Susanne Bornelöv "sblab"

bioinformatics, (epi)genomics, machine learning, piRNAs, codon usage

Welcome!


Image credit: DALL-E

Here, you will find information about my group and my research programme titled "Data-driven methods to unravel hidden layers of genome regulation", which I launched in September 2023. I am grateful to have received support from Wellcome Trust though a Wellcome Career Development Award, which will enable me to explore novel aspects of gene regulation genome organisation through computational approaches.

In addition to my own research programme, I am also partially engaged in ongoing research as a member of the Hannon lab at the Cancer Research UK Cambridge Institute, University of Cambridge.

We are recruiting! Please find more details about joining us here.

Dr Susanne Bornelöv
Wellcome Career Development Award fellow
Cancer Research UK Cambridge Institute, University of Cambridge
Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, United Kingdom

News

28 May-2 June, 2024
Susanne is attending RNA 2024 in Edinburgh, UK and presenting some of our deep learning work.

24-27 May, 2024
Chenge, Tirtharaj and Susanne are attending the The 2nd Workshop on Codon Usage in Edinburgh, UK, and Chenge is presenting her work.

24 May, 2024
We are recruiting a postdoc, please see advert here!

15 May, 2024
A new preprint "FlaHMM: unistrand flamenco-like piRNA cluster prediction in Drosophila species using hidden Markov models" describing a study started by Maria-Anna Trapotsi in the Hannon lab is now out on bioRxiv.

4 April, 2024
Susanne presented a poster at the piRNAs and PIWI protein conference in Montpellier, France.

21 March, 2024
Our journal club article "A code within the genetic code" describing the discovery of codon-optimality-mediated mRNA degradation by Jeff Coller's lab is now out in Nature Review Molecular Cell Biology.

20 March, 2024
We have a 1-year Research Assistant position in "Deep learning for Genomics" available. Please see the advert. Closing date 5 April.

12 January, 2024
A new preprint "A dual histone code specifies the binding of heterochromatin protein Rhino to a subset of piRNA source loci" describing a study done with the Hannon lab is now out on bioRxiv.

13 November, 2023
Our paper "Unistrand piRNA clusters are an evolutionarily conserved mechanism to suppress endogenous retroviruses across the Drosophila genus" is now out in Nature Communications!

9 November, 2023
Anna-Maria presents a poster describing her and Maliha's work "Deep learning enables in silico studies of piRNA biogenesis" at EMBO The Mobile Genome in Heidelberg, Germany.

2 November, 2023
Toby Clark joined our team to do a Master's project with us.

30 October, 2023
Tirtharaj Dash joined our team as a Research Associate.

12 October, 2023
Susanne presented "Unistrand piRNA clusters have repeatedly emerged across the Drosophila genus to control endogenous retroviruses of the Gypsy family" at the EMBO The Non-Coding Genome in Heidelberg, Germany.

2 October, 2023
Chenge (Joanna) Du joined our team as a Research Assistant.

19 September, 2023
We are doing another round of recruitment to fill our final position! The advert is live here.

7 September, 2023
Susanne presented "Predicting gene expression from random promoter sequences using deep learning" at the Cambridge AI Club.

1 September, 2023
Excited to announce the first official day of our new lab!!

10 July, 2023
Peter Sabakaki joined us for an eight-week summer project supported by the Undergraduate summer project programme and Cambridge Africa.

1 May, 2023
A preprint "Evaluation and optimization of sequence-based gene regulatory deep learning models" is now available on bioRxiv. We particiated as team Camformers (Random Promoter DREAM Challenge Consortium) and our model took 4th place.

23 April, 2023
We are recruiting! Our adverts are now live here, here, and here.

27 February, 2023
A new preprint "Unistrand piRNA clusters are an evolutionary conserved mechanism to suppress endogenous retroviruses across the Drosophila genus" is available on bioRxiv