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Victor Loegler

PhD in genomics, quantitative genetics
and bioinformatics

loegler.victor@gmail.com | GitHub | LinkedIn

PhD in genetics with strong expertise in genomics, bioinformatics, and multi-omics data integration. Experienced in analyzing complex genetic datasets, applying statistical and computational analyses, and translating results into clear visualizations and insights. Skilled in data science, machine learning, and scientific communication. Seeking roles in genetics or data science to apply analytical expertise to real-world biological challenges.

Education

Trainings

Research Experiences

Skills

Genomics & Bioinformatics

Population genetics, Variant detection, GWAS, Transcriptomics, Sequence alignments, Genome assemblies, Illumina, Oxford Nanopore Technology

Programming & Data Analysis

Python (Pandas, NumPy, SciPy, PyTorch), R (tidyverse, Bioconductor), SQL, Bash/UNIX, HPC SLURM, Snakemake, Git

Machine Learning & Statistics

Supervised/unsupervised learning, Regression models, Clustering, Dimensionality reduction, Statistical testing, Data visualization

Soft Skills

Project management, Data interpretation & critical thinking, Technical writing & manuscript preparation, Collaboration in multidisciplinary teams

Languages

French (Native), English

Publications

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Dynamics of genome evolution in the era of pangenome analysis

Loegler V, Friedrich A, Schacherer J. 2025.

Cell Genomics 0. doi:10.1016/j.xgen.2025.101067
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From genotype to phenotype with 1,086 near telomere-to-telomere yeast genomes

Loegler V, Thiele P, Teyssonnière E, Tsouris A, Brach G, Cruaud C, Payen E, Engelen S, Dunham MJ, Hou J, Friedrich A, Schacherer J. 2025.

Nature 1–10. doi:10.1038/s41586-025-09637-0
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Graph-based pangenome analysis uncovers structural and functional impacts of allopolyploidization events

Loegler V, Friedrich A, Schacherer J. 2025.

BioRxiv. doi:10.1101/2025.08.26.672342
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Whole-genome sequencing of 1,060 Brettanomyces bruxellensis isolates reveals significant phenotypic impact of acquired subgenomes in allopolyploids

Loegler V, Runge JN, Eberlein C, Peltier E, Harrouard J, Miot-Sertier C, Albertin W, Friedrich A, Schacherer J. 2025.

Nat Commun 16, 5500. doi:10.1038/s41467-025-60706-4
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Overview of the Saccharomyces cerevisiae population structure through the lens of 3,034 genomes

Loegler V, Friedrich A, Schacherer J. 2024.

G3 Genes|Genomes|Genetics 14:jkae245. doi:10.1093/g3journal/jkae245
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Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. 2024.

PNAS 121:e2319211121. doi:10.1073/pnas.2319211121
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Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast

Caudal É, Loegler V, Dutreux F, Vakirlis N, Teyssonnière É, Caradec C, Friedrich A, Hou J, Schacherer J. 2024.

Nat Genet 56:1278–1287. doi:10.1038/s41588-024-01769-9
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RNA Modifications in Pathogenic Bacteria: Impact on Host Adaptation and Virulence

Antoine L, Bahena-Ceron R, Devi Bunwaree H, Gobry M, Loegler V, Romby P, Marzi S. 2021.

Genes 12:1125. doi:10.3390/genes12081125

Oral communications

Portfolio

A selection of machine learning projects applying ML and deep learning to genomics and transcriptomics data.

Human Ancestry Prediction from SNP Data

Goal: Predict ancestry from SNP data

Models: KNN, Random Forest, Dense Neural Network, Ensemble

Focus: Feature encoding, classification, ensemble learning

Explainable Breast Cancer Subtype Classification

Goal: Classify samples into PAM50 subtypes

Models: XGBoost, Dense Neural Network

Focus: Multi-class classification, SHAP interpretability

Predicting Breast Tumor Malignancy with Deep Convolutional Models

Goal: Distinguish benign and malignant breast tumor images

Models: CNN baseline, Transfer Learning, Fine-tuned ResNet18

Focus: Computer vision, transfer learning, fine-tuning, model optimization

Single-Cell RNA-Seq Clustering & Classification

Goal: Cluster and predict immune cell types

Models: PCA+Leiden, K-Means, RF, MLP

Focus: Dimensionality reduction, label transfer

Transcription Factor Binding Site Prediction

Goal: Predict TF binding sites from DNA

Model: CNN (PyTorch)

Focus: Sequence encoding, CNN interpretation


Full portfolio on GitHub.