Viewing Several Interactive Plots with Plotly and Trelliscopejs: A Lipidomics Data Example Jeremy John Selva National University of Singapore Abstract In a typical lipidomics workflow, many quality checks are done to evaluate samples measured for a particular transition. This results in several quality control plots for each transition which are converted as pages in a pdf file. However, as technologies improved, many transitions can be measured in one sample at a fast rate.
Poster: Variation of gene expression in the liver of Nile tilapia at different time points of sub-chronic exposure to Benzo(a)pyrene
Variation of gene expression in the liver of Nile tilapia at different time points of sub-chronic exposure to Benzo(a)pyrene Nacira Anahi Albornoz-Abud,Rossana del Pilar Rodriguez-Canul,Reyna Cristina Colli-Dula Cinvestav del IPN Unidad Mérida Abstract Polycyclic aromatic hydrocarbons (PAHs) are pollutants of environmental concern because they are widely distributed in aquatic systems around the world. Although, benzo(a)pyrene (BaP) is a model PAH known for its carcinogenic effects in vertebrates, information on its molecular toxicity in aquatic organisms is still lacking.
Unraveling Immunogenomic Diversity in Single-Cell Data Ahmad Al Ajami,Katharina Imkeller University Hospital Frankfurt Abstract Immune molecules such as B and T cell receptors, human leukocyte antigens (HLAs), or killer Ig-like receptors (KIRs) are encoded in the genetically most diverse loci of the human genome. Many of these immune genes are hyperpolymorphic – showing high allelic diversity across human populations. In addition, typical immune molecules are polygenic, which means that multiple functionally similar genes encode the same protein subunit.
The transcriptional landscape of drug effects in CLL Caroline Lohoff,Junyan Lu Heidelberg University Hospital, Lu Group Abstract Despite extensive investigation of appropriate drugs for the treatment of CLL, the resulting transcriptional response remain incompletely understood. In this collaboration with the University Hospital Zurich and GeneCore we investigate the transcriptional landscape of drug effects in chronic lymphocytic leukemia (CLL) in a systematic and high-throughput manner. The data set consists of more than 1100 low depth 3‘-end RNAseq samples from more than 100 patients.
The current state of single-cell proteomics data analysis Christophe Vanderaa,Laurent Gatto UCLouvain Abstract Mass spectrometry-based single-cell proteomics is a fast growing field that enables quantitative proteomics at single-cell resolution. This is achieved thanks to technical improvements that dramatically increase the throughput, sensitivity and accuracy of the technology. However, little efforts have been invested in understanding single-cell proteomics data analysis. We have addressed this need with scp, an R/Bioconductor package that standardizes single-cell proteomics data analysis.
ProFaNA - Neighborhood analysis for prediction of gene function Bartosz Baranowski,Krzysztof Pawłowski Institute of Biochemistry and Biophysics, Polish Academy of Sciences Abstract Many genes in microbial genomes remain functionally uncharacterized. Understanding signaling and metabolic pathways involving such genes is essential for deeper understanding of microbial biology and also mechanisms of infectious diseases. It is well known that groups of genes neighboring in the genome are more likely to share similar biological functions than random pairs of genes.
Poster: PeptidoformViz is a shiny app for processing, visualising and analysing mass spectrometry based intensity data on peptidoform level
PeptidoformViz is a shiny app for processing, visualising and analysing mass spectrometry based intensity data on peptidoform level Nina Demeulemeester,Lien Provez,Laura Corveleyn,Bart Van Puyvelde,Lennart Martens,Maarten Dhaenens,Lieven Clement Ghent University - VIB Abstract Many histones are known to carry a plethora of post-translational modifications (PTMs)(1). Changes in these histone PTMs (hPTMs) have been linked to a variety of diseases (2). Nonetheless, the mapping of hPTMs, with the aid of mass spectrometry based proteomics (MS) has not been extensively described.
miaSim: a time series simulation R package for microbial ecology Yagmur Simsek,Yu Gao,Daniel Rios Garza,Karoline Faust,Leo M Lahti Department of Computing, University of Turku, Finland Abstract Hundreds to thousands of species interact in natural microbial communities. Computer simulations in microbiome ecology are therefore becoming increasingly important to understand the interactions between species. In this script, we introduce miaSim: a time series tool to simulate microbiome ecology, a new open-source, publicly available R package modelling microbial population dynamics in a repeatable, transparent, and scalable manner.
