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Martin Hemberg (Associate)

2017 HembergMartin Hemberg PhD, Associate Group Leader at the Gurdon Institute and Career Development Fellow Group Leader at the Wellcome Sanger Institute.

Europe PMC Pubmed




Computational analyses of large genomic datasets

What can sequencing data tell us about disease? To create the different cell types in an organism, different genes are expressed at different times as transcripts of RNA. Understanding how, why, when and where genes are expressed is crucial for understanding not just development but also many diseases.

High-throughput sequencing of RNA from different tissues can now provide quantitative information about gene expression from individual cells. However, the experimental datasets are large, high-dimensional and noisy, and efficient computational methods are required for the analysis. 

Our group uses computational and mathematical methods to develop quantitative models of gene expression and gene regulation. In particular, we are exploring single-cell RNA sequencing, which can reveal insights that are inaccessible through traditional bulk experiments; for example, to estimate the number of differentiated cell types in the body. Another strand of research aims to further our understanding of gene regulation and to understand how non-coding sequences determine gene expression levels.

Selected publications:

• Kiselev VY, Andrews TS, Hemberg M.(2019) Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet. May;20(5): 273-282. doi:10.1038/s41576-018-0088-9.

• Westoby J, Herrera MS, Ferguson-Smith AC, Hemberg M. (2018) Simulation-based benchmarking of isoform quantification in single-cell RNA-seq. Genome Biol. Nov 7;19(1):191. doi: 10.1186/s13059-018-1571-5.

• Georgakopoulos-Soares I, Morganella S, Jain N, Hemberg M, Nik-Zainal S. (2018) Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis. Genome Res. Sep;28(9):1264-1271. doi: 10.1101/gr.231688.117.

• Kiselev VY, Yiu A, Hemberg M. (2018) scmap: projection of single-cell RNA-seq data across data sets. Nat Methods. May;15(5):359-362. doi: 10.1038/nmeth.4644.

• Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg M. (2017) SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. May;14(5):483-486. doi: 10.1038/nmeth.4236.




Jacob Hepkema • Jimmy (Tsz Hang) Lee • Nicholas Keone Lee

Based at Wellcome Sanger Institute: Tallulah Andrews • Ilias Georgakopolous-Soares • Louis-Francois Handfield • Guillermo Parada Gonzalez • Cristian Riccio • Xioajuan Shen