News, Events, Seminars ...

Upcoming: Daniel Incicau - Evaluation of cell type annotation methods in multiplexed imaging

Zurich Seminars in Bioinformatics

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract In the field of biomedical imaging, multiplexed imaging technologies have become indispensable for complex tissue analysis, enabling unprecedented depth of cellular and molecular understanding. This technology allows the spatial profiling of molecular markers within tissues at a single-cell resolution. However, the novelty of the technology requires specialised computational tools to extract biologically meaningful information. In recent years, numerous methods have emerged for each data analysis step in multiplexed imaging. Yet, it is still unclear which method is best and under what circumstances.

In this work, we focus on the cell type annotation step of data analysis, where we evaluate a selection of state-of-the-art supervised and unsupervised approaches. The aim is to establish a quantitative performance framework for these methods with corresponding guidelines. Furthermore, we examine the effects of different data normalisation and integration strategies in single-cell data derived from images. We also assess how lateral spillover compensation methods, such as REDSEA, influence downstream phenotyping. We found out that STELLAR is the best-performing supervised method leveraging spatially-resolved data. While unsupervised methods generally show comparable performance, PIXIE was able to classify all cell types and stands out due to its ease of use.

Although this evaluation only focuses on proteomics profiling using Imaging Mass Cytometry (IMC) data, its applicability extends to other highly multiplexed imaging technologies. Most importantly, we provide an extensible evaluation framework that can easily incorporate new methods and datasets in the future.

Upcoming: Open science: improving your research workflow to increase transparency and reproducibilitys

Two-day workshop for graduate students in Neuchatel

December 7-8 2023, University of Neuchâtel, Chemistry building, room GE14
Organizer: Dr Dominique Roche, Social Sciences and Humanities Research Council of Canada

Abstract This two-day workshop will teach graduate student how to adopt open science practices to promote transparency and reproducibility in research. The first day of the workshop will consist of 5 lectures by invited speakers on topics including the replication crisis in science and the benefits of open science practices, publication bias and measures to counter it, statistical misconceptions and best practices, common mistakes in study design and reporting, and open data and code. Continue reading

Siyuan Luo - Benchmarking computational methods for single-cell chromatin data analysis

Zurich Seminars in Bioinformatics

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices.

We benchmarked 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluated the performance of each method at different data processing stages. This comprehensive approach allowed us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection.

Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

Carino Gurjao - Genetic Analyses of Colorectal Cancer across Ancestries and Mutagenic Exposures

ETHZ special presentation hosted by Valentina Boeva

11:00 at ETHZ, Sonneggstrasse 3, 8092 Zürich

Abstract Colorectal cancer (CRC) has several established risk factors, including diet and microbiome. However, their mutagenic effect has not been observed directly in patients’ tumors and the individuals or ethnic groups who are most susceptible to diet-induced carcinogenesis are yet to be identified. In particular, CRC disproportionately affects African American (AA) patients who have worse clinical outcomes, but the molecular underpinnings are still poorly understood. We hypothesize that mutational signature analyses in CRC, coupled with epidemiologic, tumor molecular, micro-environmental, and patient germline data, can be linked to pre-diagnosis diet and specific germline alterations, which can further inform cancer prevention efforts. Continue reading

Frederik Philipona - Simulation of spatial transcriptomic data

Zurich Seminars in Bioinformatics

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract A parametric and interpretable method to simulate spatial transcriptomic data is proposed. The simulated data is used to compare domain recognition methods.

Navigating the global ocean microbiome through a web-based genome collection

Zurich Seminars in Bioinformatics - Samuel Miravet Verde (Sunagawa Lab ETHZ)

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract

High-throughput sequencing has empowered researchers to profile taxonomic, genomic, and functional compositions of ocean microbiomes through the reconstruction of metagenome-assembled genomes (MAGs) from environmental samples. As part of an environment that is marked by vast physicochemical gradients and immense phylogenetic and functional microbial diversity, the ocean microbiome offers ample opportunities to study the ecology and evolution of microbial communities as well as gene-encoded functions in the context of its natural environment. However, this information is currently scattered across the primary literature and sequence databases and/or difficult to access, hindering the systematic analysis of large-scale metagenomic datasets at global scale.

