Direct link to our Youtube channel: ITN CONTRA.

Interviews

• Jeffrey Townsend. Jeffrey Townsend’s Top Ten Advice for LifeSci PhD Students.

Final CONTRA conference, Andermatt 2022

• Sarah Aitken. Re-running cancer evolution in vivo to disentangle DNA damage and repair.
• Jose Bonet Giner. Direct detection of different sources of DNA damage through Nanopore sequencing.
• Nico Borgsmüller. Single-cell DNA-seq data reveals deviations from clock-like evolution in cancerous and healthy tissue.
• Yeman Brhane Hagos. Deep learning to decipher the multispectral immune landscape of follicular lymphoma.
• Mandi Chen. Detecting DNA Base Modifications on Nanopore Sequencing Data.
• Fabian Crespo. Estimation of growth rate and age of somatic clones from single-cell DNA-seq data.
• Aedin Culhane. Dimension Reduction & integration of single cell data.
• Arthur Dondi. Single-cell transcript isoform sequencing for precision oncology.
• Senbai Kang. SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data.
• Hanna Kranas. Genomic segments with different UV DNA damage repair dynamics.
• Núria López-Bigas. Studying the mutagenic and cancer promotion effect of chemotherapies.
• Magda Markowska. CONET: Copy number event tree model of evolutionary tumor history for single-cell data.
• Mathilde Meyenberg. Obesity Associated Cancers – A DNA Repair Perspective.
• Hrvoje Misetic. Identification and impact of clonal and subclonal driver alterations during cancer progression at single-patient resolution.
• Mohammadreza Mohaghegh Neyshaboouri. ToMExO: Simultaneous inference of mutual exclusivity and progression patterns among oncogenes.
• David Posada. Tumor phylogeography and phylodynamics from single-cells.
• María Rodrígues Martínez. AI-driven approaches for cancer personalized medicine.
• Monica Valecha. Benchmarking single-cell DNA somatic variant callers.

Spatial Models of Tumours, London (virtual event) 2020

• Binzhi Qian. The role of macrophages in cancer metastasis and therapy resistance.
• Marcel Gehrung. Early disease detection and the art of asking clinical questions.
• María Rodríguez Martínez. DEPICTION: extracting insight from deep learning models in computational biology.
• Robert Noble. Spatial models for forecasting tumour growth and survival.
• Stephanie Ling. Integrated Imaging and Analysis to understand the biological complexity of tissues.

Cancer Biology and Treatment, Cambridge 2019

• Peter Bankhead. QuPath: Open source bioimage analysis (not just) for pathology.
• Daniel Hübschmann. Homologous recombination, BRCAness and the TOP-ART trial.
• Ultan McDermott. Cancer Pharmacogenomics.
• Jacob Scott. Controlling disease evolution: models and experiments to understand timescales, trajectories and outcomes.
• Giovanni Tonon. From targeted therapies to cancer evolution: a new paradigm to tackle cancer resistance.

Models of Evolution Workshop, Barcelona 2019

Lectures

• Mar Albà. De novo proteins in evolution and disease.
• Niko Beerenwinkel. The intra-tumor phylogeny problem.
• Francesca Ciccarelli. Systems biology to study cancer evolution.
• Holger Heyn. Single Cell Genomics: From Sample to Cell Atlas.
• Núria López-Bigas. Understanding mutational processes to understand genome evolution.
• David Posada. Inferring phylogenies from tumor NGS data.
• Ricard Solé. The evolutionary dynamics of cancer.

Tutorial

• Andrew Roth. Part 1: Introduction to probabilistic modelling
• Andrew Roth. Part 2: Inferring clonal population structure from SNV data
• Andrew Roth. Part 3: Copy number variation
• Andrew Roth. Part 4: Phylogenetics
• Andrew Roth. Part 5: Advanced inference methods

Handling Single Cell Data Workshop, Warsaw 2018

Lectures

• Michaela Asp. Spatial Transcriptomics : Bridging histology and RNA sequencing.
• Alexandra Chloé‐Villani. Unraveling new insights into human immune system landscape one cell at the time.
• Anna Cuomo. Genetic analysis of differentiating cells using single-cell RNA-seq.
• Aleksandra Deczkowska. The power of ONE: Immunology in the age of single cell genomics.
• Peter Kharchenko. Transcriptional Dynamics with Single-Cell Data.
• Aleksandra Kolodziejczyk. Single cell RNA sequencing technologies.
• Jan Korbel. Single cell & big data approaches to understanding genome variation in normal individuals & disease.
• Jonas Maaskola. Charting Tissue Expression Anatomy by Spatial Transcriptome Deconvolution.
• Alexander Schoenhuth. Discovering somatic variants in single cells.

Tutorial

The CONTRA ESR students were given a practical tutorial in handling single-cell data. We recorded the presentations given by the tutors.

• Anna Cuomo & Ximena Ibarra. Part 1: DNA sequencing and experimental design.
• Anna Cuomo & Ximena Ibarra. Part 2: Next­‐generation sequencing data processing.
• Anna Cuomo & Ximena Ibarra. Part 3: Bulk and single­‐cell RNA­‐seq experiments and batch correction methods.
• Anna Cuomo & Ximena Ibarra. Part 4: Single-­cell RNA-­seq data analysis – QC and technical variance.
• Anna Cuomo & Ximena Ibarra. Part 5: Single­-cell RNA­-seq data analysis – clustering and dimensionality reduction.
• Anna Cuomo & Ximena Ibarra. Part 6: Single-­cell RNA-­seq data analysis – differential expression and dynamic processes.

Downloads: Course materials. Schedule. Slides.

ITN-CONTRA Kickoff, Bertinoro 2018

• Valentina Boeva. Discovery of two states of neuroblastoma cells via the analysis of super-enhancer landscape.
• Brian Cleary. Understanding biological systems with compressive statements.
• Carlo Maley. Evolutionary Approaches to the Big Questions in Cancer.
• Florian Markowetz. A continuum of genomes.
• Nicholas McGranahan. Lung Cancer Evolution and Immune Escape.
• Quaid Morris. Making sense of the “noise” in cancer data.
• Ben Raphael. Inferring Cellular Migrations in Metastatic Cancers.
• Johannes Reiter. Evolutionary dynamics of cancer initiation and metastases.
• Russell Schwartz. Reconstructing tumor evolution and progression in structurally variant cancer cells.
• Sohrab Shah. Determinants of cancer evolution.
• Ewa Szczurek. Introduction to probabilistic graphical models part 1.
• Ewa Szczurek. Introduction to probabilistic graphical models part 2.
• Jeffrey Townsend. Effect sizes of somatic mutations.
• Bartłomiej Wacław. Intra-tumour heterogeneity – implications for treatment.
• David Wedge. Cancer evolution: Copy number and metastatis.
• Benjamin Werner. Population genetics of intratumour heterogeneity.