Bioinformatics Research Unit

Recognising the need for strong expertise in the field of bioinformatics and big data analysis, Rīga Stradiņš University (RSU) has recently established a Bioinformatics Research Unit. The main focus of the unit is the analysis and integration of genome, transcriptome, microbiome and other omics data with epidemiological, clinical, as well as environmental and lifestyle information in the context of personalised medicine, which aims at tailoring the diagnostics and therapy to individuals' needs.

Expertise

Genomic data: genotyping array and exome and whole genome sequencing (WES/WGS) data analysis, including quality control and data cleaning, genetic variant calling and functional annotation, adjustment for confounders, imputation, association analyses and visualisation.

Transcriptome data: RNA-seq, microarray and RT-qPCR data analysis, including quality control, data filtering, normalisation, differential expression analyses and visualisation. Both protein coding and non-coding RNA transcripts can be analyzed at gene or isoform-level.

Microbiome data: 16S rRNA amplicon sequencing data quality control, pre-processing, taxonomic classification and down-stream analyses, such as alpha and beta analyses, co-occurrence networks and predictive functional profiling of the microbial communities.

Exploratory analyses and visualisation to detect patterns and trends in the data, to extract meaningful information from the data and to prepare for further inferential analysis and prepare publication-ready figures.

Medical image analyses: Collaboration and advice for the design of research projects involving the use of medical images for medical diagnosis and prediction using artificial intelligence, from defining specifications, to deployment and scaling.

Data integration: integrating genetic variants with transcript co-expression patterns, protein and metabolite levels, phenotypic traits, experimental read-outs or clinical patient data.

Staff

PhD Baiba Vilne

Principal investigator, Bioinformatics Lab
Head of Laboratory, Bioinformatics Lab

Baiba Vilne, PhD, Principal Investigator, Bioinformatics Research Unit & Project Manager, Research Department.

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Vilne has more than 10 years of experience in bioinformatics and systems medicine, analysing genomic data (arrays, WES/WGS) transcriptome and microbiome data. Her major focus has been integrative multi-omics analysis for precision medicine, starting from pair-wise integrations (e.g., expression quantitative trait loci; eQTLs), followed by re-construction of gene co-expression networks/modules, linking those to the life-style and environment data.

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MSc. Katrīna Daila Neiburga

Research assistant, Bioinformatics Lab

Katrīna Daila Neiburga, MSc, Research Assistant at the Bioinformatics Research Unit

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Currently Neiburga performs mRNA analysis in the project Predominantly primary antibody deficiencies among adults: solving etiology and causes of clinical variability (No. lzp-2020/1-0269).

In addition, Katrina is involved in multiple other bioinformatics related projects such as identifying miRNA variation in coronary arthery disease and linking it to genomic information in collaboration with Deutsches Herzzentrum München Klinik für Herz- und Kreislauferkrankungen, identifying hypoxia associated biomarkers in HER2+ breast cancer cell lines, and identifying bacterial vs viral infection biomarkers in children with fever by transcriptome analysis in urine.

Previously K. D. Neiburga has also worked on translational profiling of neuronal cells and mathematical modelling of plant metabolic pathways.

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Methods, Tools and Equipment

Genomic data: PLINK, SNPTEST, BWA-MEM, GATK4, DeepVariant, VarScan, Mutect2, BreakDancer, ANNOVAR

Transcriptome data: FastQC, Trimmomatic, STAR, featureCounts, edgeR, LIMMA, DESeq2, ToppFun

Microbiome data: FastQC, Trimmomatic, QIIME2, BMTagger, Kraken, PROKKA, MetaWRAP

Exploratory analyses and visualisation R, Tidyverse

Medical image analyses: oro.dicom, oro.nifti, spm12r, neurobase, oro.dti, Neuroconductor

Data integration: Correlation analyses (Pearson), co-expression networks (WGCNA), Machine Learning (caret)

For data analysis and storage, we have access to the high-performance computing (HPC) cluster at the Riga Technical University (RTU), which consists of 55 nodes and 1200 cores, providing 8 TB of total RAM (random access memory) and 0.5 PB of hard disk space.