Investigation of the potential of Covariates for Multiphenotype Studies (CMS) to improve genetic risk prediction

Zurich Seminars in Bioinformatics - Anja Estermann

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Polygenic risk scores (PRSs) estimate the genetic risk of an individual to develop a certain disease or trait and find application in disease prevention and personalized medicine. These scores are calculated based on summary statistics results of genome-wide association studies (GWAS). Covariates for multiphenotype studies (CMS) is an algorithm that has been developed to increase the detection of associated genetic variants in GWAS by leveraging covariates measured on behalf of a primary outcome. Continue reading

SuperCellCyto: Enabling efficient analysis of large scale cytometry datasets

Zurich Seminars in Bioinformatics - Givanna Putri (WEHI)

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract The rapid advancements in cytometry technologies have enabled the quantification of up to 50 proteins across millions of cells at the single-cell resolution. The analysis of cytometry data necessitates the use of computational tools for tasks such as data integration, clustering, and dimensionality reduction. While numerous computational methods exist in the cytometry and single-cell RNA sequencing (scRNAseq) fields, many are hindered by extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, often prove inadequate as they risk excluding small, rare cell subsets.

To address this, we propose a practical strategy that builds on the SuperCell framework from the scRNAseq field. The supercell concept involves grouping single cells with highly similar transcriptomic profiles, and has been shown to be an effective unit of analysis for scRNAseq data.

We show that for cytometry datasets, there is no loss of information by grouping cells into supercells. Further, we demonstrate the effectiveness of our approach by conducting a series of downstream analyses on six publicly available cytometry datasets at the supercell level, and successfully replicating previous findings performed at the single cell level. We present a computationally efficient solution for transferring cell type labels from single-cell multiomics data which combines RNA with protein measurements, to a cytometry dataset, allowing for more precise cell type annotations.

Our SuperCellCyto R package and the associated analysis workflows are available on our GitHub repositories (github.com/phipsonlab/SuperCellCyto and phipsonlab.github.io/SuperCellCyto-analysis/).

splicekit: a comprehensive toolkit for splicing analysis from short-read RNA-seq

Zurich Seminars in Bioinformatics - Gregor Rot

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Splicing of RNA is a fundamental biological process. Dysregulation of splicing has been implicated in many human diseases and successfully exploited as a therapeutic target. Splicing analysis using short-read RNA-sequencing is a powerful technique to triage mechanism of action and safety profiles of drug candidates. There is, however, currently no comprehensive open-source software pipelines for such applications. Continue reading

Modeling the Tumor Microenvironment with Graph Concept Learning

Zurich Seminars in Bioinformatics - Santiago Castro Dau

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Heterogeneity is an emergent property of tumors, linked to cancer resistance and poor treatment outcomes ¹. Geometric deep learning using graph representations has emerged as a promising approach to investigate tumor heterogeneity. Still, these approaches suffer from interpretability and transferability limitations ². In this work, we propose a geometric deep-learning model with an interpretability framework to predict metadata from spatial datasets. Continue reading

Microbiome Signatures in Host - Spatial and Single Cell Transcriptomics

Zurich Seminars in Bioinformatics - Stine Anzböck

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract The tumor-associated microbiome is an integral part of the tumor microenvironment and holds great promise for a better understanding of carcinogenesis, response to therapy and early diagnosis of cancer. However, current methods limit the understanding of the spatial distribution of the microbiome in the tumor and their interaction with host cells. To this end, we have developed SpaceMicrobe, a novel computational framework to detect and profile the microbiome in 10X Visium Spatial Gene Expression data, a widely used commercially-available spatial transcriptomics technology. Continue reading

