Author

Tim Connelley

Roslin Institute

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 3 Approach:deep learning· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Assay:DNA methylation· 1 Organism:human· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Birkir ReynissoniD· 2 shared Morten NielseniD· 2 shared Nicola TernetteiD· 2 shared Søren BuusiD· 2 shared Andrea TalentiiD· 1 shared Andressa FischiD· 1 shared Annalisa NicastriiD· 1 shared Beatriz Rossetti FerreiraiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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2 shared papersBioinformatics

Birkir ReynissonORCID

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3 papers

Recent·Most cited

A high-resolution atlas of cattle regulatory variants and their cross-species activity in matched human cells

Rongrong Zhao, Lindsey J. Plenderleith, Tanmay Debnath, Rachel Owen, Ludo Pagie, Vartika Bisht, Carey L. Metheringham, Melissa M. Marr, Jessica Powell, Andrea Talenti, Chumeng Zhu, Edith A. Paxton, Kirsty Jensen, Joris Arensbergen, Tim Connelley, , James G. D. Prendergast

The paper creates a high-resolution cattle regulatory variant atlas and identifies cross-species activity in human cells, highlighting both conservation and species-specific effects.

bioRxiv · May 7, 2026 · PreprintGenetics

Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR-presented peptides and epitopes

Andressa Fisch, Birkir Reynisson, Lindert Benedictus, Annalisa Nicastri, Deepali Vasoya, Ivan W. Morrison, Søren Buus, Beatriz Rossetti Ferreira, Isabel Kinney Ferreira de Miranda Santos, Nicola Ternette, Tim Connelley, Morten Nielsen

The paper integrates immunopeptidomics and immunoinformatics to characterize BoLA-DR antigen presentation and identify CD4 T cell epitopes with high predictive accuracy.

The Journal of Immunology · Mar 31, 2021Peer-reviewedBioinformatics

NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T cell epitope predictions

Bruno Alvarez, Birkir Reynisson, Carolina Barra, Søren Buus, Nicola Ternette, Tim Connelley, Massimo Andreatta, Morten Nielsen

NNAlign_MA deconvolves poly-specific MHC peptidome data to accurately characterize binding motifs and improve T cell epitope predictions.

Molecular & Cellular Proteomics · Oct 2, 2019Peer-reviewedBioinformatics