Bioinformatics for genetics

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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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All analyses

References and customer cases

All references

Selected publications from our customers

  • 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
  • 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

  • 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
  • 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
  • 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. 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

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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.

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All analyses

References and case studies

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

  • 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
  • Manni, M. et al. (2020). The Genome of the Blind Soil-Dwelling and Ancestrally Wingless Dipluran Campodea augens: A Key Reference Hexapod for Studying the Emergence of Insect Innovations. Genome biology and evolution, 12(1), 3534–3549. https://doi.org/10.1093/gbe/evz260
  • 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
  • Rotenberg, D. et al. (2020). Genome-enabled insights into the biology of thrips as crop pests. BMC biology, 18(1), 142. https://doi.org/10.1186/s12915-020-00862-9
  • Oeyen, J. P. et al. (2020). Sawfly Genomes Reveal Evolutionary Acquisitions That Fostered the Mega-Radiation of Parasitoid and Eusocial Hymenoptera. Genome biology and evolution, 12(7), 1099–1188. https://doi.org/10.1093/gbe/evaa106
  • 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
  • Kriventseva, E. V. et al. (2019). OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic acids research, 47(D1), D807–D811. https://doi.org/10.1093/nar/gky1053
  • Waterhouse, R. M., et al. (2019). Using BUSCO to Assess Insect Genomic Resources. Methods in molecular biology (Clifton, N.J.), 1858, 59–74. https://doi.org/10.1007/978-1-4939-8775-7_6
  • 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
  • Ioannidis, P. et al. (2017). Genomic Features of the Damselfly Calopteryx splendens Representing a Sister Clade to Most Insect Orders. Genome biology and evolution, 9(2), 415–430. https://doi.org/10.1093/gbe/evx006
  • Hoy, M. A. et al. (2016). Genome Sequencing of the Phytoseiid Predatory Mite Metaseiulus occidentalis Reveals Completely Atomized Hox Genes and Superdynamic Intron Evolution. Genome biology and evolution, 8(6), 1762–1775. https://doi.org/10.1093/gbe/evw048
  • Simão, F. A. et al. (2015). BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics (Oxford, England), 31(19), 3210–3212. https://doi.org/10.1093/bioinformatics/btv351
  • Neafsey, D. E. et al. (2015). Mosquito genomics. Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Science (New York, N.Y.), 347(6217), 1258522. https://doi.org/10.1126/science.1258522

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Antti Ylipää
Antti Ylipää CEO, co-founder Genevia Technologies Oy +358 40 747 7672

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Genevia RNA-Seq Bioinformatics Grant 2022