Variational Autoencoders Supporting Conditioning in Single Cell Transcriptomics and Their Consistency

Zurich Seminars in Bioinformatics - Eljas Röllin

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Prediction of cell response to perturbation is a key goal in computational biology. To this end, different Variational Autencoder (VAE) based models have been suggested in the past. We are interested in such models and their ability to recognize their own output. We argue that this is a desirable model property, and phrase it in the context of cycle consistency. Continue reading

Analysis of copy number variant heterogeneity in the hierarchical NCIt cancer classification system

Zurich Seminars in Bioinformatics - Ziying Yang

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Cancers are heterogeneous diseases with unifying features of abnormal and consuming cell growth, where the deregulation of normal cellular functions is caused by accumulative mutations. Due to these mutations, malignant tumors present with patterns of somatic genome variants on diverse levels of heterogeneity. Among the different mutation types, genomic copy number aberrations(CNA) have emerged as one of the most distinct classes. Continue reading

Detecting Single Cell Blasts in Acute Myeloid Leukaemia using an Auto-Encoder

Zurich Seminars in Bioinformatics - Alice Driessen

12:15 UZH Irchel Y13-K-05 and ZOOM Call

Abstract Acute myeloid leukaemia (AML) is a haematological cancer in the bone marrow, with accumulation and expansion of immature cells of the myeloid lineage. Unfortunately, almost half of paediatric AML patients relapse after standard treatment with chemotherapy or stem cell transplantation. Personalised medicine including immunotherapies have the potential to target chemotherapy resistant cells and achieve long-term remission. However, identifying suitable targets for AML therapy is hampered by high patient heterogeneity, complex disease evolution and challenging discrimination between aberrant and developing cells. Therefore, we aimed to build a single-cell cytometry AML map to identify malignant cells and place them along the developmental trajectory using data from 20 patients and three time points over the course of the disease. Continue reading

Multi-omics studies of cancer signalling and immune infiltration

Zurich Seminars in Bioinformatics - Pedro Beltrao

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours that we used to estimate activity changes in 218 kinases and 292 TFs. Continue reading

Inflammatory bowel disease at single-cell and sub-cellular spatial resolution

Zurich Seminars in Bioinformatics - Helena L. Cromwell

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract CosMx Spatially Molecular Imaging (SMI) (NanoString) is a recently developed technology that enables spatially resolved profiling of tissue at molecule-level resolution at an unprecedented scale (up to 1,000 RNA analytes). We applied scRNA-seq and CosMx SMI to investigate the molecular basis of Ulcerative colitis and Crohn’s disease: chronic inflammatory bowel diseases (IBD) that show a perplexing heterogeneity in manifestations and response to treatment. Continue reading

Searching in nucleotide archives at Petabase scale with MetaGraph

Zurich Seminars in Bioinformatics - Mikhail Karasikov

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract High-throughput sequencing data is continuously accumulating in massive archives such as the NCBI Sequence Read Archive (SRA), which currently contains over 50 Petabytes of sequences. However, despite recent advances, even such a basic operation as sequence search effectively remains intractable due to the lack of cost-efficient solutions. To address this problem and enable aggregated data analysis at Petabase scale, we developed MetaGraph, a tool for indexing very large collections of sequences in de Bruijn graphs. Continue reading

Nucleosome footprints in the cell-free DNA of cancer patients

Zurich Seminars in Bioinformatics - Zsolt Balázs

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Cell-free DNA (cfDNA) is free floating DNA released from cells into bodily fluids like blood. cfDNA sequencing is being increasingly utilized in cancer diagnosis and monitoring. While many applications focus on detecting cancer-specific mutations, cfDNA can potentially tell us much more about the disease. Nucleosome-protected regions are slow to degrade in the bloodstream, therefore, nucleosome occupancy in the cell of origin can be inferred from cfDNA fragmentation. Continue reading

Genome-wide study of variations in Plasmodium falciparum and their association with different malaria interventions in Tanzania