Publications List

2021

• Hinterdobler J., Schott S., Meesmann A., Steinsiek A.-L., Zimmermann A.-S., Wobst J., Vilne B., Jin H., Baecklund C.-S., Moggio A., Braster Q., Molitor M., Hristov H., Weber C., Wenzel P., Scheiermann C., Maegdefessel L., Soehnlein O., Libby P., Nahrendorf M., Schunkert H., Kessler T., Sager H. B. (2021) Acute mental stress drives vascular inflammation and promotes plaque destabilization in mouse atherosclerosis. European Heart Journal, 2021 Jul. https://doi.org/10.1093/eurheartj/ehab371
• Zrelovs N., Ustinova M., Silamikelis I., Birzniece L., Megnis K., Rovite V., Freimane L., Silamikele L., Ansone L., Pjalkovskis J., Fridmanis F., Vilne B., Priedite M., Caica A., Gavars M., Perminovs D., Storozenko J., Savicka O., Dimina E., Dumpis U., Klovins J. (2021) First report on the Latvian SARS-CoV-2 isolate genetic diversity. Frontiers in Medicine, 2021 Apr. https://doi.org/10.3389/fmed.2021.626000
• Nieminen P and Uribe SE (2021) The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals. Entropy 2021, 23(4), 468; https://doi.org/10.3390/e23040468
• Schwendicke F, Singh T, Lee YH, Gaudin R, Chaurasia A, Wiegand T, Uribe SE, Krois J (2021) Artificial intelligence in dental research: Checklist for authors, reviewers, readers. Journal of Dentistry 107(103610). https://doi.org/10.1016/j.jdent.2021.103610.
• Uribe SE, Innes N and Maldupa I (2021) The global prevalence of early childhood caries: A systematic review with meta-analysis using the WHO diagnostic criteria. Int J Paediatr Dent. 2021; 00: 1– 14. https://doi.org/10.1111/ipd.12783
• Zambrano L.J.M., Karaduzovic-Hadziabdic K., Lonca Turukalo T., Przymus P., Trajkovik V., Aasmets O., Berland M., Gruca A., Telalovic J.H., Hron K., Klammsteiner T., Kolev M., Lahti L., Lopes M.B., Moreno V., Naskinova I., Org E., Paciência I., Papoutsoglou G., Shigdel R., Stres B., Vilne B., Yousef M., Carrillo de Santa Pau E., Claesson M., Moreno Indias I., Truu J. Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment. Front. Microbiol., 2021 Feb. https://doi.org/10.3389/fmicb.2021.634511
• Moreno-Indias I., Lahti L., Nedyalkova M., Elbere I., Roshchupkin G., Adilovic M., Aydemir O., Bakir-Gungor B., Santa Pau E.C., D'Elia D., Desai M.S., Falquet L., Gundogdu A., Hron K., Klammsteiner T., Lopes M.B., Marcos-Zambrano L.J., Marques C., Mason M., May P., Pašić L., Pio G., Pongor S., Promponas V.J., Przymus P., Saez-Rodriguez J., Sampri A., Shigdel R., Stres B., Suharoschi R., Truu J., Truică C.O., Vilne B., Vlachakis D., Yilmaz E., Zeller G., Zomer A.L., Gómez-Cabrero D., Claesson M.J. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front. Microbiol., 2021 Feb. https://doi.org/10.3389/fmicb.2021.635781
• Rusko J., Vainovska P., Vilne B., Bartkevics V. (2021) Phenolic profiles of raw mono-and poly floral honeys from Latvia. J. Food Compos. Anal. 2021 Jan. https://doi.org/10.1016/j.jfca.2021.103813

2020

• Maldupa I, Sopule A, Uribe SE, et al. (2020) Caries prevalence and severity for 12-year-old children in Latvia. International dental journal in press: https://doi.org/10.1111/idj.12627
• Montalban, E., Giralt, A., Taing, L., Nakamura, Y., Martin, C., de Pins, B., Pelosi, A., Goutebroze, L., Castell, L., Wang, W., Neiburga, K. D., Vestito, L., Nairn, A. C., Valjent, E., Hervé, D., Heintz, N., Le Novère, N. G., Greengard, P., Roussarie, J.-P., Girault, J.-A., 2020. Translational profiling of mouse dopaminoceptive neurons reveals a role of PGE2 in dorsal striatum. [PREPRINT] doi:10.1101/2020.09.02.279240
• Araujo, M. P., Uribe, S., Robertson, M. D., Mendes, F. M., Raggio, D. P., Innes, N. P. T., 2020. "The Hall Technique and exfoliation of primary teeth: a retrospective cohort study." Br. Dent. J. 228, 213–217.
• Aguilera-Muñoz, F., Uribe, S., Sandoval-Valdés, F., 2020. "Diagnostic Agreement of Bone Measurements for Dental Implants by Cone-Beam Computed Tomography." Int. J. Odontostomat. 14, 89–94.

