Bioinformatics Webinars Series
Over the last years, our understanding of physiological phenomena like cell differentiation, or their aberrant counterpart, tumorigenesis, has been enhanced by major progress on data acquisition at different granularity levels, from the ultrastructure of molecules to the organization of cells and tissues. For instance, major efforts in high resolution cryo- Electron Microscopy (EM) to visualize single molecules in their native states allow to reveal the three-dimensional organization of macromolecular complexes. Similarly, the combination of molecular biology strategies with massive parallel sequencing interrogates protein-DNA interactions involved in transcription regulation and , DNA 3-dimensional organization. The combination of cell imaging with molecular biology labelling strategies and spatial proteomics is opening the window to a molecular characterization of the cellular context, which as a whole is responsible to define cell identity as well as tissue substructure organization.
At each of these levels, the amount of data collected as well as their complexity, requires sophisticated bioinformatics solutions. We need to distinguish meaningful biological readouts from technical noise on the one hand, and summarize and integrate them into a condensed output for enhanced comprehension on the other hand. Indeed, the transition of biology into a data science requests for major investments in algorithm developments. Pipe-lines need to be designed for the granularity level at which the data has been acquired, but also providing means for interconnecting data at different levels. As part of the following Bioinformatics webinar series, we aim at getting the analytical perspective provided by major scientist leading bioinformatics developments at different levels of granularity. We expect that this event will contribute to the training of the new generations of quantitative biologists.
Marco A. Mendoza-Parra & Kristine Schauer
Speakers at a glance
Click on pictures for details
iCube - France
IBENS - France
CBiB - France
INRIA - France
"Integration of heterogenous data in structural biology"
"Single cell Omics
"Spatial Omics in
an intracellular context"
EMBL - Germany
"Leveraging Biological Knowledge to Extract Disease Mechanisms from Spatial & Multi-omics data"
"Deep Learning application in
biological imaging segmentation"
"Intratumoral transcriptional heterogeneity in cancer"
"A.I. application to
Cancelled - rescheduling
"The rise of deep learning in image analysis and
its application for segmentation in
February 11, 2022 - 1pm CET - Zoom Webinar
Laura Cantini, PhD
Permanent Researcher - CNRS
PaRis Artificial Intelligence InstitutE
References of interest
"Multi-omics integration in cancer:
methodological developments and applications"
by Laura Cantini
Due to the advent of high-throughput technologies, high-dimensional “omics” data are produced at an increasing pace. In cancer biology, national and international consortia have profiled thousands of tumors at multiple molecular levels (“multi-omics”) allowing to gather a comprehensive molecular picture of this disease. Moreover, multi-omics profiling approaches are currently being transposed at single-cell resolution, further increasing the information accessible from cancer samples. The current main challenge is to design appropriate methods to integrate this wealth of information and translate it into actionable biological knowledge.
In this talk, I will discuss two maincomputational directions for multi-omics integration: (i) multiplex networks, to integrate a large range of interactions and (ii) joint Dimensionality Reduction (jDR), to extract biological knowledge simultaneously from multiple omics. First, I will present their application to bulk data and the biological results that we derived. Then I will discuss our ongoing research in single-cell.
March 11, 2022 - 1pm CET - Zoom Webinar
"Bayesian modeling of biomolecular structures"
by Michael Habeck
Bayesian inference is a principled approach to scientific data analysis that offers a unique framework for probabilistic modeling, parameter estimation, model comparison and uncertainty quantification.
I will start by explaining the Bayesian way to do data analysis in general terms and put some emphasis on computational challenges. I will then focus on applications in structural biology including the calculation of structures with hybrid data, chromosome structure modeling and 3D reconstruction from cryo-EM projection images.
April 15, 2022 - 1pm CET
"From SNVs in targeted sequencing data to patterns of subcellular spatial localization of molecules"
by Macha Nikolski
With the generalization of -omics data generation and in particular of next-generation sequencing (NGS) methodologies, the analysis of NGS datasets has become standard for forming and testing biomedical hypotheses. Definition of specific biomarkers for diagnosis, prognosis, and monitoring, is essential to distinguish subtypes in cancer to help define therapeutic choices.
In the first part of this talk we will discuss a tailored algorithmic method MICADo to discover Single Nucleotide Variations (SNVs) in PacBio targeted sequencing of patient cohorts with application to breast cancer. MICADo is based on De Bruijn graphs and allows an effective distinction between patient- specific alterations and alterations common to the cohort, which makes the results usable in a clinical context.
In the second part of this presentation, we will concentrate on subcellular spatial localization of molecules and present a full pipeline ranging from a deep learning-based mRNA spot detection in FISH images (DeepSpot) combined with a principled approach for quantitative spatial measurements of mRNA and proteins on a subcellular level (DypFISH). We will illustrate how these two methods enable uncovering fine-grained localization statistics such as peripheral enrichment and clustering at the leading edge of polarized cells or in the direction of a wound in the wound healing assay.
Human tumors are highly heterogeneous in their cell composition; specifically, they exhibit heterogeneity in transcriptional states of malignant cells, as has been recently discovered through single-cell RNA sequencing (scRNA-seq). Distinct states of malignant cells have been linked to variability in tumorigenic properties and resistance to anti-cancer treatment. Despite the fact that scRNA-seq data contain all necessary information to uncover shared transcriptional states of malignant cells in tumors, jointly analyzing cells from multiple cancer patients comes with its set of challenges including batch correction and accounting for patient-specific genetic background driving differences between gene expression vectors.
We propose CanSig, an easy-to-use approach designed to discover known and de novo shared signatures in cancer single cells. CanSig preprocesses, integrates and analyzes scRNA-seq data to provide new signatures of shared transcriptional states and links these states to known pathways. We show that CanSig successfully rediscovers ground truth pathways determining shared transcriptional states in two simulated and three experimental datasets; the latter spanning 135 patients and 72,000 cells.
We then illustrate CanSig’s investigative potential by discovering novel signatures in esophageal squamous cell carcinoma possibly linked to targeted patient treatment; we also point out a de novo signature in breast cancer predictive of patients’ survival. In the cancer types studied, we juxtapose copy number variation with discovered shared transcriptional states and uncover a genetic component predisposing cancer cells to activation of specific transcriptional programs.
In sum, CanSig, specifically developed to analyze shared transcriptional heterogeneity of malignant cells of different genetic backgrounds, can greatly facilitate the exploratory analysis of scRNA-seq cancer data and efficiently identify novel transcriptional signatures linked to known biological pathways.
June 10, 2022 - 1pm CET
"AI approaches to find molecules in a
[Final Title to come]
July 8, 2022 - 1pm CET
Julio Saez-Rodriguez, PhD
Team Leader - Professor
Faculty of Medicine of
Director of the Institute of Computational Biomedicine
Group Leader at the
EMBL- Heidelberg University