DECIPHERING GENE CONTROL

Welcome to the Laboratory of Computational Biology. Our lab is part of the KU Leuven Center for Human Genetics and the VIB Center for Brain and Disease Research. We are interested in decoding the genomic regulatory code and understanding how genomic regulatory programs drive dynamic changes in cellular states, both in normal and disease processes. Transcriptional states emerge from complex gene regulatory networks. The nodes in these networks are cis-regulatory regions such as enhancers and promoters, where usually multiple transcription factors bind to regulate the expression of their target genes.

Wet lab

We apply high-throughput technologies to decipher enhancer logic and map gene regulatory networks, such as RNA-seq for transcriptomics and ATAC-seq and ChIP-seq for epigenomic profiling. To test the activities of promoters and enhancers we use massively parallel enhancer-reporter assays. Finally, to map high-resolution landscapes of possible cellular states we use single-cell transcriptomics and single-cell epigenomics. Our favorite model systems include Drosophila (the brain and the eye-antennal imaginal disc) as well as human cancer cells (short-term cultures, cell lines, primary cells, xenografts, and organ-on-chip).

Dry lab

We use bioinformatics methods for network inference and computational modeling of enhancers, such as machine learning and advanced motif discovery. Some of the bioinformatics methods we have developed and made available to the community include TOUCAN, ENDEAVOUR, iRegulon, i-cisTarget, mu-cisTarget, and SCENIC.

Tech lab

We develop microfluidics chips, including droplet microfluidics for single-cell assays. We also develop microfluidic devices to analyse 3D tumoroids (organ-on-chip) and single-cell migration, in combination with lens-free imaging.

RESEARCH

Research in our lab is focusing on gene and genome regulation, with applications in neuroscience (Drosophila melanogaster) and cancer.

Enhancer modeling

We combine machine learning with epigenome profiling to decode enhancer logic. To test enhancers we developed a massively parallel enhancer-reporter assay, called CHEQ-seq. Our enhancer modeling focuses on mammalian TFs, such as TP53, SOX10/SOX9, GRHL1/2/3, AP-1, and TEADs; as well as on Drosophila TFs involved in eye development (e.g. Glass, Optix, sine oculis), epithelial development (Grainyhead), and tumour development (AP-1, STAT92E, and Scalloped).

cis-Regulatory variation

  • cis-regulatory variation is a major driver of phenotypic diversity and is associated to many diseases. By comparing chromatin accessibility across Drosophila inbred lines we aim to further our understanding of CRM divergence and plasticity, and the consequential divergence of gene expression and regulatory networks
  • Similar techniques are applied to cancer genomes, where we sift through non-coding mutations to identify cis-regulatory driver mutations that have an impact on enhancer function and/or perturb the normal gene regulatory network in a cell.

Evolution of cis-regulation

By comparing transcriptomes, chromatin state and cis-regulatory modules across species, we learn about enhancer logic and the evolution of gene regulatory networks. We use RNA-seq, FAIRE-seq, and ATAC-seq across Drosophila species, alongside Ornstein-Uhlenbeck models to connect CRM evolution with variation in chromatin accessibility. We have also studied the evolution of epidermal and metabolic GRNs between Drosophila and Daphnia.

Melanoma phenotype switching

We are interested in deciphering regulatory programs of transcriptional state switches in mammalian systems, including human and mouse. To study the cis-regulatory code in mammalian genomes we mainly use cancer cells as model system. During cancer progression, gene expression profiles can change, causing regulatory heterogeneity in tumors. This heterogeneity has an important impact on therapy response, since some cell states may be more or less vulnerable to a particular drug therapy.

Fly brain & ageing

We study neuronal and glial cell types in the ageing Drosophila brain using single-cell RNA-seq, and compare normal cell states with disease mutations involved in Parkinson’s and Alzheimer’s disease.

Fly eye & cancer

The eye-antennal disc is a classical model system to study cellular differentiation. We use this system to unravel new genomic regulatory “recipes” that control cell fate decisions, such as photoreceptor specification and differentiation. We also perturb this system using irradiation, transcription factor perturbations, and RasV12-driven malignant transformation, to study cancer-related transcriptional changes, controlled by JNK, EGFR, and Hippo signaling pathways.

