The AFBI Farm Animal Bioinformatics (FAB) unit, formerly known as the Hillsborough Genetics Unit, is dedicated to creating data-driven and knowledge-driven solutions for sustainable livestock production. This is achieved through the application of advanced data science methods, including artificial intelligence, to multi-omics data. Our integrated approach enables us to explore the complex relationships between an animal’s genome, phenome, and environmental factors, ultimately aiming to enhance animal sustainability.
Aim
At AFBI’s Farm Animal Bioinformatics (FAB) unit, we are dedicated to developing innovative, data-driven solutions for sustainable livestock production. Our focus is on enhancing animal health, welfare, and productivity while significantly minimizing environmental impact.
Expertise and Approach
Our team of specialists in multi-omics, data sciences, and modeling collaborates to generate and analyze extensive datasets from diverse sources, including genomic, transcriptomic, metabolomic, and phenotypic data. Utilizing state-of-the-art techniques such as machine learning, cloud computing, and big data analytics, we identify patterns, trends, and relationships that inform decision-making within the agricultural sector.
Research Focus
Our research delves into the genetic and molecular mechanisms underlying key traits such as disease resistance, feed efficiency, and meat quality. Additionally, we investigate the interactions among genetics, nutrition, and environmental factors, assessing their effects on animal performance and sustainability.
Collaborative Solutions
By integrating our scientific expertise with active industry partnerships and stakeholder engagement, we aim to deliver practical solutions to the pressing challenges faced by the livestock sector. Our initiatives focus on reducing greenhouse gas emissions, improving feed conversion ratios, and enhancing animal health and welfare.
Commitment to Transformation
At FAB, we are committed to translating scientific discoveries into real-world applications that benefit society and promote sustainable food systems. Leveraging the power of data and innovative technologies, we strive to contribute to a resilient and sustainable future for farm animals, farmers, and consumers alike.
Precision Breeding Initiatives
The AFBI FAB is tasked with developing cutting-edge precision breeding solutions not just for Northern Ireland, but for the wider agricultural community. Our primary goal is to tackle critical challenges in livestock production, particularly the reduction of methane emissions, which significantly impact climate change. By harnessing advanced genetic technologies and data analytics, we aim to enhance feed efficiency—a vital component for maximizing animal health and productivity.
Economic and Environmental Sustainability
In addition to our environmental goals, we are dedicated to improving the overall profitability of livestock production. Our approach seamlessly integrates genetic improvement strategies with sustainable farming practices, enabling farmers to achieve economic viability while making a positive contribution to environmental sustainability. Through our research and initiatives, we aspire to set new standards in animal breeding, benefiting both the agricultural sector and the planet.
Join Us in Shaping the Future
We invite you to join us in revolutionizing the approach to sustainable livestock production. Together, we can create a brighter, more sustainable future for farm animals, farmers, and the environment.
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Meet Our Team
AFBI FAB team
AFBI FAB team from left to right:
Dr Masoud Shirali, Sarunas Dzinkevicius, Wentao Jiang, Richard Hills, Steffimol Rose Chacko Kaitholil, Stephen Ross, Edwin Ong Jun Kiat, Dr Vahid Razban.
My research is focused on developing cutting edge statistical and omics based methods to unravel the genetic background of complex traits and diseases.
Areas of research focus include;
Understanding the biological basis of complex traits and diseases in human and animals.
Develop cutting edge models by utilizing machine learning in multi-omics data such as genomics, transcriptomics and metabolomics data to predict difficult-to-measure phenotypes.
I am the head of Farm Animal Bioinformatics (FAB) at AFBI, and leading Northern Ireland Farm Animal Biobank (NIFAB). I currently act as an honorary senior lecturer / Associate Professor Quantitative Genetics at the faculty of Medicine, Health and Life Sciences Queens University Belfast.
Masoud.Shirali@afbini.gov.uk
Dr Vahid Razban
Deputy Head of AFBI FAB Deputy Leader of Northern Ireland Farm Animal Biobank (NIFAB) Associate Professor Bioinformatician and Molecular Geneticist
Vahid.Razban@afbini.gov.uk
PhD Researchers
Steffimol Chacko Kaitholil PhD researcher
Thesis Title: Multi-omics approach to predict economically important health and production traits in sheep.
steffimol.rose@afbini.gov.uk
Richard Hillis PhD researcher
Thesis Title: Network Immunology for Dairy Health: Towards Genomic Selection to Improve Animal Welfare.
Richard.Hillis@afbini.gov.uk
Stephen Ross PhD researcher
Thesis Title: Novel Machine Learning Modelling Using Experimental and Farm Data for Higher Efficiency and Lower Green House Gas Emission Dairy Production.
Stephen.Ross2@afbini.gov.uk
Wentao Jiang PhD researcher
Thesis Title: Multi-omics data integration to understand the biological background of feed efficiency in dairy cows.
Wentao.Jiang@afbini.gov.uk
Edwin Ong Jun Kiat PhD researcher
Thesis Title: Utilizing machine learning in genomic selection of dairy cattle's feed efficiency complex.
Edwin.OngJunKiat@afbini.gov.uk
Sarunas Dzinkevicius PhD researcher
Thesis Title: Host genetics influence the rumen microbiota and heritable rumen microbial features associate with methane emission production in dairy cattle.
Sarunas.Dzinkevicius@afbini.gov.uk
Farzad Ghafouri PhD researcher
Thesis Title:Identification of the mechanisms of gene regulation involved in mastitis disease of dairy cows
Farzad.Ghafouri@afbini.gov.uk
Sedigheh Akbarnezhad Neshel PhD researcher
Thesis Title: Genome analysis of reproduction efficiency in ewes.
