µSTASIS - Assessment of human microbiota stability across longitudinal samples Pedro Sánchez-Sánchez Institute for Research in Biomedicine (IRB) Barcelona Abstract Microbiome research is moving forward increasingly faster mainly due to the continuous implementations developed throughout the years to gain more detail in taxonomy and functional surveys. For example, this rapid advance is reflected by the diversity of cutting-edge microbiome-based therapeutics such as fecal microbiota transplantation, prebiotics or engineered symbiotic bacteria. So, along the years, microbiome data captured as a snapshot in time has uncovered the interindividual variability of human microbiota meanwhile changes in individualized microbiota emerge as predictors of clinical outcomes and disease forecast.
Tools for CITE-seq preprocessing Helen Lindsay,Bernat Bramon Mora,Raphael Gottardo Biomedical Data Science Center at the Lausanne University Hospital (CHUV) Abstract CITE-seq and related technologies use antibody-bound oligo probes to get a quantitative readout of surface protein expression. These technologies have the potential to enable more fine-grained exploration of single cell phenotypes. How best to normalise antibody-derived tag (ADT) expression data and integrate it with other data modalities is an active research area.
spillR: Causal Modelling of Spillover in Mass Cytometry Marco Guazzini,Alexander Gilbert Reisach,Sebastian Weichwald,Christof Seiler Department of Data Science and Knowledge Engineering, Maastricht University Abstract In mass cytometry marker interference called `spillover', can cause markers to have higher abundances than their true abundances. Chevrier and Crowell et al.~2018 introduced an experimental and computational procedure to estimate spillover and compensate for it in downstream analyses. Their R package CATALYST implements this in two steps: estimate spillover and remove spillover from data.
Short talk: Smooth epigenomics data analysis and visualisation with Bioconductor (or: an ode to Rle)
Smooth epigenomics data analysis and visualisation with Bioconductor (or: an ode to Rle) Pierre-Luc Germain,Mark Robinson ETH & University of Zürich Abstract Workflows for epigenomics data, especially ATAC/ChIP-like data, typically involve a (sometimes clunky) mix of tools, within and outside R. This can create consistency or reproducibility issues, difficulties when trying to combine elements of different workflows, and complicates teaching. Although excellent R/Bioconductor-based solutions are available for many steps (e.g. Rfastp for QC/trimming, Rsubread for alignment, and several packages for downstream analysis and visualisation), some critical steps lack good R-based alternatives (e.
Simplified, strand aware and comprehensive splicing analysis using IntEREst (1.22.0). Ali Oghabian,Mikko Frilander Folkhälsan Research Center & University of Helsinki Abstract Previously several tools were developed that can be used to perform alternative splicing analysis, e.g. DEXSeq, Cufflinks, and Whippet. Intron retention (IR) is, however, not easily detected due to peculiar characteristics of the introns and IR transcripts: introns are normally much lengthier compared to exons; therefore, they require more sequence reads to cover their length; introns feature more repetitive sequence elements i.
Short talk: Sandwich Estimators for Differential Expression Analysis of Multi-Subject scRNA-seq data
Sandwich Estimators for Differential Expression Analysis of Multi-Subject scRNA-seq data Milan Malfait,Jeroen Gilis,Koen Van den Berge,Alemu Takele Assefa,Bie Verbist,Lieven Clement Department of Applied Mathematics, Computer Science and Statistics, Ghent University Abstract Single-cell transcriptomics (scRNA-seq) is a disruptive technology that has the promise to further unravel the molecular basis of complex biological processes. Recently, there is a shift toward single-cell RNA-seq (scRNA-seq) experiments with multiple biological subjects. Indeed, multiple bio-repeats are key to extracting reproducible transcript and gene signatures and biomarkers.
Short talk: OmicSHIELD: privacy-protected federated omic data analysis in multi-center studies with Bioconductor through DataSHIELD
OmicSHIELD: privacy-protected federated omic data analysis in multi-center studies with Bioconductor through DataSHIELD Juan Ramon Gonzalez,Xavier Escribà Montagut,Yannick Marcon Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain Abstract Sharing data across studies are subject to confidentiality and must comply with data protection regulations. Therefore, performing omic data analysis in multi-center studies is a challenging task. DataSHIELD is a software solution for secure data science collaboration that allows privacy-protected data analysis of federated databases.
