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.
Quantitative profiling of peptides from RNAs classified as noncoding.
Nature communications 2014;5;5429
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.
Biophysical journal 2007;93;2;401-10
Stochastic kinetics of viral capsid assembly based on detailed protein structures.
Biophysical journal 2006;90;9;3029-42
Challenges in unsupervised clustering of single-cell RNA-seq data.
Nature reviews. Genetics 2019
M3Drop: Dropout-based feature selection for scRNASeq.
Bioinformatics (Oxford, England) 2018
Simulation-based benchmarking of isoform quantification in single-cell RNA-seq.
Genome biology 2018;19;1;191
Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis.
Genome research 2018
Briefings in functional genomics 2018;17;4;207-208
scmap: projection of single-cell RNA-seq data across data sets.
Nature methods 2018;15;5;359-362
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.
Genome biology 2018;19;1;32
False signals induced by single-cell imputation.