Ian M. Sudbery
Molecular Biology · 2014–2017
A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 73 citations into it; #12,197 of 17,773 by trust here, #10,193 by citations
Whom Ian M. Sudbery trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Katherine S. Pollard (in Molecular Biology)
- Rebecca M. Truty (in Molecular Biology)
- Sean Whalen (in Molecular Biology)
Who trusts Ian M. Sudbery
- Johan L. M. Björkegren (in Molecular Biology)
- Li-Ming Gan (in Molecular Biology)
- Oscar Franzén (in Molecular Biology)
Papers from Ian M. Sudbery's perspective
Ordered by how strongly the researchers Ian M. Sudbery trusts endorse each paper. Same corpus, a different order for every researcher.
- Quantitative single-cell RNA-seq with unique molecular identifiers (Nature Methods)
- Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)
- Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin (Nature Genetics)
- Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
- Mapping and quantifying mammalian transcriptomes by RNA-Seq (Nature Methods)
- Massively parallel single cell RNA-Seq for marker-free decomposition of tissues into cell types (Science)
- Genome-Wide Mapping of in Vivo Protein-DNA Interactions (Science)
- Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. (Genome Research)
- Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex (Nature Biotechnology)
- Identification of genes periodically expressed in the human cell cycle and their expression in tumors. (Molecular Biology of the Cell)
Their papers
- Sequencing depth and coverage: key considerations in genomic analyses (2014) — 1,328 citations
- UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2016) — 1,542 citations
- UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy (2017) — 368 citations