Short talk: Multi-omic integration with cosmosR and ocEAn to study cross-talks between signalling and metabolism in diseases.
Multi-omic integration with cosmosR and ocEAn to study cross-talks between signalling and metabolism in diseases. Aurelien Dugourd,Julio Saez-Rodriguez Heidelberg Medical University Abstract COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates multi-omic data such as (but not limited to) phosphoproteomics, transcriptomics, and metabolomics data sets. COSMOS leverages extensive prior knowledge of signaling pathways, metabolic networks, and gene regulation with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning.
Short talk: MethQuant: A package providing entropy-based measures for quantifying patterns of DNA methylation heterogeneity in (single-cell) bisulfite sequencing data
MethQuant: A package providing entropy-based measures for quantifying patterns of DNA methylation heterogeneity in (single-cell) bisulfite sequencing data Emanuel Sonder ETH Zurich Abstract MethQuant: A package providing entropy-based measures for quantifying patterns of DNA methylation heterogeneity in (single-cell) bisulfite sequencing data Emanuel Sonder, Izaskun Mallona & Mark D. Robinson DNA methylation is an essential epigenetic mark associated with gene expression regulation. In mammals, it typically affects the 5th carbon of cytosines situated in a CpG dinucleotide context Hence, DNA methylation is a binary mark, being either present or absent at the individual base level.
Short talk: Joint modeling of rare variant genetic effects using deep learning and data-driven burden scores
Joint modeling of rare variant genetic effects using deep learning and data-driven burden scores Brian Clarke,Eva Holtkamp,Hakime Öztürk,Felix Brechtmann,Florian Hölzlwimmer,Julien Gagneur,Oliver Stegle German Cancer Research Center (DKFZ) Abstract Emerging population-scale genomic resources provide novel opportunities to survey the effect of rare variants on phenotypes. Two major challenges in rare variant association studies (RVASs) are (i) the multiple testing problem caused by the large numbers of individual rare variants, and (ii) the sparsity of very rare variants.
Inclusive instruction in cancer data science: Authoring and deployment with yes4cure Alexandru Mahmoud, Brittany Michel, Latrice G Landry, Karen Burns White, Vincent James Carey Channing Division of Network Medicine, Mass General Brigham, Harvard Medical School; Dana-Farber Cancer Institute, Harvard Medical School Abstract yes4cure.bioconductor.org is a Galaxy deployment produced for the YoungEmpowered Scientists component of the Harvard/ Dana-Farber ContinUing Research Experience (YES for CURE) program. YES for CURE presents a variety of educational and research opportunities for high school and undergraduate students from underrepresented populations.
DifferentialRegulation: a novel approach to identify differentially regulated genes. Simone Tiberi,Joël Meili,Mark Robinson University of Zurich Abstract Background. Technological developments have led to an explosion of high-throughput single cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating cellular dynamic processes, such as cell differentiation, cell (de)activation, and gene regulation. In particular, RNA velocity tools (notably velocyto and scVelo), by exploiting the abundance of spliced (mature) mRNA and unspliced (immature) pre-mRNA, enable inferring the RNA velocity of individual cells, i.
Differential embedding analysis of multi-condition single-cell datasets Constantin Ahlmann-Eltze,Wolfgang Huber EMBL Heidelberg Abstract Multi-condition single-cell datasets enable the investigation of cell population-specific treatment effects. For the analysis, cell populations are typically matched across conditions by clustering and applying non-linear transformations. However, discretizing continuous latent structures and the non-linear matching sacrifice statistical power, complicate interpretation, and are difficult to validate. Here, we suggest a new framework, called differential embedding analysis, to perform regression analysis on the low dimensional embeddings of the cells in the gene-space for each condition.
Short talk: DepInfeR - A Bioconductor package for Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling
DepInfeR - A Bioconductor package for Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Alina Batzilla,Junyan Lu,Wolfgang Huber Medical Faculty Heidelberg, University of Heidelberg Abstract The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Drug perturbations can be readily applied to primary cancer samples at a large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a small compound has a range of affinities to multiple proteins.
