Visualising Nanopore Methylation Data using NanoMethViz Shian Su Walter and Eliza Hall Institute of Medical Research Abstract Data produced by Oxford Nanopore direct DNA sequencing has been shown to contain information on DNA modifications, providing an effective new tool for high-throughput and high-resolution analysis of genome-wide DNA methylation patterns. I focus on 5-methylcytosine CpG DNA methylation, which plays an important role in the epigenetic regulation of mammalian gene expression. CpG methylation is vital in embryonic development, genomic imprinting, X-inactivation and repression of repetitive elements.
velociraptor, an R toolkit for single-cell velocity computation Kevin Christophe Rue-Albrecht,Charlotte Soneson,Michael B. Stadler University of Oxford Abstract RNA velocity has become a popular computational method to investigate dynamical signals in single-cell RNA-seq data sets and predict the future state of individual cells from the analysis of spliced and unspliced RNA-seq reads. While some of the most popular software for estimating RNA velocity are available exclusively as Python packages, the reticulate (CRAN) and basilisk (Bioconductor) packages allow users to run Python code and interact with Python data structures from within R sessions.
Short talk: Unlocking insights into cellular senescence through single cell transcriptomics of ageing mesenchymal stem cells
Unlocking insights into cellular senescence through single cell transcriptomics of ageing mesenchymal stem cells Atefeh Taherian Fard,Jessica Mar Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, QLD 4072, Australia Abstract Aging is a complex biological process. The heterogeneity of ageing phenotype is driven by the complex and dynamic nature through which several key molecular and cellular traits arise and interact. To understand the heterogeneity associated with aging, and the complications associated with age-related diseases, research into clinical treatments, including Mesenchymal Stem Cell (MSC) therapy, is underway.
Short talk: unCOVERApp: an interactive graphical application for clinical assessment of sequence coverage at the base-pair level
unCOVERApp: an interactive graphical application for clinical assessment of sequence coverage at the base-pair level Emanuela Iovino,Tommaso Pippucci,Marco Seri University of Bologna Abstract Motivation Next-generation sequencing is increasingly adopted in the clinical practice largely thanks to concurrent advancements in bioinformatic tools for variant detection and annotation. However, the need to assess sequencing quality at the base-pair level still poses challenges for diagnostic accuracy. One of the most popular quality parameters is the percentage of targeted bases characterized by low depth of coverage (DoC).
Ulisse: an R package to go beyond the boundaries of knowledge of molecular pathways. Alice Chiodi,Valentina Nale,Ettore Mosca Institute of Biomedical Technologies, National Research Council, via F.lli Cervi 93, Segrate, Milan, Italy Abstract Introduction. “Omics” assays typically yield relatively long gene lists whose interpretation is a major challenge for many researchers. Pathway analysis is a fundamental tool for explaining such lists. It provides mechanistic insights, translates gene-level findings into functional “blocks” that are easier to interpret, and helps reducing the biological heterogeneity at gene-level to common underlying mechanisms.
TriCycle: Transferable Representation and Inference of cell cycle Shijie Zheng,Genevieve Stein-O’Brien,Jared Slosberg,Jonathan Augustin,Loyal Goff,Kasper D. Hansen Johns Hopkins Bloomberg School of Public Health Abstract Background: The cell-cycle has been the subject of substantial interest in the single-cell expression era, both as a biological variable of interest and as a possible confounder for other comparisons of interest. Even though several computational methods using single-cell RNAseq data for inference of cell cycle have been proposed, some can only assign cells to a discretized stage ignoring the continuous nature of cell cycle, while other continuous assignment methods are only applicable to deep sequencing single cell data.
Short talk: treekoR: An automated framework for elucidating hierarchical relationships in high dimensional cytometry data
treekoR: An automated framework for elucidating hierarchical relationships in high dimensional cytometry data Adam Chan,Jean Yang,Ellis Patrick The University of Sydney Abstract High throughput single cell technologies which measure a high number of parameters for up to millions of cells holds the promise to discover novel biological relationships between different patient conditions with effective analytical workflows. Whether identifying cell clusters is done via unsupervised clustering, or through manually gating cell subsets, the proportions of these cell types relative to the whole sample are able to be analysed to understand characteristics driving different patient conditions.
