Clinical genomics
WGS, WES and targeted panels: alignment, post-processing, variant calling with multiple callers, annotation and automatic variant classification. Pharmacogenomic (PGx) analysis and computation of polygenic and clinical risk scores.
Senior Bioinformatics Scientist
Emanuele Martorana.
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I build pipelines and analysis tools for clinical and multi-omics data: genomics, transcriptomics, proteomics, microbiome. Most of my work is on NGS data and on bringing heterogeneous sources together, from omics layers to real-world data, into digital twins: virtual replicas of a person's health status, at the service of cancer research and precision medicine.
- 0 years in the field
- 0+ peer-reviewed papers
- 0 funded research projects
- 0 industrial patent
Profile
A few words about me.
I'm a Senior Bioinformatics Scientist with a background in Computer Science and a Ph.D. in Bioinformatics. Since 2018 I have been designing algorithms and pipelines for clinical and multi-omics datasets, with particular experience on NGS data (short and long reads) across genomic, transcriptomic, proteomic and microbiomic layers.
Most of my work goes into building and optimising analysis workflows, from preprocessing to downstream statistical and integrative analyses. I use R and Python daily, manage workflows with Nextflow and Docker, and reach for machine learning or network modelling when they genuinely help.
Almost every project I work on starts by combining omics, clinical and real-world data to find molecular patterns that are useful in the clinic. It only works as a team effort: much of the result depends on talking to the people who produce the data and to the people who will use it.
Expertise
From the sequencer output to biological and clinical interpretation.
Transcriptomics
Bulk RNA-seq (QC, splice-aware alignment, quantification, differential expression, functional enrichment) and single-cell work (cell-level QC, integration, clustering, annotation, trajectories, cell-cell communication). miRNA profiling and miRNA/mRNA networks.
Microbiomics
16S and shotgun metagenomics pipelines, from raw reads to taxonomic and functional profiles. Clinical-style indicators such as the Firmicutes/Bacteroidetes ratio, alpha and beta diversity, enterotypes, dysbiosis and pathogen detection.
Digital twin
Personalised digital models fed by omics, clinical and real-world data, wearables included. Harmonisation of the sources, standardised indicators for each layer, and simulation of disease trajectories and treatment response.
Proteomics & metabolomics
LC/MS pipelines from raw files to the protein by sample matrix: run and batch QC, identification with FDR control, label-free, TMT or DIA quantification, protein inference, normalisation, missing-value handling and statistical testing.
Epigenomics
Methylation from NGS (WGBS, RRBS) with CpG-level extraction and DMC/DMR discovery; EPIC v2 arrays with normalisation, cell-type deconvolution and epigenetic clocks for biological age.
Networks, KG & AI
Biomedical knowledge graphs in Neo4j (pharmacogenomics, microbiome, supplements), GraphRAG and rule-based reasoning engines, NER for automatic graph expansion, evidence-based disease scoring and machine learning for molecular signatures.
Engineering & cloud
Reproducible, containerised workflows, cloud deployment, versioning and data traceability. Data schemas and ETL-style steps (ingest, validate, standardise, store, publish) with automated QC checkpoints.
Statistics & modelling
Predictive modelling, survival analysis, dimensionality reduction, feature selection, ROC curves and classification performance assessment, with attention to model interpretability.
Stack & tools
Languages
Workflow & infra
Databases & resources
How I work
Four things I care about when setting up an analysis.
From raw data to usable indicators
It starts with quality control and ends with numbers someone can actually act on. Containerised workflows that give the same result on every run.
Multi-omics integration
A single layer only tells half the story. I harmonise datasets that don't naturally match and make them talk to each other through statistics, interaction networks and knowledge graphs.
Defensible methods
Explicit definitions, confounders under control (depth, batch, protocol) and proper references when a result is meant to be clinical. An explainable indicator beats an opaque predictor.
Translation and collaboration
I work shoulder to shoulder with biologists, clinicians, data scientists and software engineers. A good part of the value lies in making a complex output readable.
Where I work
- Academic and translational research Multi-omics projects on immune regulation and non-coding RNAs, together with experimental groups.
- Clinical oncology NGS pipelines and integrated analyses for prevention, diagnosis and prognosis, from liquid biopsy to molecular signatures.
- Precision medicine in industry Platforms bringing together multi-omics, real-world data and knowledge graphs.
- Digital health Remote patient monitoring and analysis of longitudinal clinical datasets.
Research & projects
A selection of recent work.
