Hemberg, Martin

martin.hemberg@sanger.ac.uk

Martin Hemberg is a Career Development Fellow Group Leader and his research interests are centered around quantitative models of gene expression and gene regulation. He is particularly interested in stochastic models and analysis of single-cell data. Another line of research involves analyzing the role of non-coding transcripts and sequences.

In addition to working at the Wellcome Sanger Institute, Martin is also an associate faculty member at the Wellcome Trust/CRUK Gurdon Institute.

Publications

Quantitative profiling of peptides from RNAs classified as noncoding.

Prabakaran S, Hemberg M, Chauhan R, Winter D, Tweedie-Cullen RY et al.

Nature communications 2014;5;5429

PUBMED: 25403355; PMC: 4416701; DOI: 10.1038/ncomms6429
Widespread transcription at neuronal activity-regulated enhancers.

Kim TK, Hemberg M, Gray JM, Costa AM, Bear DM et al.

Nature 2010;465;7295;182-7

PUBMED: 20393465; PMC: 3020079; DOI: 10.1038/nature09033
Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

Chang HH, Hemberg M, Barahona M, Ingber DE and Huang S

Nature 2008;453;7194;544-7

PUBMED: 18497826; PMC: 5546414; DOI: 10.1038/nature06965
Perfect sampling of the master equation for gene regulatory networks.

Hemberg M and Barahona M

Biophysical journal 2007;93;2;401-10

PUBMED: 17468171; PMC: 1896223; DOI: 10.1529/biophysj.106.099390
Stochastic kinetics of viral capsid assembly based on detailed protein structures.

Hemberg M, Yaliraki SN and Barahona M

Biophysical journal 2006;90;9;3029-42

PUBMED: 16473916; PMC: 1432130; DOI: 10.1529/biophysj.105.076737

Supervised clustering for single-cell analysis.

Lee JTH and Hemberg M

Nature methods 2019;16;10;965-966

PUBMED: 31501544; DOI: 10.1038/s41592-019-0534-4
The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle.

Howick VM, Russell AJC, Andrews T, Heaton H, Reid AJ et al.

Science (New York, N.Y.) 2019;365;6455

PUBMED: 31439762; DOI: 10.1126/science.aaw2619
M3Drop: dropout-based feature selection for scRNASeq.

Andrews TS and Hemberg M

Bioinformatics (Oxford, England) 2019;35;16;2865-2867

PUBMED: 30590489; PMC: 6691329; DOI: 10.1093/bioinformatics/bty1044; URL:
Challenges in unsupervised clustering of single-cell RNA-seq data.

Kiselev VY, Andrews TS and Hemberg M

Nature reviews. Genetics 2019;20;5;273-282

PUBMED: 30617341; DOI: 10.1038/s41576-018-0088-9

Simulation-based benchmarking of isoform quantification in single-cell RNA-seq.

Westoby J, Herrera MS, Ferguson-Smith AC and Hemberg M

Genome biology 2018;19;1;191

PUBMED: 30404663; DOI: 10.1186/s13059-018-1571-5
Single-cell genomics.

Hemberg M

Briefings in functional genomics 2018;17;4;207-208

PUBMED: 30060035; DOI: 10.1093/bfgp/ely025
scmap: projection of single-cell RNA-seq data across data sets.

Kiselev VY, Yiu A and Hemberg M

Nature methods 2018;15;5;359-362

PUBMED: 29608555; DOI: 10.1038/nmeth.4644
Placentation defects are highly prevalent in embryonic lethal mouse mutants.

Perez-Garcia V, Fineberg E, Wilson R, Murray A, Mazzeo CI et al.

Nature 2018;555;7697;463-468

PUBMED: 29539633; PMC: 5866719; DOI: 10.1038/nature26002
Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis.

Bergiers I, Andrews T, Vargel Bölükbaşı Ö, Buness A, Janosz E et al.

eLife 2018;7

PUBMED: 29555020; PMC: 5860872; DOI: 10.7554/eLife.29312
Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci.

Amaral PP, Leonardi T, Han N, Viré E, Gascoigne DK et al.

Genome biology 2018;19;1;32

PUBMED: 29540241; DOI: 10.1186/s13059-018-1405-5
False signals induced by single-cell imputation.

Andrews TS and Hemberg M

F1000Research 2018;7;1740

PUBMED: 30906525; PMC: 6415334; DOI: 10.12688/f1000research.16613.2
Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis

Ilias Georgakopoulos-Soares, Sandro Morganella, Naman Jain, Martin Hemberg and Serena Nik-Zainal

Genome Research 2018;28;9;1264

DOI: 10.1101/gr.231688.117; URL: http://genome.cshlp.org/content/28/9/1264

Dr Martin Hemberg, PhD | CDF Group Leader

Hemberg, Martin

Publications

Quantitative profiling of peptides from RNAs classified as noncoding.

Widespread transcription at neuronal activity-regulated enhancers.

Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

Perfect sampling of the master equation for gene regulatory networks.

Stochastic kinetics of viral capsid assembly based on detailed protein structures.

Supervised clustering for single-cell analysis.

The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle.

M3Drop: dropout-based feature selection for scRNASeq.

Challenges in unsupervised clustering of single-cell RNA-seq data.

Simulation-based benchmarking of isoform quantification in single-cell RNA-seq.

Single-cell genomics.

scmap: projection of single-cell RNA-seq data across data sets.

Placentation defects are highly prevalent in embryonic lethal mouse mutants.

Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis.

Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci.

False signals induced by single-cell imputation.

Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis

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