I am a Robertson Fellow in Biomedical Data Science at the Big Data Institute, University of Oxford. I develop algorithms and software to tackle difficult problems in large-scale genomics, integrating with the Python data science stack.
Me at the BDI
- I lead development of tskit, the tree sequence toolkit. Tskit is a Python and C API that allows us to work with genealogical histories at unprecedented scale.
- I also lead development of the msprime coalescent simulator, which is based on tskit, and can simulate of the exact coalescent with recombination over chromosome-sized regions with hundreds of thousands of samples.
- I'm part of the Data Working Group in the Global Alliance for Genomics and Health (GA4GH). I am currently participating in the design and client side implementation of the htsget data access protocol.
- During my postdoc in theoretical population genetics , I worked primarily on a new model for populations evolving in a spatial continuum. During this time I developed two Python simulation packages: one to simulate a general form of the model and another to simulate a more specialised form much more efficiently.
- I found some bugs in TAOCP volume 4A while it was still in beta-test fascicle form. I am therefore a proud saver at the The Bank of San Serriffe!
- Robertson Fellowship in Biomedical Data Science at the Big Data Institute, University of Oxford.
- Senior statistical programmer in the McVean Group at the Big Data Institute (and formerly at the Wellcome Centre for Human Genetics), University of Oxford.
- Postdoc, primarily at the Institute of Evolutionary Biology, University of Edinburgh and also at the Department of Statistics, University of Oxford.
- Seniour Software Architect, MPSTOR.
- PhD Computer Science, University College Cork (thesis).
- BSc Computer Science, University College Cork.
Benjamin C. Haller, Jared Galloway, Jerome Kelleher, Philipp W. Messer and Peter L. Ralph Tree‐sequence recording in SLiM opens new horizons for forward‐time simulation of whole genomes. Molecular Ecology Resources, 19:552–566, 2019. link
Jerome Kelleher, Kevin R. Thornton, Jaime Ashander and Peter L. Ralph Efficient pedigree recording for fast population genetics simulation. PLoS Computational Biology, 14(11): e1006581, 2018. link
Björn Grüning, Ryan Dale, Andreas Sjödin, Brad A. Chapman, Jillian Rowe, Christopher H. Tomkins-Tinch, Renan Valieris, Johannes Köster and Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7):475-476, 2018. link
Jerome Kelleher, Mike Lin, CH Albach, Ewan Birney, Robert Davies, Marina Gourtovaia, David Glazer, Cristina Y Gonzalez, David K Jackson, Aaron Kemp, John Marshall, Andrew Nowak, Alexander Senf, Jaime M Tovar-Corona, Alexander Vikhorev, Thomas M Keane, and GA4GH Streaming Task Team. htsget: a protocol for securely streaming genomic data, Bioinformatics 2018. link
Jerome Kelleher, Alison M. Etheridge and Gilean McVean. Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes. PLoS Computational Biology, Volume 12, May 2016, e1004842. link
Jerome Kelleher, Alison M. Etheridge, Amandine Véber and Nicholas H. Barton. Spread of pedigree versus genetic ancestry in spatially distributed populations. Theoretical Population Biology, Volume 108, April 2016, Pages 1–12. link
Jerome Kelleher, Alison M. Etheridge and Nicholas H. Barton. Coalescent simulation in continuous space: Algorithms for large neighbourhood size. Theoretical Population Biology, Volume 95, August 2014, Pages 13–23. link
Jerome Kelleher, Rob W. Ness and Daniel L. Halligan. Processing genome scale tabular data with wormtable. BMC Bioinformatics, 14:356, 2013. link
Jerome Kelleher, Nicholas H. Barton and Alison M. Etheridge. Coalescent simulation in continuous space. Bioinformatics, Volume 29, Issue 7, pp. 955-956, 2013. link
Nicholas H. Barton, Alison M. Etheridge, Jerome Kelleher and Amandine Véber. Inference in two dimensions: Allele frequencies versus lengths of shared sequence blocks. Theoretical Population Biology, Volume 87, August 2013, Pages 105–119. link
Nicholas H. Barton, Alison M. Etheridge, Jerome Kelleher and Amandine Véber. Genetic hitchhiking in spatially extended populations. Theoretical Population Biology, Volume 87, August 2013, Pages 75–89. link
Nicholas H. Barton, Jerome Kelleher and Alison M. Etheridge. A new model for extinction and recolonisation in two dimensions: quantifying phylogeography. Evolution, Volume 64, Issue 9, pp 2701-2715, 2010. link
Konrad Lohse and Jerome Kelleher. Measuring the degree of starshape in genealogies --- summary statistics and demographic inference. Genetics Research, Volume 91, Issue 04, pp 281-292, 2009. link
William Opperman and Jerome Kelleher. A Data Storage System, International Patent WO/2008/007348, 2008. link
Jerome Kelleher. Encoding Partitions as Ascending Compositions. PhD thesis, University College Cork, 2006. pdf
Jerome Kelleher and Derek Bridge. An Accurate and Scalable Collaborative Recommender. Artificial Intelligence Review, Volume 21, Issue 3-4, pp.193-213, 2004. link
Stefano Bistarelli, Jerome Kelleher and Barry O'Sullivan. Tradeoff Generation using Soft Constraints, Recent Advances in Constraints, Springer, LNAI 3010 2004. link
Jerome Kelleher and Barry O'Sullivan. Evaluation-Based Semiring Meta-Constraints. Proceedings of MICAI, Springer, LNCS 2972 Mexico, 2004. pdf
Jerome Kelleher and Derek Bridge. RecTree Centroid: An Accurate, Scalable Collaborative Recommender. Proceedings of AICS, Trinity College, Dublin, pp.89-94, 2003. pdf
Stefano Bistarelli, Jerome Kelleher and Barry O'Sullivan. Symmetry Breaking in Soft CSPs. Proceedings of AI-2003, Springer, Cambridge, UK, 2003. pdf
Jerome Kelleher and Barry O'Sullivan. Evaluation-Based Semiring Meta-Constraints. Proceedings of AICS-2003, Poster Paper, Dublin, Ireland, 2003.
Jerome Kelleher and Barry O'Sullivan. Optimising the Representation and Evaluation of Semiring Combination Constraints. Principles and Practice of Constraint Programming - CP2003, LNCS, 2003.
Derek Bridge and Jerome Kelleher. Experiments in Sparsity reduction: Using Clustering in Collaborative Recommenders. Proceedings of AICS-2002, LNAI 2464, Springer, pp.144-149, 2002. pdf