Bioinformatics for genetics

Discover heritable variants and study their effects with computational genetics.

We employ the latest methods in computational genetics to identify clinically relevant variants and to study genetic variation in populations of humans, animals, plants and microbes.

Below we give examples of our experience in bioinformatic analyses for both clinical genetics and genetic research. If you are looking for a bioinformatics partner with expertise in genetics, we hope to hear from you!

Leave us a short description of your bioinformatics needs and we will be in touch very soon!

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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• DNA-seq data analysisSomatic mutation analysis

All analyses

References and customer cases

• Integrating scRNA-Seq and CUT&Tag data to investigate genomic instability in aging models (University of Cologne)
• Whole-genome and RNA sequencing analyses of hereditary cancer (University of Turku)
• Pharmacogenomic sequencing analysis (Karolinska Institute)

All references

Selected publications from our customers

• Zhong, M. et al. (2024). A meta-analysis and polygenic score study identifies novel genetic markers for waist-hip ratio in African populations. Obesity (Silver Spring, Md.), 10.1002/oby.24123. Advance online publication. https://doi.org/10.1002/oby.24123
• Karihtala, P. et al. (2024). Mutational signatures and their association with cancer survival and gene expression in multiple cancer types. International journal of cancer, 10.1002/ijc.35148. Advance online publication. https://doi.org/10.1002/ijc.35148
• Adebamowo, S. N. et al. (2024). Genome, HLA and polygenic risk score analyses for prevalent and persistent cervical human papillomavirus (HPV) infections. European journal of human genetics : EJHG, 10.1038/s41431-023-01521-7. Advance online publication. https://doi.org/10.1038/s41431-023-01521-7
• Hou, K. et al. (2023). Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations. bioRxiv : the preprint server for biology, 2023.09.30.560263. https://doi.org/10.1101/2023.09.30.560263
• Karihtala, P. et al. (2023). Mutational signatures and their association with survival and gene expression in urological carcinomas. Neoplasia (New York, N.Y.), 44, 100933. Advance online publication. https://doi.org/10.1016/j.neo.2023.100933
• Karihtala, P. et al. (2022). Comparison of the mutational profiles of neuroendocrine breast tumours, invasive ductal carcinomas and pancreatic neuroendocrine carcinomas. Oncogenesis, 11(1), 53. https://doi.org/10.1038/s41389-022-00427-1
• Yuan, O. et al. (2022). A somatic mutation in moesin drives progression into acute myeloid leukemia. Science advances, 8(16), eabm9987. https://doi.org/10.1126/sciadv.abm9987
• Wahlström, G. et al. (2022). The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression. Human molecular genetics, ddac012. Advance online publication. https://doi.org/10.1093/hmg/ddac012
• Karihtala, P. et al. (2022). Mutational Signatures Associate With Survival in Gastrointestinal Carcinomas. Cancer genomics & proteomics, 19(5), 556–569. https://doi.org/10.21873/cgp.20340

• 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 https://doi.org/10.21203/rs.3.rs-1991512/v1

• Pernaute-Lau, L. et al. (2021). Pharmacogene Sequencing of a Gabonese Population with Severe Plasmodium falciparum Malaria Reveals Multiple Novel Variants with Putative Relevance for Antimalarial Treatment. Antimicrobial agents and chemotherapy, 65(7), e0027521. https://doi.org/10.1128/AAC.00275-21
• Wullt, B. et al. (2021). Immunomodulation-A Molecular Solution to Treating Patients with Severe Bladder Pain Syndrome?. European urology open science, 31, 49–58. https://doi.org/10.1016/j.euros.2021.07.003
• Tikkanen, T. et al. (2018). Seshat: A Web service for accurate annotation, validation, and analysis of TP53 variants generated by conventional and next-generation sequencing. Human mutation, 39(7), 925–933. https://doi.org/10.1002/humu.23543