TimiRGeN R package - novel tool for longitudinal microRNA-mRNA integration and analysis Krutik Patel Newcastle University, UK Abstract I present the TimiRGeN R/ Bioconductor package as a tool to analyse large longitudinal microRNA-mRNA expression datasets. The TimiRGeN R/ Bioconductor package is a new (late 2020 induction) tool which can integrate, perform functional analysis, and generate small intelligible networks from big longitudinal microRNA-mRNA datasets. There are several features of TimiRGeN which makes it versatile enough to become part of most microRNA-mRNA data analysis projects.
Short talk: The Bioconductor teaching committee: A collaborative effort to consolidate Bioconductor-focused training material and establish a community of trainers
The Bioconductor teaching committee: A collaborative effort to consolidate Bioconductor-focused training material and establish a community of trainers Charlotte Soneson,Laurent Gatto,Jenny Drnevich,Robert Castelo Friedrich Miescher Institute for Biomedical Research Abstract The Carpentries (https://carpentries.org) aims to teach foundational computational and data science skills to researchers using local instructors that have been specially trained in sound pedagogical methods. They offer a variety of introductory R modules, both on generic scripting and geared towards particular fields like ecology and genomics, but there are none currently involving Bioconductor-focused training material.
Spiky: standardizing cfMeDIP-seq data with spike-in controls Lauren Marie Harmon,Samantha Lea Wilson,Michael Hoffman,Shu Yi Shen,Justin M Burgener,Scott V Bratman,Daniel D. DeCarvalho,Tim Triche Van Andel Institute Abstract Cell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) is a sensitive and template-sparing approach to identify genomic regions with DNA methylation using cell-free DNA. cfMeDIP-seq is well-suited for liquid biopsies due to its low input DNA requirement (1ng) and cost-efficiency. Furthermore, cfMeDIP-seq conserves scarce circulating DNA by avoiding bisulfite conversion, and increases diagnostic yield in independent studies when compared to cfDNA variant analysis.
shortRNA: a flexible framework for the analysis of short RNA sequencing data Deepak Tanwar,Pierre-Luc Germain,Isabelle M. Mansuy Laboratory of Neuroepigenetics, Brain Research Institute, Medical Faculty of the University of Zurich, Zurich, Switzerland; Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Zurich Neuroscience Center, ETH Zurich and University of Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics PhD Training Network, Zurich, Switzerland; Statistical Bioinformatics Group, University of Zurich, Zurich, Switzerland
Short talk: scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data
scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data Malindrie Dharmaratne University of Queensland Abstract We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modelling gene expression read counts using generalized linear models. We model each gene independently under each treatment condition using the error distributions Poisson, Negative Binomial, Zero-inflated Poisson and Zero-inflated Negative Binomial with log link function and model-based normalization for differences in sequencing depth.
scDataviz: single cell dataviz and downstream analyses Kevin Blighe Clinical Bioinformatics Research Ltd. Abstract In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a ‘plug and play’ feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz.
SCArray – Large-scale single-cell RNA-seq data manipulation with GDS files Xiuwen Zheng Genomics Research Center, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL 60064 Abstract The technology development and decreasing costs of single-cell RNA-seq are leading to larger and larger numbers of cells assayed per experiment, and the scalability leveraging on-disk data processing remains an important issue to address. Here I introduce a new Bioconductor package SCArray, and it provides large-scale single-cell RNA-seq data manipulation using Genomic Data Structure (GDS) files.
Short talk: scanMiR: a versatile toolkit for scanning microRNA binding sites and predicting miRNA-mediated transcript repression based on a biochemical model
scanMiR: a versatile toolkit for scanning microRNA binding sites and predicting miRNA-mediated transcript repression based on a biochemical model Michael Soutschek,Fridolin Gross,Gerhard Schratt,Pierre-Luc Germain ETH Zürich Abstract Despite the importance of miRNAs in regulating a broad variety of phenomena, there is a relative paucity of R-based tools for predicting and handling their binding sites. A recent study showed improved miRNA target prediction using a biochemical model combined with empirically-derived affinity predictions across 12mer sequences (McGeary, Lin et al.