2019

• Alksere, B., Berzina, D., Dudorova, A., Conka, U., Andersone, S., Pimane, E., Krasucka, S., Blumberga, A., Dzalbs, A., Grinfelde, I., Vedmedovska, N., Fodina, V., Erenpreiss, J., 2019. Case of Inherited Partial AZFa Deletion without Impact on Male Fertility. Case Rep. Genet. 2019, 3802613.
• Fodina, V., Dudorova, A., Alksere, B., Dzalbs, A., Vedmedovska, N., Andersone, S., Una, C., Juris, E., Dace, B., 2019. The application of PGT-A for carriers of balanced structural chromosomal rearrangements. Gynecol. Endocrinol. 35, 18–23.
• Innes, N. P. T., Chu, C. H., Fontana, M., Lo, E. C. M., Thomson, W. M., Uribe, S., Heiland, M., Jepsen, S., Schwendicke, F., 2019. "A Century of Change towards Prevention and Minimal Intervention in Cariology." J. Dent. Res. 98, 611–617.
• Uribe, S., 2019. "The Routine Use of 3D Imaging May Not Reduce the Risk of Injuries to the Alveolar Inferior Nerve During Third Molar Extraction." J. Evid. Based. Dent. Pract. 19, 89–90.
• Vilne, B., Meistere, I., Grantiņa-Ieviņa, L., Ķibilds, J., 2019. "Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks." Front. Microbiol. 10, 1722.

2018

• Volozonoka, L., Perminov, D., Korņejeva, L., Alkšere, B., Novikova, N., Pīmane, E. J., Blumberga, A., Kempa, I., Miskova, A., Gailīte, L., Fodina, V., 2018. Performance comparison of two whole genome amplification techniques in frame of multifactor preimplantation genetic testing. J. Assist. Reprod. Genet. 35, 1457–1472.
• Clement, J., Uribe, S., Mariño, R. J., 2018. "Non-clinical Oral Health Practice Specialities", en: Mariño, R. J., Morgan, M. V., Walmsley, A. D. (Eds.), Career Paths in Oral Health. Springer International Publishing, Cham, pp. 95–104.
• Fejerskov, O., Uribe, S., Mariño, R. J., 2018. "Dentistry in a Historical Perspective and a Likely Future of the Profession", en: Mariño, R. J., Morgan, M. V., Walmsley, A. D. (Eds.), Career Paths in Oral Health. Springer International Publishing, Cham, pp. 3–19.
• Lira-Oetiker, M., Seguel-Galdames, F., Quero-Vallejos, I., Uribe, S., 2018. "Randomised clinical trial of patient satisfaction with traditional and simplified complete dentures." J. Oral Rehabil. 45, 386–392.
• Lempiäinen, H., Brænne, I., Michoel, T., Tragante, V., Vilne, B., Webb, T. R., Kyriakou, T., Eichner, J., Zeng, L., Willenborg, C., Franzen, O., Ruusalepp, A., Goel, A., van der Laan, S. W., Biegert, C., Hamby, S., Talukdar, H.A., Foroughi Asl, H., CVgenes@target consortium, Pasterkamp, G., Watkins, H., Samani, N. J., Wittenberger, T., Erdmann, J., Schunkert, H., Asselbergs, F. W., Björkegren, J. L. M., 2018. "Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets." Sci. Rep. 8, 3434.
• Vilne, B., Schunkert, H., 2018. "Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach." Front Cardiovasc Med 5, 89.
• Schunkert, H., von Scheidt, M., Kessler, T., Stiller, B., Zeng, L., Vilne, B., 2018. "Genetics of coronary artery disease in the light of genome-wide association studies." Clin. Res. Cardiol. 107, 2–9.

2017

• Uribe, S., 2017. "Radiographic prediction of inferior alveolar nerve injury in third molar surgery." Evid. Based. Dent. 18, 88–89.
• Vilne, B., Skogsberg, J., Foroughi Asl, H., Talukdar, H.A., Kessler, T., Björkegren, J. L. M., Schunkert, H., 2017. "Network analysis reveals a causal role of mitochondrial gene activity in atherosclerotic lesion formation." Atherosclerosis 267, 39–48.
• Kessler, T., Wobst, J., Wolf, B., Eckhold, J., Vilne, B., Hollstein, R., von Ameln, S., Dang, T. A., Sager, H. B., Moritz Rumpf, P., Aherrahrou, R., Kastrati, A., Björkegren, J. L. M., Erdmann, J., Lusis, A. J., Civelek, M., Kaiser, F. J., Schunkert, H., 2017. "Functional Characterization of the GUCY1A3 Coronary Artery Disease Risk Locus." Circulation 136, 476–489.