Zurich Seminars in Bioinformatics - Catherine Bakari Mvaa (Christian Nsanzabana group @ TPH)

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Malaria affects millions of people globally, and it remains one of the major public health problems occurring in most parts of Tanzania, causing thousands of deaths each year, especially among vulnerable persons in rural settings with poor health systems. Even though the burden has decreased due to the deployment of different interventions in recent years, all those gains are threatened by biological threats that largely affect the success of different control strategies. The parasite uses different mechanisms to escape diagnostic tools and drug treatment, challenging one of the key control strategies, based on prompt detection and case management. Sequencing technologies, especially Next Generation Sequencing (NGS) have played a major role in understanding parasite genomic variation including drug resistance, diagnostic resistance, and population structure. Continue reading

A journey into the nucleus: Lnc-ing paraspeckles, lncRNA, RNA processing and phase separation

NCCR RNA and Disease seminar

Archa Fox, The University of Western Australia

Host: Prof. Dr. Magdalini Polymenidou

The seminar will be preceded by a short presentation:

Unraveling genetic modifiers and cellular pathways of physiological TDP-43 oligomerization.”

Laura de Vos (Polymenidou group, UZH)

Comprehensive network prediction for any fully sequence genome in STRING database

Zurich Seminars in Bioinformatics - Damian Szklarczyk

Abstract Every day new genomes are sequenced and existing genomes are re-sequenced and re-annotated. In the new version of STRING the user can submit any fully sequenced genome for complete network and functional annotation. To do so. will require only minimal input from the user in a form of the proteome in a FASTA format and, if known, a taxonomical clade of the given genome. Continue reading

Towards a quantitative understanding of long-range transcriptional regulation

ETHZ seminar - Luca Giorgetti, FMI Basel

recently published Nature paper on “Nonlinear control of transcription through enhancer–promoter interactions”

CompbioZurich Website Redesign

Re-implementing the Site with Mkdocs

The compbiozurich.org website has been redesigned based on the Mkdocs framework & using the Material template system. Continue reading

Novel Minimal HDV-like Ribozymes

Zurich Seminars in Bioinformatics - Lukas Malfertheiner

Abstract The hepatitis delta virus (HDV) ribozyme catalyzes site-specific self-cleavage and was first discovered in the single-stranded circular RNA virus HDV. HDV-like ribozymes (DRZ) share the conserved nested double-pseudoknot structure motif. Through a bioinformatic search using an adapted minimal active DRZ motif, we discovered hundreds of novel minimal DRZ sequences in bacteriophage genomes associated with the human microbiome. A subclass of these hits was identified to occur in direct conjunction with viral tRNA genes, indicating that we have found a solely RNA-based factor that can site-specifically cleave tRNA 3'-trailers, thus making large protein enzymes redundant for this task.

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Supervised spatial inference of dissociated single-cell data with _SageNet_

Zurich Seminars in Bioinformatics - Elyas Heidari

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Spatially-resolved transcriptomics uncovers patterns of gene expression at supercellular, cellular, or subcellular resolution, providing insights into spatially variable cellular functions, diffusible morphogens, and cell-cell interactions. However, for practical reasons, multiplexed single cell RNA-sequencing remains the most widely used technology for profiling transcriptomes of single cells, especially in the context of large-scale anatomical atlassing. Continue reading

The resemblance is uncanny a.k.a how similar are cancer cell lines really to their origins?

Zurich Seminars in Bioinformatics - Rahel Paloots

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Cancer cell lines are good and inexpensive models to study disease mechanisms and identify possible new drugs, However, many times cancer cell lines have been found to be either misidentified or contaminated. Additionally, cancer cell lines only represent a small clonal population of the origin as well as accumulate some additional mutations during in vitro handling. Continue reading

Copy number variation data calibration towards intgerative analysis in cancer

Zurich Seminars in Bioinformatics - Hangjia Zhao

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Genomic instability is common in human cancer. As a form of genomic instability, copy number variations (CNV) play an important role in cancer development. Elucidating the relationship between CNV and cancer evolvement can improve the understanding of the pathogenetic mechanism of cancer.