Rsubread: mapping and quantification of RNA-seq data Wei Shi Olivia Newton-John Cancer Research Institute, Melbourne, Australia Abstract Rsubread is a popular Bioconductor package that was developed for mapping RNA-seq reads and then counting the reads to genomic features including genes and exons. It also includes a recently developed new function, called CellCounts, for quantifying scRNA-seq data generated by the 10X Chromium platform. In this talk, I will describe the algorithms implemented in the mapping and counting functions in Rsubread and present the results of comparing to competing tools, including the CellRanger program developed by 10X for the quantification of their single-cell data.
rRbiMs, r tools for Reconstructing bin Metabolisms. Mirna Vazquez Rosas Landa,Valerie de Anda Torres,Sahil Shan,Brett Baker The University of Texas at Austin Abstract Although microorganisms play a crucial role in the biogeochemical cycles and ecosystems, most of their diversity is still unknown. Understanding the metabolic potential of novel microbial genomes is an essential step to access this diversity. Most annotation pipelines use predefined databases to add the functional annotation. However, they do not allow the user to add metadata information, making it challenging to explore the metabolism based on a specific scientific question.
Short talk: RnaSeqSampleSize: Real data based sample size estimation for RNA-Seq with complex design
RnaSeqSampleSize: Real data based sample size estimation for RNA-Seq with complex design Shilin Zhao,Yu Shyr Vanderbilt University Medical Center Abstract One of the most important components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. We developed a sample size and power estimation package named RnaSeqSampleSize, which utilized the genes expression patterns in real RNA-seq data with similar conditions to provide a more accurate and reliable estimation. The package was published two years ago and widely used by investigators in different areas.
Short talk: RadioGx: a Package for Integrative Analysis of Cellular Features and Radiosensitivity in Cancer
RadioGx: a Package for Integrative Analysis of Cellular Features and Radiosensitivity in Cancer Ian Smith,Petr Smirnov,Benjamin Haibe-Kains University Health Network, University of Toronto Abstract While radiation therapy is an integral method for cancer treatment, clinical choices are not currently informed by the genetic and molecular profile of a patient’s tumour. Though it has been shown that genetic features implicate variability, the exact relationship between these features and radiosensitivity is poorly understood.
Quantitative multi-omic data analysis and integration with the COSMOS R package Aurelien Dugourd,Katharina Zirngibl,Attila Gabor Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany Abstract Multi-omic data sets are a combination of snapshots of different molecular layers of the cell. While they can give comprehensive information into the biological system under study, their analysis and interpretation remains challenging. Each omic layer can be exploited to give specific insights, such as transcription factor activity form transcriptomics and kinase activity from phosphoproteomics (using Omnipath(Türei, Korcsmáros, and Saez-Rodriguez 2016) and dorothea(Garcia-Alonso et al.
proActiv: Estimating promoter activity from RNA-seq data Joseph Lee,Deniz Demircioğlu,Jonathan Goeke National University of Singapore, Genome Institute of Singapore Abstract Most human protein-coding genes have multiple promoters that control the expression of distinct isoforms. The use of alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have also been found to be important in a wide number of cell types and diseases, and thus the choice of promoter is as important as its level of transcriptional activity.
POMA: User-friendly Workflow for Metabolomics and Proteomics Data Analysis Pol Castellano Escuder,Cristina Andrés-Lacueva,Alex Sánchez-Pla University of Barcelona Abstract Mass spectrometry, like other high-throughput technologies, usually faces a data mining challenge to provide an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the hard points and it’s critical in the subsequent biological interpretation of the results. Due to this fact combined with the computational programming skills needed for this type of analysis, several bioinformatic tools have emerged to simplify mass spectrometry data analysis.
Short talk: PoDCall - Positive droplet calling and normalization of droplet digital PCR methylation data
PoDCall - Positive droplet calling and normalization of droplet digital PCR methylation data Marine Jeanmougin,Hans Petter Brodal,Heidi Pharo,Guro Elisabeth Lind Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital Abstract Droplet digital PCR (ddPCR) is an attractive technology for analyses of DNA methylation. ddPCR consists in partitioning samples into droplets, each undergoing its own PCR amplification. At end-point reactions, the droplets containing the fluorescent PCR target molecule are scored as (i) “positive”, and used to estimate the target concentration in the sample from binomial Poisson statistics, or (ii) “negative”, if they don’t contain the target of interest.
