Erik Sonnhammer

Key publications

“A Novel Method for Crosstalk Analysis of Biological Networks: Improving accuracy of Pathway Annotation”, Ogris C, Guala D, Helleday T, Sonnhammer ELL., Nucleic Acids Res., DOI:10.1093/nar/gkw849 (2016)

“Avoiding pitfalls in L1-regularised inference of gene networks”, Andreas Tjärnberg, Torbjörn E.M. Nordling, Matthew Studham, Sven Nelander and Erik L. L. Sonnhammer, Mol. BioSyst. 11:287-296 (2015)

“InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic”, Erik L.L.Sonnhammer and Gabriel Östlund, Nucleic Acids Res. 43:D234-D239 (2015)

“MaxLink: network-based prioritization of genes tightly linked to a disease seed set”, Dimitri Guala, Erik Sjölund and Erik L.L. Sonnhammer, Bioinformatics 30:2689-2690 (2014)

FunCoup 3.0: database of genome-wide functional coupling networks, Thomas Schmitt, Christoph Ogris, Erik L.L.Sonnhammer, Nucleic Acids Res. 42:D380-8 (2014)

Research interests

Systems biology: We are developing new methods to make regulatory network inference robust and reliable. The accuracy of regression-based methods depend strongly on a sparsity parameter and we have developed a system to optimise this. We are using the SciLifeLab units to perform perturbation experiments by inhibiting gene expression in cell lines and measuring the transcriptomic response. This pipeline is applied to unravel gene regulatory networks implicated in cancer.

Network biology: The FunCoup system builds networks of functionally coupled genes/proteins by integrating data of many types, including microarray mRNA and protein expression, tandem affinity purification, colocalisation, regulatory signals, and evolutionary patterns. Functional coupling observed in one species is transferred using the orthology relations in InParanoid. The data sources are integrated by Bayesian training using up to four gold standard training sets per species, each conveying a different kind of functional link. FunCoup is designed such that multiple evidences for functional coupling add to the total score and give higher confidence scores. The website is the only one that supports ”comparative genomics”, i.e. quering of conserved subnetworks in multiple species (see figure). We are furthermore developing several network analysis tools to identify network modules, measure gene group cross-talk, and to rank new candidate members of a group. The latter was applied to cancer and predicted nearly 2000 novel cancer genes (Östlund et al., 2010).

Orthology identification: we are developing the InParanoid method and database, which is an efficent way to find high-quality orthologs between two species.  This algorithm is being upgraded to support multiple species in a hierarchical manner.

Protein domain architecture analysis: We are developing various tools to analyse the evolution of protein domain architectures based on the Pfam database.

Group members

Dimitri Guala, Senior Scientist
Shohag Barman, Postdoc
Mateusz Garbulowski, Postdoc
Thomas Hillerton, PhD student
Emma Persson, PhD student
(Erik) Korvin Zhivkoplias, PhD student
Davide Buzzao, PhD student


Last updated: 2022-11-30

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