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Use R to Create and Execute Reproducible CWL Workflows for Genomic Research

Use R to Create and Execute Reproducible CWL Workflows for Genomic Research Author(s): Qian Liu Affiliation(s): Roswell Park Comprehensive Cancer Center Social media: https://twitter.com/QianLiu28878838 The bioinformatics community increasingly relies on ‘workflow’ frameworks to manage the analysis of large and complex biomedical data. One solution facilitating portable, reproducible and scalable workflows across platforms is the Common Workflow Language (CWL), which has been widely adopted by the community, including large biomedical projects such as The Cancer Genome Atlas and Galaxy, and cloud computing platforms, such as the Cancer Genomics Cloud (CGC) and CAVATICA.

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Tidy genomic and transcriptomic single-cell analyses

Tidy genomic and transcriptomic single-cell analyses Author(s): Stefano Mangiola,Michael I Love Affiliation(s): WEHI Social media: https://twitter.com/steman_research This workshop will present how to perform genomic and transcriptomic data analysis using the tidy data paradigm. This paradigm became the standard in R data analysis across many fields. It provides a standard way to organise data values, where each variable is a column, each observation is a row, and data is manipulated using a familiar and easy-to-understand vocabulary.

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Statial: A Bioconductor package for identifying spatially-related changes in cell state

Statial: A Bioconductor package for identifying spatially-related changes in cell state Author(s): Ellis Patrick,Farhan Ameen,Sourish Iyengar,Shila Ghazanfar Affiliation(s): The University of Sydney Social media: https://twitter.com/TheEllisPatrick The human body comprises over 37 trillion cells with diverse forms and functions, which can exhibit dynamic changes based on their environmental context. Understanding the spatial interactions between cells and changes in their state within the tissue microenvironment is crucial to comprehending the development of human diseases.

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rGREAT: an R/Bioconductor Package for Functional Enrichment on Genomic Regions

rGREAT: an R/Bioconductor Package for Functional Enrichment on Genomic Regions Author(s): Zuguang Gu Affiliation(s): German Cancer Research Center Functional interpretation is important for genomics and epigenomic studies. GREAT (http://great.stanford.edu/) is a popular tool for functional enrichment on genomic regions and it has been very widely used in current studies. However, as an online tool, GREAT still has limitations, such as outdated annotation data, small numbers of supported organisms (only human and mouse) and gene set collections (only seven).

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Orchestrating Hi-C analysis with Bioconductor

Orchestrating Hi-C analysis with Bioconductor Author(s): Jacques Serizay Affiliation(s): Pasteur Institute, Paris; Institut de Biologie de l'Ecole Normale Supérieure, Paris Hi-C is a chromosome conformation capture experimental method used to comprehensively detect chromatin interactions based on spatial proximity. During the last decade, it has become a prevalent approach in nuclear biology (gene regulation, genome spatial reorganization, genome rearrangements, etc), but also in a wide range of more distant fields such as medical biology, microbiology and environmental biology, genome assembly, biophysics and more recently in synthetic biology.

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Multi-omic Integration and Analysis of cBioPortal and TCGA data with MultiAssayExperiment

Multi-omic Integration and Analysis of cBioPortal and TCGA data with MultiAssayExperiment Author(s): Marcel Ramos,Levi Waldron,Ludwig Geistlinger Affiliation(s): CUNY School of Public Health, Roswell Park Comprehensive Cancer Center This workshop demonstrates how to leverage public multi-omics databases, such as the cBioPortal and The Cancer Genome Atlas (TCGA). Workshop participants are given an overview of the `cBioPortalData`, `curatedTCGAData`, `terraTCGAdata`, and `SingleCellMultiModal`, data packages. It introduces users to minimal data management with `MultiAssayExperiment` and `TCGAutils`, packages that organize and manage multi-omics datasets.

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mbQTL: An R/Bioconductor Package for Microbial Quantitative Trait Loci (QTL) Estimation

mbQTL: An R/Bioconductor Package for Microbial Quantitative Trait Loci (QTL) Estimation Author(s): Mercedeh Movassagh,Steven Schiff,Joseph Paulson Affiliation(s): Yale Medical School Motivation: Large studies have begun to collect both host gene expression and microbiome data at scale. This is an exciting time where we are able to estimate the genetic and genomic interactions between host and its microbial community. Coupling host gene expression in healthy and diseased individuals with microbial abundance data and taxonomy promises to shed light on many important problems, including disease development, progression and individual response to therapies.

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Isoformic: Isoform level biological interpretation from transcriptomic data

Isoformic: Isoform level biological interpretation from transcriptomic data Author(s): Izabela Mamede Conceição,Lucio Rezende Queiroz,Thomaz Luscher Dias,Julia Raspante Martins,Nayara Evelin de Toledo,Gloria Regina Franco Affiliation(s): Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil Social media: https://twitter.com/Izabela_M_C_A_C Transcriptome analysis is one of the most used methods in current biological sciences. There are multiple software that execute the gathering of the reference transcriptome, the alignment or pseudo-alignment, the quality check, the differential expression and the biological interpretation of all data.