Short talk: PDATK: an R package for molecular classification and survival prediction in pancreatic ductal adenocarcinoma
PDATK: an R package for molecular classification and survival prediction in pancreatic ductal adenocarcinoma Christopher Bernard Eeles,Heewon Seo,Anthony Mammoliti,Benjamin Haibe-Kains Princess Margaret Cancer Research Center Abstract Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond.
Short talk: ORCESTRA: Web application for orchestrating and sharing large multimodal data for transparent and reproducible research
ORCESTRA: Web application for orchestrating and sharing large multimodal data for transparent and reproducible research Anthony Mammoliti,Minoru Nakano University Health Network Abstract Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon.
Short talk: Omics Playground: A user-friendly and interactive self-service bioinformatics platform for the in-depth analysis, visualisation and interpretation of transcriptomics and proteomics data
Omics Playground: A user-friendly and interactive self-service bioinformatics platform for the in-depth analysis, visualisation and interpretation of transcriptomics and proteomics data Murodzhon Akhmedov,Axel Martinelli,Ivo Murodzhon BigOmics Analytics Abstract Omics Playground is a user-friendly and interactive self-service bioinformatics platform for the in-depth analysis, visualisation and interpretation of transcriptomics and proteomics data. It is implemented in R using the Shiny web application framework and provides access to a large number of bioconductor modules that include data quality checks, clustering analysis, standard pairwise comparisons, gene set enrichment analysis and biomarker discovery among others.
MISTy: Multiview Intercellular SpaTial modeling framework Jovan Tanevski,Attila Gabor,Ricardo Omar Ramirez Flores,Denis Schapiro,Julio Saez-Rodriguez Heidelberg University & Heidelberg University Hospital Abstract The proliferation of different technologies for measuring rich spatial omics requires flexible and scalable tools for their analysis. We present MISTy a package for knowledge discovery and analysis of highly multiplexed spatial omics data by explainable multi-view machine learning. It can be readily applied to samples containing up to millions of spatial units and thousands of markers.
Microbiome data science in the SummarizedExperiment universe Felix G.M. Ernst,Tuomas Borman,Sudarshan Shetty,Ruizhu Huang,Domenick James Braccia,Hector Bravo,Leo M Lahti University of Turku Abstract Recent developments in Bioconductor classes and methods have opened up new opportunities for microbiome data science. Contemporary microbiome research needs support for hierarchies in the sample and phylogenetic feature spaces, for integrating multiple data types, for analysing spatio-temporal variation, and for general performance optimization. Whereas many promising techniques for such purposes are readily available through the Bioconductor SummarizedExperiment universe, they have been largely underutilized in microbiome data science.
Microbial community diversity and network analysis Rui Guan,Ruben Garrido Oter Max Planck Institute for Plant Breeding Research Abstract With the help of rapidly developing sequencing technologies, an increasing number of microbiome datasets have been generated and analysed. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members and limits the study of community dynamics. To better understand the principles that govern the establishment of these microbial communities, we developed a framework for microbial community diversity analysis based on higher-order features.
Short talk: margheRita: an R package for analyzing the entire workflow of mass spectrometry-based metabolic profiles.
margheRita: an R package for analyzing the entire workflow of mass spectrometry-based metabolic profiles. Maria Ulaszewska,Edoardo Bellini,Denise Drago,Valeria Mannella,Marco Morelli,Annapaola Andolfo,Ettore Mosca National Research Council, Institute of Biomedical Technologies, Segrate (Milan), Italy Abstract Mass spectrometry-based metabolic profiling in circulating biofluids and tissues is a promising approach to identify biomarkers for disease prediction, progression and prognosis. Besides, metabolomics is a powerful methodology, which can be applied to personalized medicine for drug development or for biomarker discovery in public healthcare.
Managing and running conda packages through R with the Herper package Matt Paul,Doug Barrows,Tom Carroll The Rockefeller University Abstract Many tools for data analysis are not available in R, but are present in public repositories like conda. In this workshop we will demonstrate the Herper package and its comprehensive set of functions to interact with the conda package management system. With Herper users can install, manage and run conda packages from the comfort of their R session.
Large-scale analyses of biological sequence motifs with the universalmotif package Benjamin JM Tremblay Centre for Research in Agricultural Genomics (CRAG), CSIC‐IRTA‐UAB‐UB, Spain Abstract Motifs are a common way of representing patterns in biological sequences, such as transcription factor binding sites or specific protein active sites. Predicting the presence or absence of these patterns within sequences is a crucial step in understanding their regulation or function. Additionally, determining the similarity or distance between motifs can help in identifying duplicate motifs and comparing motif evolution with function.