Selected publications from our team

• Ricordel, C. et al. (2023). Genomic characteristics and clinical significance of CD56+ circulating tumor cells in small cell lung cancer. Scientific reports, 13(1), 3626. https://doi.org/10.1038/s41598-023-30536-9
• Tielbeek, J. J. et al. (2022). Uncovering the genetic architecture of broad antisocial behavior through a genome-wide association study meta-analysis. Molecular psychiatry, 10.1038/s41380-022-01793-3. Advance online publication. https://doi.org/10.1038/s41380-022-01793-3
• Rajamäki, K. et al. (2021). Genetic and Epigenetic Characteristics of Inflammatory Bowel Disease-Associated Colorectal Cancer. Gastroenterology, 161(2), 592–607. https://doi.org/10.1053/j.gastro.2021.04.042
• Cerqueira, J. et al. (2021). Independent and cumulative coeliac disease-susceptibility loci are associated with distinct disease phenotypes. Journal of human genetics, 66(6), 613–623. https://doi.org/10.1038/s10038-020-00888-5
• van Heukelum, S. et al. (2021). A central role for anterior cingulate cortex in the control of pathological aggression. Current biology : CB, 31(11), 2321–2333.e5. https://doi.org/10.1016/j.cub.2021.03.062
• Kaikkonen, E. et al. (2018). ANO7 is associated with aggressive prostate cancer. International journal of cancer, 143(10), 2479–2487. https://doi.org/10.1002/ijc.31746
• Viana, J. et al. (2017). Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions. Human molecular genetics, 26(1), 210–225. https://doi.org/10.1093/hmg/ddw373
• Määttä, K. et al. (2016). Whole-exome sequencing of Finnish hereditary breast cancer families. European journal of human genetics : EJHG, 25(1), 85–93. https://doi.org/10.1038/ejhg.2016.141
• Pritchard, C. C. et al. (2016). Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer. The New England journal of medicine, 375(5), 443–453. https://doi.org/10.1056/NEJMoa1603144
• Laitinen, V. H. et al. (2016). Germline copy number variation analysis in Finnish families with hereditary prostate cancer. The Prostate, 76(3), 316–324. https://doi.org/10.1002/pros.23123
• Hannon, E. et al. (2016). An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome biology, 17(1), 176. https://doi.org/10.1186/s13059-016-1041-x

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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.

Learn more

• DNA-seq data analysis

All analyses

References and case studies

• Bacterial genomics (University of Helsinki)
• Reindeer population genetics (LUKE)

All references

Selected publications from our customers

• 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. https://doi.org/10.3389/fmicb.2021.688819
• Gallegos, J. E. et al. (2020). Challenges and opportunities for strain verification by whole-genome sequencing. Scientific reports, 10(1), 5873. https://doi.org/10.1038/s41598-020-62364-6

Selected publications from our team

• Kron N. S. (2022). In search of the Aplysia immunome: an in silico study. BMC genomics, 23(1), 543. https://doi.org/10.1186/s12864-022-08780-6
• Verta, J. P. et al. (2021). Genetic Drift Dominates Genome-Wide Regulatory Evolution Following an Ancient Whole-Genome Duplication in Atlantic Salmon. Genome biology and evolution, 13(5), evab059. https://doi.org/10.1093/gbe/evab059
• Yusuf, L. et al. (2020). Noncoding regions underpin avian bill shape diversification at macroevolutionary scales. Genome research, 30(4), 553–565. https://doi.org/10.1101/gr.255752.119
• Hayes, K. et al. (2020). A Study of Faster-Z Evolution in the Great Tit (Parus major). Genome biology and evolution, 12(3), 210–222. https://doi.org/10.1093/gbe/evaa044
• Lindfors, K. et al. (2020). Metagenomics of the faecal virome indicate a cumulative effect of enterovirus and gluten amount on the risk of coeliac disease autoimmunity in genetically at risk children: the TEDDY study. Gut, 69(8), 1416–1422. https://doi.org/10.1136/gutjnl-2019-319809
• Zeng, K. et al. (2019). Methods for Estimating Demography and Detecting Between-Locus Differences in the Effective Population Size and Mutation Rate. Molecular biology and evolution, 36(2), 423–433. https://doi.org/10.1093/molbev/msy212
• Barton, H. J. et al. (2019). The Impact of Natural Selection on Short Insertion and Deletion Variation in the Great Tit Genome. Genome biology and evolution, 11(6), 1514–1524. https://doi.org/10.1093/gbe/evz068
• Olofsson, J. K. et al. (2019). Population-Specific Selection on Standing Variation Generated by Lateral Gene Transfers in a Grass. Current biology : CB, 29(22), 3921–3927.e5. https://doi.org/10.1016/j.cub.2019.09.023
• Barton, H. J. et al. (2018). New Methods for Inferring the Distribution of Fitness Effects for INDELs and SNPs. Molecular biology and evolution, 35(6), 1536–1546. https://doi.org/10.1093/molbev/msy054
• Kim, J. M. et al. (2018). A high-density SNP chip for genotyping great tit (Parus major) populations and its application to studying the genetic architecture of exploration behaviour. Molecular ecology resources, 18(4), 877–891. https://doi.org/10.1111/1755-0998.12778
• Lin, J. et al. (2018). Bioinformatics Assembling and Assessment of Novel Coxsackievirus B1 Genome. Methods in molecular biology (Clifton, N.J.), 1838, 261–272. https://doi.org/10.1007/978-1-4939-8682-8_18
• Corcoran, P. et al. (2017). Determinants of the Efficacy of Natural Selection on Coding and Noncoding Variability in Two Passerine Species. Genome biology and evolution, 9(11), 2987–3007. https://doi.org/10.1093/gbe/evx213

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