The patient's digital twin
A virtual replica of a person's health status, fed by wearables, blood tests, phenotypic data, genomics and microbiome. Each layer is harmonised and summarised into standardised indicators that flow into the platform.
Explainable disease scoring
A knowledge graph joining pharmacogenomics, microbiome and supplements on shared entities such as disease and drug. The score accumulates evidence from microbiome, genomics and phenotypes, and shows the graph paths that justify it.
Clinical decision support in oncology
A patented platform that starts from FASTQ, BAM or VCF and produces a full clinical report, through QC, alignment, variant calling and annotation. Variants are interpreted against the main oncology, pharmacology and regulatory databases.
Extracellular vesicles in prostate cancer
Differential diagnosis between prostate cancer and benign hyperplasia in a cohort with PSA between 2.5 and 10 ng/mL, where PSA alone says little. Counting single vesicles under super-resolution microscopy, the STAT3, CyclinD1 and CD81 combination reaches an AUC of about 0.77 against 0.67 for PSA.
miRNAs under microgravity
How microgravity affects gene expression in a 3D bone-like model, within an experiment carried out in orbit. Integrated RNA-seq and miRNA-seq analysis points to modules centred on Wnt5b and Runx2, consistent with a rewiring of osteoblast differentiation circuits.
Immune checkpoints in NK cells
Crosstalk between miRNAs, mRNAs and the proteome in PD-1 positive NK cells. Network analysis shows that modulation of certain miRNAs matches inverse changes in mRNA and protein levels, a hint of post-transcriptional control over checkpoints.
From dysbiosis to metabolites in colon cancer
A framework linking colorectal cancer associated microbial shifts to the metabolic tendencies of the community. A score balances the abundance of producer and consumer microbes for each metabolite and returns a ranked list.
Network motifs and analytical models
Doctoral work on models and algorithms for graph motifs, the small patterns that recur in interaction networks. The model estimates the expected number of motifs without enumerating every possible configuration, cutting computation time considerably.
Funded projects
- Co-Investigator, bioinformatics unit lead Artificial intelligence for drug repositioning Predictive models and automated pipelines for drug repurposing in oncology. National programme funded by the European Union.
- Bioinformatics and computational modelling Innovative diagnostics for precision oncology Computational methods on genomic and clinical data supporting personalised molecular diagnostics. Regional programme.
- Multi-omics integration Liquid biopsies for the clinical management of tumours Analysis and integration of omics data for non-invasive tumour characterisation. National programme, health area.
- PK/PD bioinformatics Vehicles for sustainable pharmaceutical innovation Multi-omics analysis and in-silico models for pharmacokinetic evaluation and drug delivery strategies. National programme.
Selected publications
More than 30 peer-reviewed works across computational oncology, multi-omics and methods.
- 2025 Sepsis detection in hematologic and solid tumor malignancies using quantitative inflammatory biomarker differences in a prospective single center study. Scientific Reports 15:31701
- 2025 Preoperative ctDNA retains prognostic relevance beyond postoperative assessment in stage II–III colon cancer. Clinical and Experimental Medicine
- 2025 Quantitative method for monitoring tumor evolution during and after therapy. Journal of Personalized Medicine 15(7):275
- 2024 A novel liquid biopsy method based on specific combinations of vesicular markers allows us to discriminate prostate cancer from hyperplasia.◆ Cells 13(15):1286
- 2024 Mathematical modeling of the synergistic interplay of radiotherapy and immunotherapy in anti-cancer treatments. Frontiers in Immunology 15:1373738
- 2023 Screening of precancerous lesions in women with HPV infection by molecular typing and microRNA analysis. Journal of Personalized Medicine 13(3):531
- 2023 Forecasting individual patients' best time for surgery in colon-rectal cancer by tumor regression during and after neoadjuvant radiochemotherapy.◆ Journal of Personalized Medicine 13(5):851
- 2022 Combination of interleukin-6, C-reactive protein and procalcitonin values as predictive index of sepsis in course of fever episode in adult haematological patients. Journal of Clinical Medicine 11(22):6800
- 2021 RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis. BMC Bioinformatics 22:298
- 2021 Adipose stem cell niche reprograms the colorectal cancer stem cell metastatic machinery. Nature Communications 12:5006
- 2020 Establish the expected number of induced motifs on unlabeled graphs through analytical models.◆ Applied Network Science 5:58
Full list (30+ papers) on Google Scholar ↗
Contact
For scientific collaborations or data analysis projects, drop me a line.
E-mail
emanuele@martorana.email
Time zone
CET (Central European)
Profiles
Google Scholar ↗
ORCID ↗
LinkedIn ↗