AI & MACHINE LEARNING

Data-driven research in our lab is powered by machine learning and artificial intelligence (AI) to help us guide and understand more about biological systems and processes. Here is a non-exhaustive list what the lab has been and is currently working on:

Single-cell gene regulation

Single-cell transcriptomics (scRNA-seq) and single-cell epigenomics (scATAC-seq) data revolutionize the field of regulatory genomics. We combine new computational strategies (e.g., SCENIC, cisTopic) with state-of-the-art single-cell measurements (Drop-seq, 10X, InDrops, SeqWell) to decipher cis-regulatory “programs”, to reverse engineer gene regulatory networks, and to better define cell types and cell state transitions.

Single-cell systems biology

We develop new computational approaches that exploit single-cell technologies to link genome variation with changes in epigenome, transcriptome, proteome, and phenome. We apply this to human melanoma (e.g., phenotype switching), to the mouse liver, to the developing Drosophila eye and to ageing/neurodegeneration in the Drosophila brain. See also our collaborations.

Gene regulation bioinformatics

We develop new bioinformatics tools for motif and CRM detection, and for gene regulatory network inference, such as i-cisTarget, iRegulon, and TOUCAN. We also maintain a large collection of curated position weight matrices (currently > 20.000). We exploit single-cell RNA-seq and single-cell ATAC-seq data to improve the identification of GRNs and enhancers, with our tools SCENIC and cisTopic.

SOFTWARE

SCENIC+

SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data. This software is part of SCENIC Suite.

BioRXiv Preprint Github Read the Docs

ScoMAP

ScoMAP (Single-Cell Omics Mapping into spatial Axes using Pseudotemporall ordering) is an R package to spatially integrate single-cell omics data into virtual cells. This software is part of SCENIC Suite.

Paper Github

VSN-Pipelines

VSN-Pipelines Is a repository of pipelines for single-cell data analysis in Nextflow DSL2. It contains multiple workflows for analyzing single cell transcriptomics data, and depends on a number of tools, which are organized into submodules within the VIB-Singlecell-NF organization. This software is part of SCENIC Suite.

Github

SCope

SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Visit https://scope.aertslab.org to test out SCope on several published datasets! This software is part of SCENIC Suite.

Paper Github

cisTopic

cisTopic is an R package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data. This software is part of SCENIC Suite.

BioRXiv Preprint Paper Github

pySCENIC

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-CEll regulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data. This software is part of SCENIC Suite.

Github PyPi Read the Docs

arboreto

Arboreto is a computational framework that offers scalable implementations of Gene Regulatory Network inference algorithms. It currently supports GRNBoost2 and GENIE3 (Huynh-Thu et al., 2010). This software is part of SCENIC Suite.

Paper Github PyPi Read the Docs

SCENIC

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. This software is part of SCENIC Suite.

Read more... Paper Github

i-cisTarget

i-cisTarget is an integrative genomics method for the prediction of regulatory features and cis-regulatory modules.

Paper Website

iRegulon

iRegulon is a Cytoscape plugin that detects the TF, the targets and the motifs from a set of genes.

Paper Website

TECH-DEV

HyDrop

An open-source droplet microfluidics platform for single-cell RNA-seq and single-cell ATAC-seq strongly inspired by inDrop and Drop-seq. Developed at Aerts lab in close collaboration with the Single cell and Microfluidics Expertise Unit at the VIB Center for Brain and Disease Research

BioRXiv Preprint Paper Github Website

NEWS & PUBLICATIONS

  • SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

    SCENIC+ is a new method for the inference of eGRNs. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression.

  • How regulatory sequences learn cell representations

    New computational method uses convolutional neural networks for cis-regulatory sequence analysis to analyze and cluster scATAC-seq data.

  • Cell type directed design of synthetic enhancers

    We implemented and compared three different enhancer-design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target brain cell types in Drosophila, and melanoma cell states in human. We exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional.

    https://twitter.com/ibrahimihsan/status/1552370069681864709?s=20&t=g19-Ql8vVgPwEsEQ4cUTIg

  • ERC Advanced Grant

    ERC 2022

    Stein Aerts receives an ERC Advanced Grant for “Genome2Cells” where we will study how the genome “translates” into cell types.

  • Stein Aerts elected as EMBO Member (2022)

    EMBO 2022

    EMBO, or the European Molecular Biology Organization, brings together top researchers in the life sciences to promote collaboration and scientific progress. Each year, EMBO elects new members to join its ranks. Being elected as an EMBO member is an indication of a strong, high-quality research program that seeks to answer the molecular riddles in the life sciences. This year, Prof. Sarah-Maria Fendt (VIB-KU Leuven Center for Cancer Biology) and Prof. Stein Aerts (VIB-KU Leuven Center for Brain & Disease Research) join the other VIB EMBO members.

