Genomics and Bioinformatics for Non-Model Organisms (BNMO01)
Bioinformatics for Non-Model Organisms: learn genome assembly, annotation, and genomic analysis for non-model species.
Duration: 25 hours
Next Date: December 7-11, 2026
Format: Live Online Format
Sweden (GMT+1) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£400Registration Fee
Register NowLike what you see? Click and share!
- Overview
- Instructors
- Schedule
Course Description
Advances in high-throughput sequencing technologies have opened unprecedented opportunities for studying non-model organisms across ecology, evolutionary biology, conservation genetics, and environmental sciences. However, the analysis of genomic and transcriptomic data from non-model species presents unique challenges due to incomplete reference genomes, limited functional annotation, complex population histories, and the scarcity of species-specific genomic resources. This course provides a practical introduction to bioinformatics methods for analyzing genomic, transcriptomic, and multi-omics datasets from non-model organisms.
Participants will learn how to process sequencing data, identify genetic variation, quantify gene expression, perform differential expression and eQTL analyses, and integrate multiple layers of biological information. The course combines theoretical foundations with practical workflows widely used in modern genomics, transcriptomics, and population genetics, enabling participants to conduct comprehensive analyses of non-model organism datasets and interpret their biological significance.
What You’ll Learn
- The principles of bioinformatics and the unique challenges associated with non-model organism research.
- How genomic resources differ between model and non-model species and how these differences affect data analysis.
- The foundations of genetic variation analysis and gene expression profiling.
- An introduction to single-cell transcriptomics and its applications in ecology and evolutionary biology.
- How to perform sequencing data quality control, adapter trimming, and contamination assessment.
- The components of the GATK best-practice workflow, including alignment, duplicate removal, base quality score recalibration, indel realignment, and variant calling.
- How to align RNA-seq data using STAR and quantify gene expression using featureCounts.
- Methods for differential gene expression analysis and interpretation of transcriptomic results.
- How to identify and analyze alternative splicing events.
- The principles of expression quantitative trait locus (eQTL) analysis and genotype-expression associations.
- Strategies for integrating genomic, transcriptomic, and other omics datasets in multi-omics studies.
- Applications of PCA and UMAP for population structure analysis.
- The concepts behind polygenic risk scores and their potential applications beyond human genetics.
- The missing heritability problem and current approaches for understanding complex trait architecture.
- Practical skills for analyzing genomic and transcriptomic datasets from non-model organisms.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is intended for ecologists, evolutionary biologists, conservation geneticists, bioinformaticians, molecular biologists, postgraduate students, and early-career researchers working with non-model organisms.
Participants are expected to have a basic understanding of genetics, molecular biology, and statistics, together with some familiarity with command-line computing and R. Prior experience with next-generation sequencing data is beneficial but not required. Familiarity with genomics, transcriptomics, or population genetics would be advantageous, although key concepts and methodologies will be introduced throughout the course.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R / Rstudio and Python / Jupyter, which are free tools and can be installed from https://posit.co/download/rstudio-desktop/ and Jupyter https://jupyter.org/install, resepctively. During the course, the Google Colab, https://colab.research.google.com/, and Posit Cloud, https://posit.cloud, will be used for practical session, which require a Google account.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Nikolay Oskolkov
Nikolay is a bioinformatician, computational biologist, and data scientist working at the intersection of biology, medicine, statistics, and artificial intelligence. His research focuses on applying mathematical statistics, machine learning, and deep learning methods to complex biological and biomedical datasets, including genomics, transcriptomics, microbiome research, single-cell data, metagenomics, and multi-omics integration.
Nikolay has a PhD in theoretical physics from 2007, he transition to the Life Sciences in 2011. He currently leads the Metabolic Research Group (MRG) within the TARGETWISE project at the National Institute of Research and Innovation in Latvia, and having a teaching position at Lund University, Sweden, he has previously held research positions at the Danish Technical University, University of North Carolina, Lund University and the National Bioinformatics Infrastructure Sweden (NBIS/SciLifeLab).
Nikolay has more than 20 years of teaching experience and is widely recognised for his ability to communicate advanced statistical and computational methods to researchers from diverse scientific backgrounds. His expertise spans both frequentist and Bayesian statistics, machine learning, dimensionality reduction, clustering, bioinformatics, and scientific programming in R and Python. He has delivered numerous international workshops, summer schools, and professional training courses in computational biology, genomics, and AI-driven biomedical research.