Short talk: DeeDee - Evaluating, Visualizing, and Integrating Results from Differential Expression Analysis Workflows
DeeDee - Evaluating, Visualizing, and Integrating Results from Differential Expression Analysis Workflows Federico Marini,Lea Rothörl University Medical Center Mainz Abstract Differential expression (DE) analysis is employed ubiquitously as a bioinformatic workflow to study the patterns of gene regulation, and established methods and software tools support the execution of this step, from the command line as well as via interactive web application, simplifying their adoption in the context of individual endeavors. The ease of generating larger, complex datasets encompassing a multitude of experimental conditions, together with the increasing need of contextualizing the own DE results within the wealth of information available at public repositories, has led researchers to perform comparisons and integrative analyses, where they efficiently combine the information from multiple DE results.
CTexploreR, a package to explore CT genes Axelle Loriot,Julie Devis,Anna Diacofotaki,Charles De Smet,Laurent Gatto UCLouvain Abstract Cancer-Testis genes (CT genes), also referred as cancer-germline genes (CG genes), are a group a genes bearing a highly restricted expression pattern. They are normally expressed exclusively in male germ cells, but they become aberrantly activated in a wide variety of tumors. As germ cells do not express antigen presenting molecules, CT genes produce tumor-specific antigens and are extensively studied in the field of tumor immunotherapy.
Co-clustering of Spatially Resolved Transcriptomic Data Andrea Sottosanti,Davide Risso University of Padova Abstract Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumor-microenvironment interaction.
Clinical microbiome data science with MultiAssayExperiment Tuomas Borman,Paulina Salminen,Leo M Lahti University of Turku Abstract Because of the complex and high dimensional nature of microbiome profiling data, machine learning and other computational approaches have become an instrumental part of the researcher’s toolkit in this field. There is an increasing need to develop robust and reproducible methods to integrate and analyze taxonomic, functional, and clinical data across multiple sources, such as microbial abundances in the gut with biomolecular profiling data from blood samples.
Analyzing hydrogen-deuterium exchange mass-spectrometry data in R Oliver Crook,Oliver Crook University of Oxford Abstract A protein’s structure is state-specific and a key determinant of its function. Proteins can undergo subtle structural changes when binding to another protein, small molecule or because of environmental perturbations. Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a technique to explore differential protein structure by examining the rate of deuterium incorporation for specific peptides. This rate will be altered upon structural perturbation and detecting significant changes to this rate requires a statistical test.
Analysis of transposable elements in R and Bioconductor with atena Beatriz Calvo-Serra,Robert Castelo Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain Abstract Transposable elements (TEs) are DNA sequences that can mobilize within the genome either through a DNA or an RNA intermediate. Their insertions have resulted in a complex distribution of repeated elements occupying approximately half of the human genome . These elements, particularly endogenous retroviruses (ERVs), participate in physiological processes and have been involved in the development of some human diseases .
Analysing multiplexed assays of variant effects with mutscan Alexandra Bendel,Guillaume Diss,Charlotte Soneson,Michael B Stadler Friedrich Miescher Institute for Biomedical Research Abstract Multiplex assays of variant effects (MAVE) measure the fitness of large numbers of sequence variants in a single experiment. For example, a large library of variants is created by mutating a sequence of interest (deep mutational scanning, DMS), and the resulting pool of variants is subjected to an assay that allows for amplification or selective enrichment of sequences with desirable properties.
A new principled approach for single-cell proteomics data analysis Laurent Gatto,Christophe Vanderaa de Duve Institute, UCLouvain, Belgium Abstract In a Nature Methods Technology Feature in August 2019 (doi:10.1038/s41592-019-0540-6), Vivien Marx dreamt of single-cell proteomics. Today, thanks to some pioneers' groundbreaking efforts, we can confidently claim that a new and exciting single cell modality is available: mass spectrometry-based, that is un-targeted, single-cell proteomics. Now that that dream has come true, what can we learn from the growing number of studies and datasets published since 2019?