioconductor iceberg. Proteomics has been part of the early days of the project, with the *PROcess* package to process SELDI-TO-MS data, that was cited/documented in the very first Bioconductor paper (2004) and monograph (2005).

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Fast trajectory-based differential expression and splicing analyses

Fast trajectory-based differential expression and splicing analyses Author(s): Alexandre Segers,Jeroen Gilis,Davide Risso,Koen Van den Berge,Lieven Clement Affiliation(s): Ghent University Trajectory inference methods have revolutionized the analysis of dynamic gene expression changes through single cell RNA-sequencing (scRNA-seq). To this end, methods have been developed to identify differentially expressed genes across various lineages or conditions, often using generalized additive models (GAMs) to account for pseudotime in cellular processes. With the ever-increasing size of scRNA-seq datasets, particularly multi-patient data, the computational burden has exploded, requiring methods to resort to the log-normal distribution, although this ignores heteroscedasticity.

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Development of a Convolutional Neural Network for Automated Copy Number Variants Validation and its Application in the UKB

Development of a Convolutional Neural Network for Automated Copy Number Variants Validation and its Application in the UKB Author(s): Simone Montalbano,Andres Ingason Affiliation(s): Institute of Biological Psychiatry Structural variants are a major source of variation in the human genome. In particular, copy number variants (CNVs) have been associated with multiple diseases and syndromes. CNVs are typically defined as deletions or duplications spanning ~50kbp to ~10Mbp. Genotyping arrays still remain the most widely used platform to detect CNVs from, especially in large biobanks.

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crupR: a Bioconductor package to predict condition-specific enhancers from ChIP-seq experiments

crupR: a Bioconductor package to predict condition-specific enhancers from ChIP-seq experiments Author(s): Persia Akbari Omgba,Martin Vingron,Verena Laupert Affiliation(s): Max Planck Institute for Molecular Genetics Enhancers are cis-regulatory elements that play a key role in the regulation of gene expression. Epigenetic data is usually exploited to identify them. Their prediction using such data requires a carefully trained classification method. Enhancer activity varies across conditions such as cell types, developmental stages or cancer tissues.

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Context is important! Identifying context aware spatial relationships with Kontextual.

Context is important! Identifying context aware spatial relationships with Kontextual. Author(s): Farhan Ameen,Shila Ghazanfar,Ellis Patrick Affiliation(s): University of Sydney State-of-the-art technologies such as PhenoCycler, IMC, MERFISH, Xenium, and others can deeply phenotype cells in their native environment, providing a high throughput means to effectively quantify spatial relationships between diverse cell populations in their native tissue environments. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications.

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Conformal inference for cell type prediction leveraging the cell ontology

Conformal inference for cell type prediction leveraging the cell ontology Author(s): Daniela Corbetta,Livio Finos,Davide Risso Affiliation(s): Department of Statistical Sciences, University of Padova Recently, there has been rapid advancement in single-cell RNA sequencing technologies, leading to the generation of diverse datasets. A multitude of annotated datasets are now readily accessible, providing valuable references for annotating cells in unannotated datasets originating from similar tissues. Typically, a model is chosen and trained on the reference data to predict the label of a new, unannotated cell in the query dataset.

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Challenges in Integrating Transcriptomics and Metabolomics Data in Cancer Research

Challenges in Integrating Transcriptomics and Metabolomics Data in Cancer Research Author(s): Maryna Chepeleva,Petr V. Nazarov Affiliation(s): Luxembourg Institute of Health Understanding the molecular mechanisms driving cancer is crucial for developing of effective treatment strategies. Transcriptomics uncovers key regulatory pathways and molecular signatures associated with disease progression, while metabolomics provides a snapshot of cancer metabolic phenotype, highlighting metabolic reprogramming and dysregulated pathways driving cancer growth and survival. However, the direct linkage of transcriptomic and metabolomic levels of regulation is still challenging but offers a promising direction for unraveling the complexities of cancer processes.

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Benchmark of single-cell RNA-seq analysis workflows: evaluating scalability across R and Python

Benchmark of single-cell RNA-seq analysis workflows: evaluating scalability across R and Python Author(s): Ilaria Billato,Gabriele Sales,Chiara Romualdi,Davide Risso Affiliation(s): Department of Biology, University of Padova The rapid growth of single-cell RNA-seq data has led to an increase in computationally intensive workflows, making it crucial to adopt more efficient algorithms and out-of-memory data representations for analysis. This study compares various workflows for single-cell data analysis, evaluating their performance and efficacy within R and Python programming environments.

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Batch effect detection and visual quality control with CytoMDS, a Bioconductor package for low dimensional representation of distances between cytometry samples

Batch effect detection and visual quality control with CytoMDS, a Bioconductor package for low dimensional representation of distances between cytometry samples Author(s): Philippe Hauchamps,Dan Lin,Laurent Gatto Affiliation(s): Computational Biology and Bioinformatics, de Duve Institute, UCLouvain, Belgium Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, identification of potential batch effects and sample outliers is paramount, to avoid mistaking these effects for true biological signal in downstream analyses.

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BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis

BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis Author(s): Vipul Singhal,Nigel Chou,Joseph Lee,Kok Hao Chen,Shyam Prabhakar Affiliation(s): Genome Institute of Singapore A core property of solid tissue is the spatial arrangement of cell types into stereotypical spatial patterns. These cell types can be investigated with spatial omics technologies to reveal both their omics features (transcriptomes, proteomes, etc), and their spatial coordinates. Because a cell’s state can be influenced by interactions with other cells, it is informative to cluster cells using both omics features and spatial locations.

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Annotating the Human Cell Atlas with HPCell: an extensible high-performance-computing grammar for omic analyses

Annotating the Human Cell Atlas with HPCell: an extensible high-performance-computing grammar for omic analyses Author(s): Stefano Mangiola,Jiayi Si Affiliation(s): Adelaide University Single-cell and spatial omic technologies have transformed biological research. The vast amount of data generated challenges bioinformatics pipelines and the ability of a single user to keep pace with the rapidly evolving needs of impactful data-oriented research. One option is to use static Snakemake-like workflows, which live outside R.

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Analysis of Biomedical Networks with BioNAR

Analysis of Biomedical Networks with BioNAR Author(s): Anatoly Sorokin,Colin Mclean,J. Douglas Armstrong,Oksana Sorokina Affiliation(s): Okinawa Institute of Science and technology Networks and graphs are ubiquitous in biology, they are used to represent a wide range of concepts from intermolecular interactions in the protein complexes, to gene-disease associations, and food networks in ecology. In the analysis of such networks, scientists would need to identify keystone nodes, find clusters of highly connected nodes, and annotate edges and nodes with numerical or categorical information.

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A metric set to investigate the evolution of expression divergence following gene duplications in bulk and single-cell RNA-seq data

A metric set to investigate the evolution of expression divergence following gene duplications in bulk and single-cell RNA-seq data Author(s): Fabrício Almeida-Silva,Yves Van de Peer Affiliation(s): VIB-UGent Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium Gene and genome duplications are important sources of novel genetic material for evolution to work with. However, after duplications, retained duplicate pairs evolve differently depending on the duplication mechanism that originated them. At the transcriptional level, some gene classes tend to preserve ancestral expression patterns, while others tend to subfunctionalize or neofunctionalize, eventually leading to novel traits.

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