Discover heritable variants and study their effects with computational genetics.

We employ the latest methods in computational genetics to identify cilinically relevant variants and to study genetic variation in population of humans , animals, plsants, microbes.
Below we give examples of our experience in bioinformatics analyses for both clinicsl genetics and genetic research. If you are looking for a bioinformatics partner with expertize in genetics, we hope to hear from you !!!

HUMAN GENETICS

Genetic variants, from simple polymorphisms to complex genomic rearrangements, are increasinly used in diagnosing patients and to estimate their risk of developing a disease in the future. We identify variants of all types from clinical DNA-sequencing data and, importantly, annotate and prioritize them to facilitate clinical decision-making. For researchers studying the heritable determinants of diseases, we identify risk loci from family sequencing studies and patient cohorts. For large cohorts, we run genome-wide association studies (GWAS) and develop polygenic risk score (PRS) models. Our broad expertise in multiomic analyses puts us in a great place to also study the molecular effects of genetic variants using RNA-sequencing and epigenomic data, for instance.

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NON-HUMAN GENETICS

Non-human models enable studying a wider range of questions in biology, and are vital for application areas such as agricultural and industrial biotechnology. We do not know how many different species our bioinformaticians have analyzed, but there are quite a few, from viruses to insects and mammals — as you will see from our publications below. Our experience is particularly strong in assembling and annotating de novo sequenced species, and in using population genetic methods to study how species and populations are moulded in the hands of evolution. For microbiologists, we characterize genomes and pangenomes from cultivated strains, and metagenome-sequenced microbial populations from both phylogenetic and functional perspectives.

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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