Frequent co-regulation of splicing and polyadenylation by RNA-binding proteins inferred with MAPP

Zurich Seminars in Bioinformatics - Maciek Bak | Biozentrum, University of Basel | Swiss Institute of Bioinformatics

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

The processing of nascent pre-mRNA consists of many steps, where splicing and primary transcript polyadenylation play key roles in determining transcriptome and subsequently, proteome diversity. Several studies indicate that many RNA-binding proteins (RBPs) act both on splicing as well as 3’ end processing but the context of this multi-level regulation and the full spectrum of RBPs involved are yet to be discovered. To facilitate answering these questions we have developed a novel computational method to identify RBPs that could shape the pre-mRNA maturation.

An ion channel that allows you to see at night: the cryo-EM structure of the rod CNG channel opens up the hypothesis of mRNA editing on the CNGB1 sequence

Zurich Seminars in Bioinformatics - Jacopo Marino, Laboratory of Biomolecular Research, Paul Scherrer Institut

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract I will present the latest advances in cryo-electron microscopy and how this technique allows to solve protein structures at fast pace also of difficult protein targets, thus revolutionizing the field of structural biology and biomedicine. I will then focus on the recent developments on solving the structure of a ion channel that is located in rod photoreceptors of the retina, a protein complex that was discovered nearly 35 years ago and its structure has finally being revealed (article).

Taxonomic profiling of metagenomes from diverse environments with mOTUs3

Zurich Seminars in Bioinformatics - Hans-Joachim Ruschewey

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Taxonomic profiling is a crucial task in microbiome research that aims at detecting and quantifying the relative abundance of microbes in biological samples. However, in many environments, varying fractions of microbial species still lack a sequenced genome and remain unaccounted for during taxonomic profiling based on shotgun metagenomic data.

mVIRs: A tool to identify and locate inducible prophages in microbial genomes using NGS data

Zurich Seminars in Bioinformatics - Mirjam Zünd

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract An estimated 40 - 50% of mammalian gut bacteria carry a prophage in their genome. Prophages can be induced upon stress and produce phage particles to infect and kill bacterial populations and thereby directly modulate the composition of the gut microbiota. Despite advances in studying prophages using whole-genome sequencing, challenges in identifying and locating inducible prophages within their host have limited our understanding of phage-bacterial interactions.

CanIsoNet beta-version: A Database to Study the Functional Impact of Isoform Switching Events in Cancer

Zurich Seminars in Bioinformatics - Tülay Karakulak

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Alternative splicing, as an essential regulatory mechanism in normal mammalian cells, is frequently disturbed in cancer. Switches in the expression of alternative isoforms can alter protein interaction networks of associated genes giving rise to cancer progression and metastases. We have recently analyzed the pathogenic impact of switching events in 1209 cancer samples covering 27 different cancer types.

Structured metadata for genomic correlations in the Progenetix database

Zurich Seminars in Bioinformatics - Ziying Yang

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Abstract Enormous amounts of biomedical data have been and are being produced at an unprecedented rate by researchers all over the world. However, in order to enable reuse, there is an urgent need to understand the structure of datasets, the experimental conditions under which they were produced and the information that other investigators may need to make sense of the data.

Functional implications of Short tandem repeat (STR) variation in NGS data

Zurich Seminars in Bioinformatics - Max Verbiest

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

Short tandem repeats (STRs) are genomic elements that consist of consecutive repetitions of a 1-6 nucleotide motif. They are abundant in the human genome and are known to be mutational hotspots. Several cancer types are known to have a microsatellite instability high (MSI-H) subtype wherein the DNA mismatch repair system is defective, leading to hypermutation of STRs.

Identifying clones and quantifying diversity in repertoire sequencing data.

