Giuseppe Martone · Bioinformatician & Freelance NGS Consultant
Bioinformatician and data analyst specialising in transcriptomics and the development of reproducible, scalable pipelines. Experienced in R, Bash and workflow management, with a background in optimising HPC and cloud infrastructure and applying machine-learning techniques to biomedical data. I work effectively with multidisciplinary teams to bring analytical results into translational research projects.
I support biotech companies, startups, research centres and scientific teams with cloud adoption, RNA-Seq, DNA-Seq and single-cell data, and reproducible pipeline optimisation—turning raw data into scientific and clinical insights.
Pozzuoli | Dec 2022 – Jul 2025
Day-to-day management of bioinformatics infrastructure for NGS data processing on Google Cloud Platform, including access management, work virtual machines, and configuring Nextflow and Docker for use with GCP. End-to-end design and management of RNA-Seq workflows and development of reproducible pipelines for identifying differentially expressed genes. Technical consulting and R&D support for internal and external clients, from infrastructure to data interpretation, integrating Oxford Nanopore, MGI STOMICS and NVIDIA Parabricks technologies.
Pozzuoli | Nov 2021 – Nov 2022
Developed and optimised Nextflow pipelines for large-scale transcriptomic analyses. Containerised workflows with Docker and Singularity and automated execution on HPC and cloud infrastructure. Wrote reproducibility guidelines and provided technical support to research groups.
Padua | Jul 2021 – Aug 2021
Developed an LSTM predictive model for post-operative clinical time series, including preprocessing, feature engineering, hyperparameter optimisation and cross-validation. Documented the workflow and built a deployment prototype for pilot clinical pathways.
End-to-end organisation of bioinformatics environments on Google Cloud Platform, including NGS data access, work virtual machines, and integration of Nextflow and Docker with cloud infrastructure. Design and implementation of pipelines and tools for transcriptome analysis, supporting tailored analyses and reports aligned with each biological question.
Validated and characterised recombinant vectors using short-read sequencing, de novo assembly, alignment and variant analysis. Customised pipelines to quantify mutations and CRISPR/Cas9 editing profiles.
Benchmarked and optimised genomic pipelines with NVIDIA Parabricks on GPUs; integrated Illumina NovaSeq X and DRAGEN. Developed workflows for MGI STOMICS spatial transcriptomics and Oxford Nanopore PromethION sequencing, including basecalling and long-read epigenomic analyses.
Optimised and validated short-read NGS pipelines for tumour markers in clinical cohorts. Conducted single-cell analyses to identify markers and differentially expressed genes across cell subpopulations.
Processed and analysed 3′ RNA-Seq, total RNA-Seq, full-length RNA and miRNA data, from quality assessment through quantification and interpretation. Used Kraken2 for taxonomic classification to assess contamination and, where suitable data and references are available, characterise microbial composition.
Padua | Nov 2020 – Sep 2021
Machine Learning and Big Data in Precision Medicine and Biomedical Research.
Turin | Oct 2017 – May 2020
Biological Sciences, with elective courses in Forensic Genetics and R programming. Final grade: 102/110.
Caserta | Oct 2014 – May 2017
Environmental, Biological and Pharmaceutical Sciences and Technologies; degree in Biological Sciences and Biotechnology. Final grade: 99/110.
R, Python and Bash/Shell scripting; machine learning for biomedical data, clinical time-series analysis and omics data.
Nextflow, Docker, Singularity; AWS Batch, EC2, S3, Google Cloud Platform (GCP Batch), HPC clusters, SLURM and RStudio Server.
FastQC, MultiQC, Trimmomatic, fastp, Cutadapt, BWA, STAR, Bowtie2, Minimap2, Salmon, Kallisto, Samtools, GATK and DeepVariant.
Nanopolish, Clair3, Modkit/Modbam, NVIDIA Parabricks, Illumina DRAGEN, MGI STOMICS and Oxford Nanopore PromethION.