# Cole Trapnell
**Cole Trapnell** is a computational biologist and Professor of Genome Sciences at the University of Washington, known for developing widely used RNA-seq analysis tools (TopHat, Cufflinks), and the Monocle single-cell trajectory-inference program.[1](https://www.gs.washington.edu/about/directory/faculty/cole-trapnell/) • [2](https://cole-trapnell-lab.github.io/team/cole-trapnell/)
| Key fact | Detail |
|---|---|
| Position | Professor of Genome Sciences, University of Washington; Adjunct Professor of Computer Science & Engineering; Co-Director of the Center for the Multiplexed Assessment of Phenotype[1](https://www.gs.washington.edu/about/directory/faculty/cole-trapnell/) |
| Signature work | Monocle trajectory inference (Nature Biotechnology, 2014); Cufflinks transcript assembly (Nature Biotechnology, 2010)[4](https://www.nature.com/articles/nbt.2859) • [1](https://www.gs.washington.edu/about/directory/faculty/cole-trapnell/) |
| Other roles | Co-Director, Seattle Hub for Synthetic Biology (announced December 7, 2023)[5](https://brotmanbaty.org/news/q-and-a-with-dr-cole-trapnell-on-seahub) |
## Education and career
Trapnell completed his Ph.D. in Computer Science at the [University of Maryland, College Park](https://www.edgepedia.org/university-of-maryland-college-park) in 2010, advised by Professor Steven Salzberg with [Lior Pachter](https://www.edgepedia.org/lior-pachter) of the [California Institute of Technology](https://www.edgepedia.org/california-institute-of-technology) as co-advisor; his dissertation was titled *Transcript Assembly and Abundance Estimation with High-Throughput RNA Sequencing*.[3](https://cole-trapnell-lab.github.io/pdfs/team/cole-trapnell-cv.pdf) • [6](https://api.drum.lib.umd.edu/server/api/core/bitstreams/c9a61a8d-72d7-4190-9f59-9b908136527b/content)
Since December 7, 2023, he has co-led the Seattle Hub for Synthetic Biology, a project of UW Medicine, the Allen Institute, and the [Chan Zuckerberg Initiative](https://www.edgepedia.org/chan-zuckerberg-initiative) aimed at understanding how the genome encodes the program of development.[5](https://brotmanbaty.org/news/q-and-a-with-dr-cole-trapnell-on-seahub) • [7](https://alleninstitute.org/seattle-hub-for-synthetic-biology/trapnell-lab)
## RNA-seq tools: TopHat and Cufflinks
As a graduate student Trapnell wrote TopHat and Cufflinks, two widely used tools for transcriptome sequencing (RNA-seq) analysis, and helped develop the Bowtie read-mapping program.[2](https://cole-trapnell-lab.github.io/team/cole-trapnell/) • [7](https://alleninstitute.org/seattle-hub-for-synthetic-biology/trapnell-lab) Cufflinks, published in *Nature Biotechnology* in 2010, assembles transcripts and estimates their abundances, and applied to a model of muscle differentiation revealed thousands of new transcripts and switching among isoforms during cell differentiation.[6](https://api.drum.lib.umd.edu/server/api/core/bitstreams/c9a61a8d-72d7-4190-9f59-9b908136527b/content) • [1](https://www.gs.washington.edu/about/directory/faculty/cole-trapnell/) His review articles include *Computational methods for transcriptome annotation and quantification using RNA-seq* ([Nature Methods](https://www.edgepedia.org/nature-methods), 2011)[8](https://doi.org/10.1038/nmeth.1613) and, according to *Genome Research*, the 2015 review *Defining cell types and states with single-cell genomics*.[9](https://doi.org/10.1101/gr.190595.115)
## Monocle and trajectory inference
Near the end of his Harvard postdoc, Trapnell developed Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points.[2](https://cole-trapnell-lab.github.io/team/cole-trapnell/) • [4](https://www.nature.com/articles/nbt.2859) The method introduced pseudotime, a quantitative measure of biological progression through a process such as cell differentiation: Monocle reduces the dimensionality of expression data, then reconstructs a trajectory along which cells are projected.[10](https://pubmed.ncbi.nlm.nih.gov/26430159/)
Applied to differentiating primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and regulators not previously known to act in differentiation, some of which were validated in a loss-of-function screen.[4](https://www.nature.com/articles/nbt.2859) Monocle went through three major versions; Trapnell is principal developer of Monocle 3, an open-source R program that anyone can download for free.[11](https://assets.illumina.com/science/customer-stories/icommunity-customer-interviews-case-studies/trapnell-uwash-interview-single-cell.html)