  • Decoding gene regulation in the fly brain

    A chromatin accessibility atlas of 240,919 cells in the adult and developing Drosophila brain reveals 95,000 enhancers, that are integrated in cell-type specific enhancer gene regulatory networks and decoded into combinations of functional transcription factor binding sites using deep learning.

  • Analysis of long and short enhancers in melanoma cell states

    Multi-level massively parallel reporter assays (H3K27ac, ATAC and short tiles) in a panel of melanoma cell lines, together with a deep learning model, reveal location, multiplicity, and grammar of subtype specific enhancers.

  • Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly

    For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type–related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

  • Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads

    Our lab developed HyDrop, a flexible and open-source droplet microfluidic platform for scRNA-seq and scATAC-seq experiments. We applied HyDrop-ATAC and HyDrop-RNA to flash-frozen mouse cortex and generated 7996 high-quality single-cell chromatin accessibility profiles and 9508 single-cell transcriptomes closely matching reference single-cell gene data. Additionally, we leveraged HyDrop-RNA’s high capture rate to analyze a small population of FAC-sorted neurons from the Drosophila brain, confirming the protocol’s applicability to low input samples and small cells. Our publication includes step-by-step protocols for producing dissolvable barcoded hydrogel beads and applying these beads in scRNA-seq and scATAC-seq experiments.

  • Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

    Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states.

  • Chromatin accessibility profiling methods

    In this Primer, we discuss these biochemical methods, as well as bioinformatics tools for analysing and interpreting the generated data, and insights into the key regulators underlying developmental, evolutionary and disease processes. We outline standards for data quality, reproducibility and deposition used by the genomics community.

  • Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma

    Melanoma cells can switch between a melanocytic and a mesenchymal-like state. Scattered evidence indicates that additional intermediate state(s) may exist. Here, to search for such states and decipher their underlying gene regulatory network (GRN), we studied 10 melanoma cultures using single-cell RNA sequencing (RNA-seq) as well as 26 additional cultures using bulk RNA-seq.

  • Cross-species analysis of enhancer logic using deep learning

    Genomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators.

  • Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics

    Single‐cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single‐cell RNA ‐seq and single‐cell ATAC ‐seq atlases of the Drosophila eye‐antennal disc and spatially integrate the data into a virtual latent space that mimics the organization of the 2D tissue using ScoMAP (Single‐Cell Omics Mapping into spatial Axes using Pseudotime ordering).

  • A scalable SCENIC workflow for single-cell gene regulatory network analysis

    This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines.

  • Author File in Nature Methods features Stein Aerts

    Nature Methods 16, 355 (2019)

    “It’s still me,” says Stein Aerts about his diverse science chapters to date. […] Click on link below to read more.

  • cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data.

    Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover “cis-regulatory topics” and stable cell states from sparse single-cell epigenomics data.

  • A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.

    A single-cell atlas of the adult fly brain during aging:

    • Network inference reveals regulatory states related to oxidative phosphorylation
    • Cell identity is retained during aging despite exponential decline of gene expression
    • SCope: An online tool to explore and compare single-cell datasets across species

  • The transcription factor Grainy head primes epithelial enhancers for spatiotemporal activation by displacing nucleosomes.

    Using ATAC-seq across a panel of Drosophila inbred strains, we found that SNPs affecting binding sites of the TF Grainy head (Grh) causally determine the accessibility of epithelial enhancers.

  • Data/Software Update

    Data & Software - Mar. 30th 2018

    • SCENIC is now available for Fly.
    • Arboreto makes GENIE3 highly parallelizable and comes with a novel and fast GRN inference algorithm as an alternative for GENIE3 for very large datasets.
    • pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-CEll regulatory Network Inference and Clustering).

  • Fly Cell Atlas

    Gasthuisberg campus, University of Leuven (Belgium), Dec. 8th 2017

    The Fly Cell Atlas will bring together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models.

  • SCENIC: single-cell regulatory network inference and clustering.

    We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data.

  • Hippo Reprograms the Transcriptional Response to Ras Signaling.

    Here we show that the Hippo pathway is critical for this decision. Loss of Hippo switches Ras activation from promoting cellular differentiation to aggressive cellular proliferation.

  • Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic.

    Using two complementary techniques of multiplex enhancer-reporter assays, we discovered that functional enhancers could be discriminated from nonfunctional binding events by the occurrence of a single TP53 canonical motif.

  • Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state.

    Using regulatory landscapes and in silico analysis, we show that transcriptional reprogramming underlies the distinct cellular states present in melanoma. Furthermore, it reveals an essential role for the TEADs, linking it to clinically relevant mechanisms such as invasion and resistance.