Short talk: Introducing idpr: A Package for Profiling and Analyzing Intrinsically Disordered Proteins in R.
Introducing idpr: A Package for Profiling and Analyzing Intrinsically Disordered Proteins in R. William Michael McFadden,Judith L. Yanowitz Magee-Womens Research Institute, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, PA 15213, USA Abstract Intrinsically Disordered Proteins (IDPs) are proteins or protein-domains that do not have a single native structure but rather are dynamic peptides that can adopt multiple conformations. The field of “unstructured biology” has gained prominence over the last few decades as it has become apparent that proteins experiencing intrinsic disorder comprise substantial portions of proteomes.
iCLIP data analysis: Defining binding sites, an R package Mirko Brüggemann Goethe University Frankfurt Abstract Most cellular processes are regulated by RNA-binding proteins (RBPs). Knowledge on their exact positioning can be obtained from individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) experiments. In a recent publication we described a complete analysis workflow to detect RBP binding sites from iCLIP data. The workflow covers all essential steps, from quality control of sequencing reads, different peak calling options, to the downstream analysis and definition of binding sites.
Short talk: GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases
GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases Sehyun Oh,Ludwig Geistlinger,Marcel Ramos,Vincent James Carey,Casey Greene,Levi Waldron,Sean Davis The City University of New York Abstract PURPOSE: Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. Existing methods for leveraging these public resources have focused on the reanalysis of existing data or analysis of new datasets independently. We present a novel approach to interpreting new transcriptomic datasets by near-instantaneous comparison to public archives without high-performance computing requirements.
Short talk: Generating genomic null ranges via block bootstrap for overlap statistics across a range of effect sizes
Generating genomic null ranges via block bootstrap for overlap statistics across a range of effect sizes Wancen Mu,Eric Scott Davis,Mikhail Dozmorov,Douglas Howard Phanstiel,Stuart Lee,Michael I Love University of North Carolina, Chapel Hill Abstract Introduction: In some genomic analyses, it is of interest to compare the rate of overlaps between two genomic feature sets, e.g. genes and peaks satisfying a significance and effect size threshold for differential expression (DE), binding, or accessibility.
Feature selection by replicate reproducibility and non-redundancy Tümay Capraz,Wolfgang Huber EMBL, Heidelberg, Genome Biology Unit Abstract A fundamental first step in many analyses of high-dimensional data is dimension reduction. Different scientific or domain-specific objectives necessitate different choices of dimension reduction methods, and indeed there is a plethora of methods. Feature selection is one approach to dimension reduction whose strengths include interpretability, conceptual simplicity, transferability and modularity (if additional data are to be acquired, it suffices to do so with the reduced set of features).
ExperimentSubset: An R package to manage subsets of Bioconductor Experiment objects Irzam Sarfraz,Muhammad Asif,Joshua D. Campbell National Textile University, Faisalabad, Pakistan & Boston University School of Medicine, Boston, MA, USA Abstract R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing.
Short talk: EaSIeR: predicting immune response using quantitative descriptors of the tumor microenvironment extracted from RNA-seq data
EaSIeR: predicting immune response using quantitative descriptors of the tumor microenvironment extracted from RNA-seq data Óscar Lapuente-Santana,Federico Marini,Arsenij Ustjanzew,Francesca Finotello,Federica Eduati Eindhoven University of Technology Abstract Identification of biomarkers of immune response in the tumor microenvironment for prediction of patients’ response to immune checkpoint inhibitors is a major challenge in immuno-oncology. Tumors are complex systems, and understanding immune response in the tumor microenvironment requires holistic strategies. We have developed a user-friendly R package to Estimate Systems Immune Response (EaSIeR), which provides a systems-level characterization of anti-tumor immune responses.