Zurich Seminars in Bioinformatics - Siyuan Luo

12:15 UZH Irchel Y55-l-06/08 and ZOOM Call

The adaptive immune system is remarkable for its ability to produce immunoglobulins that can specifically bind to a wide variety of antigens. During adaptive immune responses, activated B cells expand and undergo accelerated mutation of their B cell receptor (BCR), forming a clone of diversified cells that can be related back to a common ancestor.

Supervised Hierarchical Autoencoders for Multi-Omics Integration in Cancer Survival Models

Zurich Seminars in Bioinformatics - David Wissel

12:00 UZH Irchel Y55-l-06/08 and ZOOM Call (12:15)

Abstract With the increasing amount of high-throughput sequencing data becoming available, the proper integration of differently sized and heterogeneous molecular and clinical groups of variables has become crucial in cancer survival models.

Comparison of Probabilistic Cell Type Annotation Methods Applied to Immunological CITE-seq Data

Zurich Seminars in Bioinformatics - Ahmad Al Ajami

12:15 UZH Irchel Y34-K-01 and ZOOM Call

Abstract: A fundamental task in the analysis of single-cell RNA-sequencing data is cell type annotation. Current cell type annotation methods rely on either prior biological knowledge, which is not always available in a comprehensive and consistent manner, or well-annotated reference datasets, which are not always complete, i.e., not all cell types present in the target are represented in the reference data. We propose conducting cell type annotation in a probabilistic framework to fill the gap incomplete references create. Continue reading

A global exploration of factors contributing to rates of horizontal gene transfer

Zurich Seminars in Bioinformatics - Marija Dmitrijeva

12:15 UZH Irchel Y34-K-01 and ZOOM Call

Horizontal gene transfer - the exchange of genetic material between organisms through mechanisms other than reproduction, has been shown to play an important role in prokaryotic genome evolution. Previous studies have predominantly focused on recent horizontal gene transfer events, often limiting investigation to human-associated microorganisms. In this study, we aim to conduct a more global survey of the factors contributing to horizontal gene transfer. Continue reading

Introducing Progenetix Cell Line Beacon - A comprehensive cancer cell line variant knowledge resource

Zurich Seminars in Bioinformatics - Rahel Paloots

12:15 om ZOOM Call

Cancer cell lines are good models for studying the disease mechanisms and testing for possible drugs. For the cell lines to be accurate representations of the disease, they would need to be genetically highly similar to their primary neoplasias. Another issue when working with cell lines is the possible contamination or misidentification of the cell lines. To address both of these concerns, the genetics of both cancer cell lines and their origins will need to be evaluated. Cancers as well as cancer cell lines exhibit copy number variation (CNV) profiles that show regions in the chromosomes that have been deleted or amplified. Continue reading

A computational framework to analyse phenotypic heterogeneity in spatial single cell data

Zurich Seminars in Bioinformatics - Adriano Martinelli

Highly-multiplexed imaging technologies with single-cell resolution, such as imaging mass cytometry or multiplexed ion beam imaging, are revolutionising the field of biomedical research.

Stratifying sources of heterogeneity in single-cell whole-genome bisulfite sequencing datasets

Zurich Seminars in Bioinformatics - Emanuel Sonder

DNA methylation is on the base level a binary mark. However, when calculating average methylation rates across cells in bulk samples, certain regions of the genome exhibit intermediate methylation values, indicating heterogeneous methylation patterns.

A morphometric framework for the embryo-wide quantification of tissue organisation at single cell resolution

Zurich Seminars in Bioinformatics - Max Brambach

12:15 om ZOOM Call

Discovering copy number variation across multiple cancer types

Zurich Seminars in Bioinformatics - Qingyao Huang

12:15 om ZOOM Call

Abstract Genomic variations are direct cause of tumor formation and accomplice in its continuous evolution. While point mutations can be pinpointed to a targeted genetic element, copy number variations (CNVs) involve copy number gain or loss of a large DNA segment which often covers hundreds of genetic elements in one event. Although the vast majority of variations are not directly fucntionally cancer promoting, we observe consistency in CNV landscape within the same cancer types and corresponding increase in heterogeneity along with increased distinction in physiology and morphology. This implies that particular CNV may promote cancer type-specific progression. Continue reading

