Edinburgh Genomics

About EdGe

Edinburgh Genomics is embedded in the rich academic environment of the School of Biological Sciences at University of Edinburgh, spanning medical, veterinary and biological realms.

The EdGe staff comprises highly skilled and motivated individuals who are dedicated to delivering high quality outputs in collaboration with colleagues across the spectrum of the genome science.

Management Team

  • Matt Arno- Laboratory Team Lead
  • Donald Dunbar - Bioinformatics Team lead
  • Javier Santoyo-Lopez - Service Manager

Laboratory Team

The EdGe lab team has many years of experience across the molecular biology and genomics, including library preparation and quality control; sequencing; and automation. The team is led by Matt Arno and comprises:

  • Robert Foster
  • Liz Heap
  • Stewart Laing
  • Marian Thomson
  • Ana Vieira

Bioinformatics Team

The EdGe bioinformatics team's expertise is in analysis and programming and covers all our data quality control; generation and maintenance of pipelines; and data analysis and delivery. The team is led by Donald Dunbar and comprises:

  • Tim Booth
  • Nathan Medd
  • Urmi Trivedi
  • Frances Turner


Acknowledging EdGe

Acknowledging the contribution of our research facilities provides a mechanism to demonstrate their success thus increasing their visibility internally and externally. In turn, this generates additional income to the School through external collaborations, it raises our profile within the UK and internationally, and it strengthens our funding applications and REF cases.

Please routinely note the contributions made by research facilities and staff in all publications.

Please communicate with, and acknowledge, specific facility staff if they were involved in performing experiments or provided more involved training/advice/work for you. Please make sure you consider co-authorship if their contributions go beyond this.

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Sequencing (and/or Bioinformatics analysis) was carried out by Edinburgh Genomics, the University of Edinburgh, which is partly supported with core funding from NERC (UKSBS PR18037).