Hemberg, Martin
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
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Quantitative profiling of peptides from RNAs classified as noncoding.
Nature communications 2014;5;5429
PUBMED: 25403355; PMC: 4416701; DOI: 10.1038/ncomms6429
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Widespread transcription at neuronal activity-regulated enhancers.
Nature 2010;465;7295;182-7
PUBMED: 20393465; PMC: 3020079; DOI: 10.1038/nature09033
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Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.
Nature 2008;453;7194;544-7
PUBMED: 18497826; PMC: 5546414; DOI: 10.1038/nature06965
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Perfect sampling of the master equation for gene regulatory networks.
Biophysical journal 2007;93;2;401-10
PUBMED: 17468171; PMC: 1896223; DOI: 10.1529/biophysj.106.099390
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Stochastic kinetics of viral capsid assembly based on detailed protein structures.
Biophysical journal 2006;90;9;3029-42
PUBMED: 16473916; PMC: 1432130; DOI: 10.1529/biophysj.105.076737
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Challenges in unsupervised clustering of single-cell RNA-seq data.
Nature reviews. Genetics 2019
PUBMED: 30617341; DOI: 10.1038/s41576-018-0088-9
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M3Drop: Dropout-based feature selection for scRNASeq.
Bioinformatics (Oxford, England) 2018
PUBMED: 30590489; DOI: 10.1093/bioinformatics/bty1044
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Simulation-based benchmarking of isoform quantification in single-cell RNA-seq.
Genome biology 2018;19;1;191
PUBMED: 30404663; DOI: 10.1186/s13059-018-1571-5
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Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis.
Genome research 2018
PUBMED: 30104284; DOI: 10.1101/gr.231688.117
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Single-cell genomics.
Briefings in functional genomics 2018;17;4;207-208
PUBMED: 30060035; DOI: 10.1093/bfgp/ely025
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scmap: projection of single-cell RNA-seq data across data sets.
Nature methods 2018;15;5;359-362
PUBMED: 29608555; DOI: 10.1038/nmeth.4644
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Placentation defects are highly prevalent in embryonic lethal mouse mutants.
Nature 2018;555;7697;463-468
PUBMED: 29539633; PMC: 5866719; DOI: 10.1038/nature26002
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Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis.
eLife 2018;7
PUBMED: 29555020; PMC: 5860872; DOI: 10.7554/eLife.29312
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Genomic positional conservation identifies topological anchor point RNAs linked to developmental loci.
Genome biology 2018;19;1;32
PUBMED: 29540241; DOI: 10.1186/s13059-018-1405-5