Analysis and Method development for High-Throughput DNA Methylation and Gene Expression with Applications to Colorectal Tumors

Zurich Seminars in Bioinformatics - Stephany Orjuela

12:15 om ZOOM Call

Degradation of human mRNA transcripts over time as an indicator of the time since deposition (TsD) in biological crime scene traces

Zurich Seminars in Bioinformatics - Giancarlo Russo

12:15 om ZOOM Call

Abstract Knowledge about the age of a stain or rather the time since deposition (TsD) would provide law enforcing authorities with valuable information for the prosecution of criminal offences. There is no reliable method for estimation of TsD available yet. The aim of this study is to gain further insight into the RNA degradation pattern of forensically relevant body fluids, and to find markers that allow to estimate the TsD interval. Stain samples from several body fluids were exposed to indoora nd outdoor conditions for up to 1.5 years. We were able to identify degradation as well as body fluid specific signatures in both indoor and outdoor samples. Continue reading

Leveraging single-cell RNA-seq with CITE-seq data

Zurich Seminars in Bioinformatics - Anthony Sonrel

12:15 om ZOOM Call

Recently, the ability to profile gene expression at the single-cell level has been expanded to include the measure of both RNA and protein levels from the same cells (Stoeckius et al., 2017). While RNA-based data has broadly proven sufficient for cell type detection and classification, its usage for determining drug target is still limiting: we still often rely on the assumption that RNA expression levels are representative of protein expression levels. Continue reading

Protein-protein interaction prediction using co-evolution based method

Zurich Seminars in Bioinformatics - Tao Fang

12:15 om ZOOM Call

It has been found that protein families consisting of orthologous proteins usually show divergent sequence similarities due to amino acid mutations in evolutionary history while structures of protein families are usually much more conserved. This phenomenon can be explained by two types of mutant positions in the protein sequence. One is conserved regions across the protein family and even a slight change in this region could result in critical loss of protein structure or function. Another is the so-called compensated mutation or coevolution region in protein. Continue reading

From Biomolecular Networks to Pathways: Tools for the Interpretation of Genome-Wide Experiments

Zurich Seminars in Bioinformatics - Annika Gable

12:15 om ZOOM Call

Functional enrichment analysis (also called gene set enrichment analysis or pathway analysis) is a widely-used method to interpret genome-wide experiments, especially in transcriptomics and proteomics. The method relies on previously annotated gene sets, such as pathways, cellular locations, protein families, disease genes, or other annotation terms. Continue reading

Phylogeny and Metabolic Potential of SAR324

Zurich Seminars in Bioinformatics - Lukas Malfertheiner

12:15 om ZOOM Call

The bacterial SAR324 cluster is ubiquitous in the ocean, especially around hydrothermal vents and in the deep sea, where it can account for up to 20% of the whole bacterial community. According to a new taxonomy generated using multiple universal protein-coding genes instead of the previously used 16s rRNA single gene marker, the former Deltaproteobacteria cluster SAR324 is since 2018 classified as its own phylum. Yet, very little is known about the phylogeny, metabolic potential and the influence of SAR324 on the different environments where they live. We downloaded all publicly available SAR324 genomes (65) from the genome taxonomy database, and also constructed 18 new genomes out of different oceanic metagenomic samples, including from under the Ross Ice Shelf in the Antarctica. Continue reading

Doublet identification and characterization in single-cell data

Zurich Seminars in Bioinformatics - Pierre-Luc Germain

12:15 om ZOOM Call

Doublets (i.e. two cells captured as a single cell) can form at a high frequency in single-cell sequencing studies (often 10-20%). They can appear as spurious 'new cell types' or distort trajectory and co-expression analyses. While experimental strategies can be used to mitigate this effect, they are insufficient and need to be complemented with doublet identification methods. Continue reading