2016

• Senakola E, Maldupa I, Uribe S, Niznamovs M, 2016. "MUTES VESELĪBAS PĒTĪJUMS SKOLĒNIEM LATVIJĀ." The Centre for Disease Prevention and Control of Latvia.
• Mariño, R., Ramos-Gómez, F., Manton, D. J., Onetto, J. E., Hugo, F., Feldens, C. A., Bedi, R., Uribe, S., Zillmann, G., 2016. "The future of pediatric dentistry education and curricula: a Chilean perspective." BMC Oral Health 17, 20.
• Kessler, T., Erdmann, J., Vilne, B., Bruse, P., Kurowski, V., Diemert, P., Schunkert, H., Sager, H. B., 2016. "Serum microRNA-1233 is a specific biomarker for diagnosing acute pulmonary embolism." J. Transl. Med. 14, 120.
• Kessler, T., Vilne, B., Schunkert, H., 2016. "The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease." EMBO Mol. Med. 8, 688–701.

2015

• López, N.J., Uribe, S., Martinez, B., 2015. "Effect of periodontal treatment on preterm birth rate: a systematic review of meta-analyses." Periodontol. 2000 67, 87–130.
• Franke, K., Vilne, B., Prazeres da Costa, O., Rudelius, M., Peschel, C., Oostendorp, R.A.J., Keller, U., 2015. "In vivo hematopoietic Myc activation directs a transcriptional signature in endothelial cells within the bone marrow microenvironment." Oncotarget 6, 21827–21839.
• Istvánffy, R., Vilne, B., Schreck, C., Ruf, F., Pagel, C., Grziwok, S., Henkel, L., Prazeres da Costa, O., Berndt, J., Stümpflen, V., Götze, K. S., Schiemann, M., Peschel, C., Mewes, H.-W., Oostendorp, R. A. J., 2015. "Stroma-Derived Connective Tissue Growth Factor Maintains Cell Cycle Progression and Repopulation Activity of Hematopoietic Stem Cells In Vitro." Stem Cell Reports 5, 702–715.
• Brænne, I., Civelek, M., Vilne, B., Di Narzo, A., Johnson, A. D., Zhao, Y., Reiz, B., Codoni, V., Webb, T. R., Foroughi Asl, H., Hamby, S. E., Zeng, L., Trégouët, D.-A., Hao, K., Topol, E. J., Schadt, E. E., Yang, X., Samani, N. J., Björkegren, J. L. M., Erdmann, J., Schunkert, H., Lusis, A. J., Leducq Consortium CAD Genomics‡, 2015. "Prediction of Causal Candidate Genes in Coronary Artery Disease Loci." Arterioscler. Thromb. Vasc. Biol. 35, 2207–2217.

Seminars and Conferences

Upcoming

• 2020, Fall Term. Mastering your Data - from Exploration to Visualization Sergio Uribe, Visiting Associate Professor at RSU, DDS, MSc in Maxillofacial Radiology, PhD in Medical Sciences
• 2020, Nov 4th. Artificial Intelligence for Medical Imaging: A Brief Review of Clinical Evidence. Sergio Uribe, Visiting Associate Professor at RSU, DDS, MSc in Maxillofacial Radiology, PhD in Medical Sciences.
• 2020, Nov 4th. Artificial Intelligence Workflow for Medical Imaging Diagnosis: 7 steps from acquisition to prediction. Sergio Uribe, Visiting Associate Professor at RSU, DDS, MSc in Maxillofacial Radiology, PhD in Medical Sciences

Past

• 2020, June 3. The Role of Bioinformatics in Multi-OMICS-based Precision Medicine Baiba Vilne, Principal Investigator of the Bioinformatics Research Unit at RSU, PhD
• 2020, The Challenges of Medical Imaging Data Analyses for Precision Medicine Sergio Uribe, Visiting Associate Professor at RSU, DDS (Oral Surgeon), MSc in Maxillofacial Radiology, PhD in Medical Sciences