Short talk: Compartmap: Direct inference of higher-order chromatin structure in individual cells from single-cell RNA-seq & single-cell ATAC-seq
Compartmap: Direct inference of higher-order chromatin structure in individual cells from single-cell RNA-seq & single-cell ATAC-seq Benjamin K Johnson,Jean-Philippe Fortin,Kasper Daniel Hansen,Hui Shen,Tim Triche Van Andel Institute Abstract Single-cell profiling of higher-order chromatin structure remains a challenge due to cost, throughput, and resolution. We introduce compartmap as a method to reconstruct higher-order chromatin domains in individual cells, inferred from single-cell transcriptomic and epigenomic assays. In multiple cell lines and primary human samples, compartmap infers higher-order chromatin structure at least as well as chromatin capture or proximity ligation based methods, while distinguishing clinically relevant structural alterations in single cells.
CLIPflexR: a generic R package for CLIP analysis Kathryn Rozen-Gagnon The Rockefeller University Abstract By now, transcriptome-wide maps of RNA binding protein (RBP) target sites or RNA modifications are routinely generated via crosslinking immunoprecipitation followed by high-throughput sequencing (CLIP-seq). While CLIP-seq is a common approach to understand post-transcriptional RNA networks, researchers still face considerable obstacles in subsequent bioinformatic analyses. First, computational set-ups are laborious due to disparate workflows requiring multiple dependencies.
BiocSwirl: Interactive R Tutorials for Bioinformatics Lisa N Cao,Julia Philipp,Matt Moss,Almas Khan,Jasdeep Singh,Mariam Arab,Sourav Singh Simon Fraser University Abstract Bioinformatics has grown in adoption in many non-technical fields, advancing so rapidly that traditional bench scientists are finding it challenging to keep up with gold standard workflows. Complex bioinformatics data analyses become difficult to retain when learned using traditional teaching methods or static formats, which are often not updated or available for feedback.
Short talk: Bioconductor framework for consistent annotation of hyperpolymorphic HLA genes in human populations
Bioconductor framework for consistent annotation of hyperpolymorphic HLA genes in human populations Katharina Imkeller,Wolfgang Huber European Molecular Biology Laboratory Heidelberg Abstract MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. This process, also known as MHC-dependent antigen presentation, is essential for the coordination of adaptive immune responses. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus on chromosome 6.
BentoBox: A New Paradigm for Multifigure, Coordinate-Based Plotting in R Nicole Kramer,Eric Davis,Craig Wenger,Sarah Parker,Douglas Phanstiel University of North Carolina at Chapel Hill Abstract R is unparalleled in its ability to transform raw data into a wide array of beautiful graphics, all within the same environment. However, when it comes to complex, multi-paneled plots, users rely on 3rd party graphic design software to arrange and customize plots. Here we present BentoBox, a coordinate-based genomic data visualization package that will revolutionize multi-figure plotting in R.
Short talk: BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty
BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty Simone Tiberi,Mark D Robinson University of Zurich Abstract Alternative splicing plays a fundamental role in the biodiversity of proteins as it allows a single gene to generate several transcripts and, hence, to code for multiple proteins. However, variations in splicing patterns can be involved in diseases. When investigating differential splicing (DS) between conditions, typically healthy vs disease, scientists are increasingly focusing on differential transcript usage (DTU), i.
Bambu - Context-Aware Quantification of Transcript Expression with Long Read RNA-Seq Andre Sim,Ying Chen,Jonathan Goeke Genome Institute of Singapore, A*STAR Abstract Here we present bambu, a computational method for context aware quantification of transcript expression from long read RNA-Seq data. Bambu utilizes two modules: (1) generates a set of curated annotations across all samples of interest, (2) then quantifies isoform expression with an expectation maximisation algorithm that estimates full-length and partial-length read support per transcript.
atena: analysis of transposable elements in R and Bioconductor Beatriz Calvo-Serra,Robert Castelo Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain Abstract Transposable elements (TEs) are DNA sequences that can mobilize within the genome either through a DNA intermediate, and hence those are known as class II TEs or DNA transposons, or through an RNA intermediate, which are known as class I TEs or retrotransposons, as in the case of endogenous retroviruses (ERVs).
A graphical model for single-cell RNA-seq data Davide Risso University of Padova Abstract Recent technological advances in molecular biology allow the sequencing of RNA from individual cells (single-cell RNA-seq). Typically, the genes whose expressions are differential between cell states or across experimental conditions are identified with univariate (gene-wise) models. However, it may be beneficial to explicitly account for gene dependencies in multivariate statistical models. In this talk, I will introduce a graphical model for single-cell RNA-seq and show how to use it to explore the dynamics of transcription factors in development.