MetaboAnnotation: an R package simplifying metabolite annotation Andrea Vicini,Carolin Huber,Michael Witting,Johannes Rainer Institute for Biomedicine, Eurac Research, 39100 Bolzano, Italy. Abstract We present the R package MetaboAnnotation designed to assist the end-user in metabolite annotation. Specifically, MetaboAnnotation provides the high level functions matchValues() and matchSpectra() to perform MS1- and MS2-based annotation. The former involves matching of measured m/z values and/or retention times of LC–MS features against reference values; the latter a comparison of potentially generated experimental MS2 spectra against reference spectra, either from in-house or external libraries.
Means to visualize spatial patterns in host-associated microbiome data Ida Holopainen,Matti Ruuskanen,Leo M Lahti,Aki Havulinna Finnish Institute for Health and Welfare Abstract Host-associated microbial communities are affected by ecological processes, living environment, diet, and health status of the host organism. Because many of these factors display spatial patterns, they can also create spatial variation in the microbiome compositions between hosts. To date, there are only few studies that have investigated such spatial patterns in host-associated microbiomes.
LMWiRe: an R package for Linear Modeling of Wide Responses based on ASCA family of methods Michel Thiel,Nadia Benaiche,Manon Martin,Sébastien Franceschini,Robin Van Oirbeek,Bernadette Govaerts UCLouvain Abstract Many modern analytical methods are used to analyze samples issued from an experimental design; for example in medical, biological, chemical or agronomic fields. Those methods generate most of the time highly multivariate data like spectra or images, where the number of variables (descriptors) tends to be much larger than the number of experimental units.
Introducing the hermes package for analyzing and reporting RNA-seq data Daniel Sabanes Bove Roche Abstract The hermes package published on Bioconductor provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed RNA-seq data. Data can be imported from SummarizedExperiment as well as matrix objects and can be annotated from BioMart. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported.
hCoCena: Horizontal integration and analysis of transcriptomics datasets Marie Oestreich,Lisa Holsten,Shobhit Agrawal,Kilian Dahm,Philipp Koch,Han Jin,Matthias Becker,Thomas Ulas Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Abstract Transcriptome-based gene co-expression analysis has become a standard procedure for structured and contextualized understanding and comparison of different conditions and phenotypes. Since large study designs with a broad variety of conditions are costly and laborious, extensive comparisons are hindered when utilizing only a single data set. Thus, there is an increased need for tools that allow the integration of multiple transcriptomic data sets with subsequent joint analysis, which can provide a more systematic understanding of gene co-expression and co-functionality within and across conditions.
EMMA - Enrichment Methods Matter Federico Marini,Annekathrin Silvia Ludt University Medical Center Mainz Abstract Functional enrichment analysis, performed either via scripted analysis or with web-based tools, is one of the most frequently adopted steps in computational biology, especially when identifying the systems level mechanisms captured by high-dimensional molecular datasets. Recent work (Wijesooriya et al. 2022 - doi: 10.1371/journal.pcbi.1009935) showed that, despite their popularity, methodological issues (e.g. the use of inappropriate background for enrichment, or the lack of detail provided in the Materials and Methods section) might undermine the validity and reproducibility of many research endeavors using (incorrectly) such methods.