OUTREACH

Fly Cell Atlas

Together with B. Deplancke and R. Zinzen we founded the Fly Cell Atlas.

The Fly Cell Atlas will bring together Drosophila researchers interested in single-cell genomics, transcriptomics, and epigenomics, to build comprehensive cell atlases during different developmental stages and disease models.

Go to flycellatlas.org

BIG

  • The Leuven Bioinformatics Interest Group organises a monthly bioinformatics meeting to bring together bioinformaticians, and anybody interested in bioinformatics, across departments. It allows participants working on very different biological questions to present and discuss the technical, algorithmic, and mathematical aspects of their work. Moreover, it aims to create a meeting place for next-generation biologists to meet and discuss with bioinformaticians. Research areas include, but are not limited to : Analysis of next generation sequencing data, comparative and evolutionary genomics, data management and genome informatics, proteomics, epigenomics and gene regulation, network biology, …
  • Register on our mailing list to become a member! Visit BIG for more info.

Teaching

  • E06E2A Introduction to Bioinformatics (taught in Dutch)
  • I0D52A Bioinformatics: Structural and Comparative Genomics
  • E02N3AE Bioinformatics and Systems Biology: Sequence, Structure & Evolution
  • E02N4A Bioinformatics and Systems Biology: Expression, Regulation and Networks

Mendelcraft

MendelCraft is a MineCraft mod developed in the lab to teach children about DNA, genetics, and the laws of Mendel. You can visit the website at https://mendelcraft.aertslab.org/

Partners

  • VIB & KU Leuven
    • Our lab is part of the KU Leuven Center for Human Genetics at the KU Leuven, and the VIB Center for Brain and Disease Research
  • Genomics Core
    • The Genomics Core offers next-generation sequencing services on MiSeq, NextSeq, HiSeq2500, HiSeq4000, NovaSeq, and PacBio Sequel.
  • Vlaams Supercomputer Centrum (VSC)
  • JEDI

Press & Media

DATA & RESOURCES

JOIN US

We are always on the lookout for highly motivated scientists to join our team. If you are interested in a PhD or postdoc position to work on single-cell regulatory genomics - computational or experimental - please send your motivation letter and CV to Stein Aerts.

Currently open positions

Team

Stein Aerts

Stein Aerts

PRINCIPAL INVESTIGATOR
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Sara Aibar Santos

Sara Aibar Santos

POSTDOCTORAL SCIENTIST
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David Mauduit

David Mauduit

POSTDOCTORAL SCIENTIST
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Nikolai Hecker

Nikolai Hecker

POSTDOCTORAL SCIENTIST
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Olga Sigalova

Olga Sigalova

POSTDOCTORAL SCIENTIST
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Gabriele Partel

Gabriele Partel

POSTDOCTORAL SCIENTIST
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Carmen Bravo González-Blas

Carmen Bravo González-Blas

PHD STUDENT
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Jasper Janssens

Jasper Janssens

PHD STUDENT
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Ibrahim Taskiran

Ibrahim Taskiran

PHD STUDENT
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Florian De Rop

Florian De Rop

PHD STUDENT
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Seppe De Winter

Seppe De Winter

PHD STUDENT
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Cas Blaauw

Cas Blaauw

PHD STUDENT
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Hannah Dickmänken

Hannah Dickmänken

PHD STUDENT
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Eren Can Ekşi

Eren Can Ekşi

PHD STUDENT
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Alexandra Pančíková

Alexandra Pančíková

PHD STUDENT
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Darina Abaffyová

Darina Abaffyová

PHD STUDENT
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Valerie Christiaens

Valerie Christiaens

LAB MANAGER
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Koen Theunis

Koen Theunis

TECHNICAL STAFF / LAB ENGINEER
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Roel Vandepoel

Roel Vandepoel

TECHNICAL STAFF / LAB ENGINEER
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Gert Hulselmans

Gert Hulselmans

TECHNICAL STAFF / BIOINFORMATICIAN
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Katina Spanier

Katina Spanier

TECHNICAL STAFF / BIOINFORMATICIAN
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Niklas Kempynck

Niklas Kempynck

TECHNICAL STAFF / ARTIFICIAL INTELLIGENCE ENGINEER
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Embedded/Associated Members

Suresh Poovathingal

Suresh Poovathingal

LEADER OF SINGLE-CELL AND MICROFLUIDICS EXPERTISE UNIT
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Kristofer Davie