Education & Career
- PhD in Theoretical Physics (2007)
• Transitioned from theoretical physics to bioinformatics and computational biology in 2011
• Group Leader (PI), Metabolic Research Group, TARGETWISE Project, Latvia
• Former researcher and bioinformatician at Lund University and NBIS/SciLifeLab, Sweden
• Author of more than 60 peer-reviewed scientific publications with extensive international collaborations in computational biology and biomedical research
Research Focus
Nikolay’s work centres on extracting biological insight from large-scale, high-dimensional datasets using advanced statistical and machine learning approaches. His research interests include:
- Machine learning and deep learning for biomedical and omics data
• Multi-omics integration and systems biology
• Single-cell transcriptomics and dimensionality reduction methods
• Population genomics and evolutionary biology
• Microbiome, environmental DNA, and ancient DNA analysis
• Statistical modelling and Bayesian approaches for complex biological systems
• AI applications in precision medicine and drug discovery
Current Projects
- Development of machine learning methods for multi-omics data integration and drug discovery in metabolic diseases
• AI-driven approaches for genomics and computational biology
• Statistical and computational methods for ancient and environmental DNA research
• Machine learning analysis workflows for single-cell and population genomics datasets
• Research on metabolic diseases through integrative bioinformatics and systems biology approaches
Professional Consultancy
Nikolay provides expert consultancy in biological and biomedical data analysis, supporting academic researchers, healthcare scientists, and industry teams. His consultancy expertise includes:
- Bioinformatics and computational biology
• Medical genomics and precision medicine
• Single-cell and multi-omics data analysis
• Metagenomics and population genomics
• Frequentist and Bayesian statistical modelling
• Machine learning and deep learning applications
• Scientific programming in R, Python, Bash, and C++
• Study design, data analysis pipelines, and reproducible research workflows
Teaching & Skills
- More than 20 years of teaching experience in statistics, machine learning, and computational biology
• Teaches topics including machine learning, deep learning, Bayesian statistics, dimensionality reduction, clustering, single-cell analysis, genomics, and bioinformatics
• Instructor for international courses and workshops through organisations including Instats, Physalia, NBIS SciLifeLab, TARGETWISE, and RaukR
• Strong advocate for rigorous statistical thinking, reproducible research, and accessible scientific education
• Experienced in translating advanced computational methods into practical tools for life scientists and healthcare researchers
Links
Session 1 – 02:30:00 – Introduction to Bioinformatics for Non-Model Organisms
Overview of bioinformatics workflows; challenges associated with non-model species; incomplete genome assemblies, annotation limitations, and ecological applications.
Break – 01:00:00
Session 2 – 02:30:00 – Genetic Variation and Gene Expression Analysis
Introduction to genetic variation, transcriptomics, RNA sequencing, and single-cell gene expression analysis in ecological and evolutionary studies.
Session 3 – 02:30:00 – Quality Control and Contamination Assessment
Quality assessment of sequencing data; adapter removal, filtering strategies, contamination detection, and best practices for genomic and transcriptomic analyses.
Break – 01:00:00
Session 4 – 02:30:00 – GATK Workflow for Variant Discovery
Sequence alignment, duplicate removal, base quality score recalibration, indel realignment, variant discovery, and best-practice workflows for genomic analyses.
Session 9 – 02:30:00 – Population Structure Analysis
Population structure inference using PCA and UMAP; interpretation of clustering patterns; effects of uneven sampling and high-dimensional data.
Break – 01:00:00
Session 10 – 02:30:00 – Polygenic Risk Scores and Missing Heritability
Construction and validation of polygenic risk scores; applications to evolutionary biology; the missing heritability problem and emerging approaches for understanding complex traits.
Session 7 – 02:30:00 – eQTL Analysis and Regulatory Genomics
Expression quantitative trait loci (eQTL) analysis; integrating genotype and gene expression data; identification of regulatory variants.
Break – 01:00:00
Session 8 – 02:30:00 – Multi-Omics Data Integration for Non-Model Organisms
Combining genomic, transcriptomic, epigenomic, metabolomic, and ecological datasets; challenges and opportunities in integrative biology.
Session 9 – 02:30:00 – Machine Learning in ancinet DNA research
Characteristics of ancient DNA datasets; challenges associated with degradation and contamination; missing data and low coverage challenges in ancient DNA; feature engineering approaches.
Break – 01:00:00
Session 10 – 02:30:00 – Deep Learning applications in ancient DNA and evolutionary biology
Deep learning approaches for ancient-status inference; current applications, limitations, and future directions in ecology and evolutionary biology.
Testimonials
PR Stats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PR Stats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PR Stats provided excellent training in stable isotope analysis through the SIMMPR course, which was incredibly valuable for my research. I was fortunate to attend the course through a generous fee waiver, which directly supported my work and enabled me to develop skills that contributed to my recent publication on reservoir food webs in Sri Lanka. I’m very grateful for the opportunity and support, and would highly recommend their courses to others working in ecological research.
Subodha Silva
Aquatic Ecology Researcher
Courses attended
SIMMPR
Testimonials
PR Stats has become an invaluable part of developing my skills in advanced statistical and spatial analysis. Through training in areas such as Bayesian statistics and Species Distribution Modelling, I’ve gained both practical expertise and exposure to leading experts in the field. The impact on my research has been significant with at least four of my published papers have been directly influenced by PR Stats courses. My most recent work benefitted from modelling advice on sample design and model accuracy evaluation and can be seen here.
Carlos P.E. Bedson
Quantitative Spatial Ecology, Ecology and Environment Research Centre, Manchester Metropolitan University, United Kingdom
Courses attended
ADVR08, ENMR03, BMIN02, ISBD01, BADA01, SDMB06
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
Still have questions?
Can’t find the answer you’re looking for? Please chat to our friendly team.
