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Positions on AI Data Integration Ecosystem in Paediatric Oncology

Positions on AI Data Integration Ecosystem in Paediatric Oncology

We are looking for one or more researchers (graduate and/or postdoctoral) within the frame of a multidisciplinary project involving several European Institutions coordinated by the European Society for Paediatric Oncology (https://siope.eu). This ambitious project, entitled “UNICA4EU: Towards an UNIque approach for artificial intelligence data-driven solutions to fight Childhood cAncer FOR EUrope”, wants to pave the foundation of an ecosystem that will facilitate the upscaling and wide-scale application of Artificial Intelligence to Paediatric Cancer in the next ten years, positioning Europe as the worldwide benchmark in the field. The project is funded by the European Commission under the Horizon Europe program.

The candidate will work within the Data and Computational Biology Lab and the Bicocca Biostatistics, Bioinformatics, and Bioimaging Centre (B4) of the Università degli Studi di Milano-Bicocca, Italy (http://dcb.disco.unimib.it), in tight collaboration with computer scientists, computational biologists, and oncologists. Her/his tasks will be to analyse tools and policies for the AI-based integration of multi-omics data collected from several Europe wide experiments and clinical trials.

The ideal candidate will have:

  • Advanced degree in computer science, data-science, machine learning, bioinformatics, computational biology, or related field.
  • Strong expertise in programming, bioinformatics, and AI/ML data analysis.
  • Interest in translational research and biomedical sciences.
  • Ability in independent research and proven aptitude in interaction with interdisciplinary scientific teams
  • Excellent fluency in English and efficiency in technical/scientific writing.

Experience with multi-omics data handling pipelines; familiarity with the application of statistical methods, AI/ML techniques for imaging and/or sequencing analysis is a plus. Candidates with such background will be given precedence.

The positions are funded by the Università degli Studi di Milano-Bicocca under the UNICA4EU Horizon Europe grant. The positions are for 12 months with very competitive salaries for Italian standards up to 46K EUR gross. Start date: November 1st, 2022

The current call is informal; information about the full selection procedure will follow ASAP pending University regulations. To be given preliminary consideration please send:

  1.  a curriculum vitae (with a list of publications)
  2. a statement of research
  3. a list of two or more references

to marco.antoniotti@unimib.it with the keyword UNICA4EU-application in the subject. Use the same address for all inquiries.

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Publications

Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution

Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution

Daniele Ramazzotti, Fabrizio Angaroni, Davide Maspero, Mario Mauri, Deborah D’Aliberti, Diletta Fontana, Marco Antoniotti, Elena Maria Elli, Alex Graudenzi, Rocco Piazza
Virus Evolution, Volume 8, Issue 1, 2022

DOI: https://doi.org/10.1093/ve/veac026

⚠️ Interview by Luca Salvi, BNews, to Daniele Ramazzotti (link).

Abstract. Many large national and transnational studies have been dedicated to the analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome, most of which focused on missense and nonsense mutations. However, approximately 30 per cent of the SARS-CoV-2 variants are synonymous, therefore changing the target codon without affecting the corresponding protein sequence.

By performing a large-scale analysis of sequencing data generated from almost 400,000 SARS-CoV-2 samples, we show that silent mutations increasing the similarity of viral codons to the human ones tend to fixate in the viral genome overtime. This indicates that SARS-CoV-2 codon usage is adapting to the human host, likely improving its effectiveness in using the human aminoacyl-tRNA set through the accumulation of deceitfully neutral silent mutations.

One-Sentence Summary. Synonymous SARS-CoV-2 mutations related to the activity of different mutational processes may positively impact viral evolution by increasing its adaptation to the human codon usage.

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Publications

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

Daniele Ramazzotti, Fabrizio Angaroni, Davide Maspero, Gianluca Ascolani, Isabella Castiglioni, Rocco Piazza, Marco Antoniotti, Alex Graudenzi
Journal of Computational Science, February 2022

Abstract. The rise of longitudinal single-cell sequencing experiments on patient-derived cell cultures, xenografts and organoids is opening new opportunities to track cancer evolution, assess the efficacy of therapies and identify resistant subclones.

We introduce LACE, the first algorithmic framework that processes single-cell mutational profiles from samples collected at different time points to reconstruct longitudinal models of cancer evolution. The approach maximizes a weighted likelihood function computed on longitudinal data points to solve a Boolean matrix factorization problem, via Markov chain Monte Carlo sampling.

On simulations, LACE outperforms state-of-the-art methods for both bulk and single-cell sequencing data with respect to the reconstruction of the ground-truth clonal phylogeny and dynamics, also in conditions of unbalanced datasets, significant rates of sequencing errors and sampling limitations. As the results are robust with respect to data-specific errors, LACE is effective with mutational profiles generated by calling variants from (full-length) scRNA-seq data, and this allows one to investigate the relation between genomic and phenotypic evolution of tumors at the single-cell level.

Here, we apply LACE to a longitudinal scRNA-seq dataset of patient-derived xenografts of BRAFV600E/K mutant melanomas, dissecting the impact of BRAF/MEK-inhibition on clonal evolution, also in terms of clone-specific gene expression dynamics. Furthermore, the analysis of breast cancer PDXs from longitudinal targeted scDNA-sequencing experiments delivers a high-resolution temporal characterization of intra-tumor heterogeneity.

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Publications

VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data

VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data

Davide Maspero, Fabrizio Angaroni, Danilo Porro, Rocco Piazza, Alex Graudenzi, Daniele Ramazzotti
STAR PROTOCOLS, December 2021
Virmutsig graphical abstract

Abstract. We describe the procedures to perform the following: (1) the de novo discovery of mutational signatures from raw sequencing data of viral samples and (2) the association of existing viral mutational signatures to the samples of a given dataset. The goal is to identify and characterize the nucleotide substitution patterns related to the mutational processes that underlie the origination of variants in viral genomes. The VirMutSig protocol is available at this link: https://github.com/BIMIB-DISCo/VirMutSig.

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Publications

GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction​

GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction

Marzia Di Filippo, Chiara Damiani, Dario Pescini
PLOS COMPUTATIONAL BIOLOGY, November 2021
GPRuler graphical representation

Abstract. Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.