Kristofer Davie

LEADER OF SINGLE-CELL BIOINFORMATICS EXPERTISE UNIT
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Alumni

  • Zeynep Kalender Atak (Senior scientist AstraZeneca)
  • Jasper Wouters (Senior scientist Galapagos)
  • Swann Floc'hlay
  • Jonas Demeulemeester (Assistant Professor KU Leuven & VIB Group Leader)
  • Bram Van De Sande (Head of Bioinformatics for Immuno-oncology, UCB)
  • Marina Naval Sanchez (PhD); postdoc at CSIRO, Australia
  • Annelien Verfaillie (Operational Group Responsible, Genomics Core Leuven)
  • Dmitry Svetlichnyy (PhD); Research Scientist, Skoltech, Russia
  • Delphine Potier (Independent CNRS researcher, Marseille)
  • Rekin's Janky (Postdoc); bioinformatician, VIB Nucleomics Core, Leuven
  • Mark Fiers (part-time Professor KULeuven)
  • Jelle Jacobs (Phd); postdoc at IMP (Stark lab), Vienna
  • Hana Imrichova (PhD); postdoc at CeMM (Bock lab), Vienna
  • Xiaojiang Quan (Postdoctoral Scientist)
  • Samira Makhzami (Technical Staff/Lab Technician)
  • Dafni Papasokrati (Veteranary sciences, Cambridge)
  • Liesbeth Minnoye (Scientist Bioinformatics at Galapagos)
  • Joy Ismail (University teacher, University of Colorado)
  • Christopher Flerin (Senior Data Scientist, GSK)
  • Maxime De Waegeneer (started a software company)
  • Nikoleta Psatha (Assistant Professor, Aristotle University of Thessaloniki)

  • Lab events & Pictures

    CME DAY LCB
    CME DAY LCB
    LAB RETREAT 2012
    LAB RETREAT 2012
    LCBRETREAT 2013 (1)
    LCBRETREAT 2013 (1)
    LCBRETREAT 2013 (2)
    LCBRETREAT 2013 (2)
    LCBRETREAT 2013 (3)
    LCBRETREAT 2013 (3)
    LAB LUNCH 2016
    LAB LUNCH 2016
    LAB RETREAT 2016 (1)
    LAB RETREAT 2016 (1)
    LAB RETREAT 2016 (2)
    LAB RETREAT 2016 (2)
    NEW YEARS LUNCH 2018
    NEW YEARS LUNCH 2018

    Contact

    Stein Aerts

    Herestraat 49, PO Box 602, 3000 LEUVEN, Belgium

    +32-16-33 07 10

    olga-sigalova alexandra-pancikova cas-blaauw darina-abaffyova eren-can-eksi hannah-dickmanken niklas-kempynck roel-vandepoel gabriele-partel koen-theunis nikoleta-psatha-(assistant-professor,-aristotle-university-of-thessaloniki) nikolai-hecker joy-ismail-(university-teacher,-university-of-colorado) jonas-demeulemeester-(assistant-professor-ku-leuven-&-vib-group-leader) florian-de-rop seppe-de-winter annelien-verfaillie-(operational-group-responsible,-genomics-core-leuven) bram-van-de-sande-(head-of-bioinformatics-for-immuno-oncology,-ucb) carmen-bravo-gonzalez-blas christopher-flerin-(senior-data-scientist,-gsk) dafni-papasokrati-(veteranary-sciences,-cambridge) david-mauduit delphine-potier-(independent-cnrs-researcher,-marseille) dmitry-svetlichnyy-(phd);-research-scientist,-skoltech,-russia gert-hulselmans hana-imrichova-(phd);-postdoc-at-cemm-(bock-lab),-vienna ibrahim-taskiran jasper-janssens jasper-wouters-(senior-scientist-galapagos) jelle-jacobs-(phd);-postdoc-at-imp-(stark-lab),-vienna katina-spanier kristofer-davie liesbeth-minnoye-(scientist-bioinformatics-at-galapagos) marina-naval-sanchez-(phd);-postdoc-at-csiro,-australia mark-fiers-(part-time-professor-kuleuven) maxime-de-waegeneer-(started-a-software-company) rekin's-janky-(postdoc);-bioinformatician,-vib-nucleomics-core,-leuven samira-makhzami-(technical-staff/lab-technician) sara-aibar-santos stein-aerts suresh-poovathingal swann-floc'hlay valerie-christiaens xiaojiang-quan-(postdoctoral-scientist) zeynep-kalender-atak-(senior-scientist-astrazeneca)