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Giotto Suite: A multi-scale and technology-agnostic spatial omics analysis framework

Giotto Suite: A multi-scale and technology-agnostic spatial omics analysis framework Author(s): Jiaji George Chen,Joselyn Cristina Chavez-Fuentes,Matthew O'Brien,Irzam Sarfraz,Eddie Ruiz,Pratishtha Guckhool,Guo-Cheng Yuan,Ruben Dries Affiliation(s): Boston University Emerging spatial-omics technologies allow interrogation of the role of tissue architecture in specific biological processes, such as the establishment of cellular phenotypes or the inner workings of tissues. They can profile different molecular analytes such as chromatin accessibility, RNA, or protein, which represent different, but interconnected, layers of the cell regulatory network.

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extraChIPs: Detection and Visualisation of Differential ChIP-Seq Signal

extraChIPs: Detection and Visualisation of Differential ChIP-Seq Signal Author(s): Stevie M Pederson Affiliation(s): Telethon Kids Institute, Adelaide, Australia Designed as core infrastructure for the larger “Gene Regulatory Analysis using Variable Inputs” (GRAVI) workflow (https://github.com/steveped/GRAVI), extraChIPs enables differential Chip-Seq Signal analysis using either fixed width ranges or sliding windows, in a peak-dependent or peak-agnostic manner. Merging strategies for sliding window analysis build on those provided in csaw with the addition of the harmonic mean p-value for combining dependent test results.

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Epidemiology for Bioinformaticians

Epidemiology for Bioinformaticians Author(s): Chloe Anya Mirzayi,Levi Waldron Affiliation(s): CUNY Graduate School of Public Health and Health Policy, Institute for Implementation Science in Public Health, New York, NY, USA Concepts of causal inference in epidemiology have important ramifications for studies across bioinformatics and other fields of health research. In this workshop, we introduce basic concepts of epidemiology, study design, and causal inference for bioinformaticians. Emphasis is placed on addressing bias and confounding as common threats to assessing a causal pathway in a variety of study design types and when using common forms of analyses such as GWAS and survival analysis.

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Differential Expression Analysis using Limma and qsvaR

Differential Expression Analysis using Limma and qsvaR Author(s): Hedia Tnani,Joshua M. Stolz,Leonardo Collado-Torres,Louise A. Huuki-Myers,Leonardo Collado Torres Affiliation(s): Lieber Institute for Brain Development Social media: https://twitter.com/TnaniHedia RNA-seq and its expression levels are vulnerable to both environmental and technical influences. In previous studies that explored gene expression data from bulk RNA-seq of postmortem human brain samples, degradation was identified as a crucial and often overlooked issue, particularly when comparing patients to controls.

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demuxSNP: supervised demultiplexing of scRNAseq data using cell hashing and SNPs

demuxSNP: supervised demultiplexing of scRNAseq data using cell hashing and SNPs Author(s): Michael P Lynch,Laurent Gatto,Aedin C Culhane Affiliation(s): University of Limerick Social media: https://twitter.com/AedinCulhane Sequencing at a single-cell resolution allows unprecedented understanding of biologically relevant differences between individual cells compared to previous bulk methods. Though the cost of sequencing has dropped considerably, multiplexing, that is loading multiple biological samples into each sequencing lane, is widely used to further reduce costs.

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Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2 Author(s): Yingxin Lin,Yue Cao,Elijah Willie,Ellis Patrick,Jean Yang Affiliation(s): The University of Sydney The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies.

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Analyzing Spatially-Resolved Transcriptomics Data from Visium using spatialLIBD

Analyzing Spatially-Resolved Transcriptomics Data from Visium using spatialLIBD Author(s): Louise A. Huuki-Myers,Nicholas J Eagles,Leonardo Collado Torres Affiliation(s): Lieber Institute for Brain Development Social media: https://twitter.com/lahuuki Spatially-resolved transcriptomics is a powerful technique for understanding the organization of gene expression within tissues, providing new insights into function or disease and was named the Nature Methods of the year in 2020. In this workshop, we will demonstrate how to apply our Bioconductor package, spatialLIBD, to analyze spatially-resolved transcriptomics data (from Visium by 10x Genomics) and add spatial context to single cell RNA-seq or clinical gene datasets.

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A shiny application for single cell RNA-seq visualization

A shiny application for single cell RNA-seq visualization Author(s): Jianhong Ou Affiliation(s): Duke Regeneration Center, Duke University Social media: https://twitter.com/gaziou Single-cell RNA sequencing (scRNA-seq) is a powerful technique to study gene expression, cellular heterogeneity, and cell states within samples in single-cell level. The development of scRNA-seq shed light to address the knowledge gap about the cell types, cell interactions, and key genes involved in biological process and their dynamics. To precisely meet the publishing requirement, reduce the time of communication the bioinformatician with researchers, and increase the re-usability and reproducibility of scientific findings, multiple interactive visualization tools were developed to provide the researchers access to the details of the data.

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