## The lab's program since 2023
The Trapnell Lab develops technologies that let biologists study gene regulation and cell-cell communication in development and disease, and has applied its tools to pancreatic islet development, olfactory neurogenesis, thyroid hormone-dependent pigmentation, and zebrafish embryonic development, aiming to build a quantitative map of genetic requirements in zebrafish.[2](https://cole-trapnell-lab.github.io/team/cole-trapnell/) • [7](https://alleninstitute.org/seattle-hub-for-synthetic-biology/trapnell-lab) Recent work includes embryo-scale single-cell spatial transcriptomics, embryo-scale reverse genetics at single-cell resolution, and a paper titled "Proteostasis governs differential temperature sensitivity across embryonic cell types".[2](https://cole-trapnell-lab.github.io/team/cole-trapnell/)
A 2025 preprint describes two software tools, Hooke and Platt, which exploit statistical patterns within single-cell datasets of millions of cells from thousands of specimens to characterize the consequences of experimental perturbations and infer genetic requirements.[12](https://www.biorxiv.org/content/10.1101/2025.04.03.646654v1.full-text) Applied to a single-cell atlas of thousands of perturbed zebrafish embryos, they produced a map of lineage dependencies and revealed previously unappreciated roles for fate-determining transcription factors.[12](https://www.biorxiv.org/content/10.1101/2025.04.03.646654v1.full-text) The lab also distributes Hooke for differential analysis of cell abundances using Poisson-Lognormal models, Cicero for single-cell chromatin accessibility, and Monocle for gene expression.[7](https://alleninstitute.org/seattle-hub-for-synthetic-biology/trapnell-lab)
## Representative work
- **"Computational methods for transcriptome annotation and quantification using RNA-seq"**, *Nature Methods* (2011), [doi:10.1038/nmeth.1613](https://doi.org/10.1038/nmeth.1613).
- **"Defining cell types and states with single-cell genomics"**, *Genome Research* (2015), [doi:10.1101/gr.190595.115](https://doi.org/10.1101/gr.190595.115).
## References
1. [Cole Trapnell, UW Genome Sciences faculty directory](https://www.gs.washington.edu/about/directory/faculty/cole-trapnell/)
2. [Cole Trapnell, lab biography](https://cole-trapnell-lab.github.io/team/cole-trapnell/)
3. [Cole Trapnell, Curriculum Vitae](https://cole-trapnell-lab.github.io/pdfs/team/cole-trapnell-cv.pdf)
4. [The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells (Nature Biotechnology, 2014)](https://www.nature.com/articles/nbt.2859)
5. [Q&A with Dr. Cole Trapnell on the Seattle Hub for Synthetic Biology (Brotman Baty Institute)](https://brotmanbaty.org/news/q-and-a-with-dr-cole-trapnell-on-seahub)
6. [Transcript Assembly and Abundance Estimation with High-Throughput RNA Sequencing (PhD dissertation, 2010)](https://api.drum.lib.umd.edu/server/api/core/bitstreams/c9a61a8d-72d7-4190-9f59-9b908136527b/content)
7. [Trapnell Lab, Allen Institute, Seattle Hub for Synthetic Biology](https://alleninstitute.org/seattle-hub-for-synthetic-biology/trapnell-lab)
8. [Computational methods for transcriptome annotation and quantification using RNA-seq (Nature Methods, 2011)](https://doi.org/10.1038/nmeth.1613)
9. [Defining cell types and states with single-cell genomics (Genome Research, 2015)](https://doi.org/10.1101/gr.190595.115)
10. [Defining cell types and states with single-cell genomics (PubMed record)](https://pubmed.ncbi.nlm.nih.gov/26430159/)
11. [Single-cell analysis in developmental biology (Illumina interview)](https://assets.illumina.com/science/customer-stories/icommunity-customer-interviews-case-studies/trapnell-uwash-interview-single-cell.html)
12. [A statistical framework for inferring genetic requirements from embryo-scale single-cell sequencing experiments (bioRxiv, 2025)](https://www.biorxiv.org/content/10.1101/2025.04.03.646654v1.full-text)
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*Topic: Encyclopedia › Life and health › Life and health scientists › Life scientists › Researchers in computational biology, bioinformatics, and systems biology › Single-cell and spatial omics*
*Initially written Sep 20, 2026 · Reviewed: Sep 25, 2026 · Edited: Sep 25, 2026; Oct 15, 2026 · Last review: Sep 25, 2026*
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License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license