Explore microbial functions, communities and interactions.

Next-generation sequencing enables increasingly accurate identification of microbial identities, functions, and interactions.
We leverage the latest computational methods and databases to help microbiologists answer difficult questions.
How does the genomic composition of microbes affect their functions and cross-species interactions?
How can that information be used to discover new treatments or industrial applications?

What do microbiologists use our bioinformatics service for?

An essential starting point for sequence-based microbial analyses lies in establishing a high-quality genome assembly, complete with functional annotations. This enables

• identifying species and strains,
• characterizing the shared and unique genes (core- and pan-genomes) of diverse strains,
• identifying novel genes, particularly enzymes, for industrial or clinical applications,
• associating genomic and functional compositions to growth conditions or clinical variables.

RNA-sequencing from isolated strains or microbiomes (metatranscriptomics) enables interrogating subtler changes in microbial functions even when the genomic composition remains the same.

• study the host's reaction to infection (from e.g., host RNA-sequencing data),
• identify virulence factors,
• identify genes essential for drug resistance (using e.g., transposon insertion sequencing data), and
• identify molecular markers (e.g., surface peptides) of drug resistance.

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Selected publications from our customers

1. Mezheyeuski, A. et al. (2023). An immune score reflecting pro- and anti-tumoural balance of tumour microenvironment has major prognostic impact and predicts immunotherapy response in solid cancers. EBioMedicine, 88, 104452. Advance online publication
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2. Tusup, M. et al. (2022). Epitranscriptomics modifier * indirectly triggers Toll-like receptor 3 and can enhance immune infiltration in tumors. Molecular therapy : the journal of the American Society of Gene Therapy, 30(3), 1163–1170.
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3. Cramer, M. et al. (2022). Transcriptomic Regulation of Macrophages by Matrix-Bound Nanovesicle-Associated Interleukin-33. Tissue engineering. Part A, 28(19-20), 867–878
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4. Ribeiro, R. et al. (2022). Synchronous Epidermodysplasia Verruciformis and Intraepithelial Lesion of the Vulva is Caused by Coinfection with α-HPV and β-HPV Genotypes and Facilitated by Mutations in Cell-Mediated Immunity Genes. Preprint at
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5. Wullt, B. et al. (2021). Immunomodulation-A Molecular Solution to Treating Patients with Severe Bladder Pain Syndrome?. European urology open science, 31, 49–58.
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6. Åvall-Jääskeläinen, S. et al. (2021). Genomic Analysis of Staphylococcus aureus Isolates Associated With Peracute Non-gangrenous or Gangrenous Mastitis and Comparison With Other Mastitis-Associated Staphylococcus aureus Isolates. Frontiers in microbiology, 12, 688819.
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7. Madonna, G. et al. (2021). Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy. Cancers, 13(16), 4164.
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8. Gurvich, O. L. et al. (2020). Transcriptomics uncovers substantial variability associated with alterations in manufacturing processes of macrophage cell therapy products. Scientific reports, 10(1), 14049.
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9. Oksanen, M. et al. (2020). NF-E2-related factor 2 activation boosts antioxidant defenses and ameliorates inflammatory and amyloid properties in human Presenilin-1 mutated Alzheimer's disease astrocytes. Glia, 68(3), 589–599.
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