Differential transcript usage analysis for dynamic biological processes Jeroen Gilis,Koen Van den Berge,Lieven Clement Ghent University Abstract Trajectory inference has been instrumental for modelling dynamic changes in biological systems during processes like cell differentiation, tissue development and response to external stimuli. Downstream of trajectory inference, one key challenge is the identification of marker genes that are associated with the dynamic process at hand. In this context, our Bioconductor package tradeSeq is a highly flexible and scalable tool that allows for testing both within-lineage and between-lineage changes in gene expression.
Differential analysis of labelled MS-based proteomics data with msqrob2 Stijn Vandenbulcke Universiteit Gent Abstract Mass spectrometry (MS) enables direct and comprehensive quantification of the proteome in biological samples, gaining a better understanding of biological systems. The quantification of the protein abundances can be done either with label-free workflows or by labelling with TMT or iTRAQ, which allows users to effectively multiplex multiple samples in the same MS-run. We have adapted msqrob2, an R/Bioconductor tool for differential analysis of label-free MS-based proteomics data, towards the analysis of labelled MS-based proteomics data.
decoupleR: ensemble of computational methods to infer biological activities from omics data Pau Badia i Mompel Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant, Heidelberg 69120, Germany Abstract Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework.
Poster: CytoPipeline: building and visualizing automated pre-processing and quality control pipelines for flow cytometry data
CytoPipeline: building and visualizing automated pre-processing and quality control pipelines for flow cytometry data Philippe Hauchamps,Dan Lin,Laurent Gatto Computation Biology and Bioinformatics (CBIO) Unit, de Duve Institute, UCLouvain, Belgium Abstract With the increase of the dimensionality in conventional flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. Examples of such pipelines have been documented in the recent literature (e.
Comparison of two methods for deteting diffferential expression in a RNA-seq Tilapia study Reyna Cristina Colli-Dula,Nacira Anahi Albornoz-Abud Cinvestav del IPN Unidad Mérida Abstract Benzo(a)pyrene (BaP) is a hydrocarbon present in aquatic systems derived from various anthropogenic activities. This compound can be bioaccumulated in aquatic organisms of economic importance and because it is considered carcinogenic and mutagenic, it may present adverse effects on the development of reproductive organs. Therefore, its early detection is important at the molecular level in aquatic organisms using RNA-sequencing (RNA-seq) omic techniques, which have revolutionized the characterization of changes in their transcriptome.
Poster: Cancer signatures for reproducible gene expression analysis data: the computational way to achieve precision medicine
Cancer signatures for reproducible gene expression analysis data: the computational way to achieve precision medicine Stefania Pirrotta,Laura Masatti,Fabiola Pedrini,Chiara Romualdi,Enrica Calura University of Padova Abstract Cancer is a complex disease, characterized by extensive genomic aberrations with an evident impact on gene expression regulation and cell biological processes. Many studies and some clinical trials proposed gene expression signatures as a valuable tool for understanding cancer mechanisms and defining subtypes. Moreover, transcriptional signatures have the potential to show cancer activities while they are happening.
A corpus of standardized CITE-seq data Bernat Bramon Mora,Helen Lindsay,Raphael Gottardo Biomedical Data Science Center at the Lausanne University Hospital (CHUV) Abstract Cell type annotation is one of the central challenges in single-cell analyses, a necessary step for biological interpretation and downstream statistical analyses. While a lot of progress has been made to this date, current state-of-the-art techniques still suffer from major limitations, including lack of ground truth, reliance on limited reference datasets, and lack of standard annotations.
A Bioconductor workflow for dynamic spatial proteomics Lisa M Breckels,Oliver Crook,Laurent Gatto,Kathryn S Lilley University of Cambridge Abstract Spatial proteomics is the systematic study of proteins and their assignment to subcellular niches including organelles. The field has continued to grow in importance as many diseases result from protein mislocalisation. The knowledge of subcellular localisation of proteins is extremely desirable to biologists, as it can assist elucidation of a protein’s role within the cell, as proteins are spatially organised according to their function and specificity of their molecular interactions.