Skip to main content Skip to footer
Alma universitas studiorum parmensis A.D. 962 - Università di Parma
Università degli Studi di Parma

Deep Whole Genome Sequencing of bulk milk for sustainable cattle productions (DeepMilk)

Finanziato dall'Unione europea - NextGenerationEU
Ministero dell'Università e della Ricerca
Italia Domani - Piano Nazionale di Ripresa e Resilienza
Prin 2022 PNRR
Dipartimento di Scienze Medico-Veterinarie
Print

Details

ERC sector
LS9 - Biotechnology and Biosystems Engineering
ERC subsector
LS9_10 - Veterinary and applied animal sciences
Project start date
CUP
D53D23021920001
Financial support received
€84.258,00

Description and purpose

The dairy sector aims at higher standards of quality, welfare and safety to reduce its impact on health and the environment. Preserving biodiversity is key to face climate change and resource scarcity. This project combines milk phenotypes with Next Generation sequencing of bulk milk to develop cost-effective pipelines providing data on health, safety and traceability, improving farming and biodiversity conservation.

Website: https://site.unibo.it/deepmilk/en/the-project 
 

Purpose

DeepMilk aims to develop a monitoring method for dairy production by integrating routine techniques with advanced technologies: milk phenotype analysis for health, cheesemaking quality and nutrition, and genomic analyses to assess genetic variability and breed conservation. The goal is an easily farm-implementable approach, tested on small native breeds like Reggiana and cosmopolitan ones like Italian Holstein.

Expected results

Field-applicable milk-based protocols and pipelines will enable cost-effective early diagnosis, mastitis management, microbiological control, and genetic data handling for breed monitoring and product quality. The project will identify markers for conservation of local breeds, herd management, resilience and disease resistance, as well as for dairy product traceability and authentication.

Achieved results

Milk, somatic and differential cell counts and cheese-related traits were analyzed; multivariate methods (PCA, PCoA, hierarchical clustering, t-SNE) were applied to assess variability, detect sample patterns and design representative pools from bulk milk. These approaches link milk properties to processing quality and provide practical tools for herd health monitoring and the valorization of dairy products.

Modified on