Statistical genomics:



• A popular article Bioinformatics: new science - new statistics? published in Significance, 2004
  
• A short course on Statistical Bioinformatics presented at the RSS 2006 conference, Belfast
• Co-organiser of Stochastic Computation in the Biological Sciences at the Isaac Newton Institute for Mathematical Sciences, including a workshop on Recent Advances in Statistical Genetics and Bioinformatics, Oct-Dec 2006
  

Monte Carlo methods in statistical inference:



• Fortran and C code for Adaptive Rejection Sampling from complex univariate distributions
  
• Book on Markov Chain Monte Carlo in Practice by W.R. Gilks, S. Richardson and D.J. Spiegelhalter
  

Downloads

• Code for Adaptive Rejection Sampling from complex univariate distributions, written in Fortran and C
  
• Code for computing a metric to compare multiple DNA alignments or RNA structured alignments, written in C#
  
• An R package SDSparsimony for detecting Specificity Determining Sites in multiple sequence alignments
  
• An R package linkage3p for assessing the quality of a genetic linkage map
  

Previous Research at the Medical Research Council Biostatistics Unit

• 2000-2006
  
• 1994-1999
  

General bioinformatics

• Walter R. Gilks. Editorial: Special Issue on Statistical Bioinformatics. Statistical Methods in Medical Research, 2009, 18(5), 435-436.
• Walter R. Gilks. Bioinformatics - new science, new statistics? Significance, 2004.

Phylogenetics

• Walter R. Gilks and Chinying Wang. A non-parametric method for detecting specificity determining sites in protein sequence alignments. Statistical Applications in Genetics and Molecular Biology, 2011, 10, Art. 20, 1-31.
• Walter R. Gilks, Tom M. W. Nye and Pietro Liò. A variance-components model for distance-matrix phylogenetic reconstruction. Statistical Applications in Genetics and Molecular Biology, 2011, 10, Art. 16, 1-33.
• Kerstin Hommola, Judith E. Smith, Yang Qiu and Walter R. Gilks. A permutation test of host-parasite cospeciation. Molecular Biology and Evolution, 2009, 26(7), 1457-1468.
• Tom M. W. Nye, Brad J.C. Baxter and Walter R. Gilks. A covariance matrix inversion problem arising from the construction of phylogenetic trees. London Mathematical Society Journal of Computation and Mathematics, 2007, 10, 119-131.
• Tom M. W. Nye, Pietro Liò, and Walter R. Gilks. A novel algorithm and web-based tool for comparing two alternative phylogenetic trees. Bioinformatics, 2006, 22, 117-119.

Protein structure

• Kerstin Hommola, Walter R. Gilks and Kanti V. Mardia. Log-linear modelling of protein dipeptide structure reveals interesting patterns of side-chain - backbone interactions. Statistical Applications in Genetics and Molecular Biology, 2011, 10, Art. 8, 1-27.

Protein interactions

• Tom M. W. Nye, Carlo Berzuini, Walter R. Gilks, M. Madan Babu and Sarah Teichmann. Predicting the strongest domain-domain contact in interacting protein pairs. Statistical Applications in Genetics and Molecular Biology, 2006, 5, Art. 5, 1-17.
• Tom M. W. Nye, Carlo Berzuini, Walter R. Gilks, M. Madan Babu and Sarah A. Teichmann. Statistical analysis of domains in interacting protein pairs. Bioinformatics, 2005, 21, 991-1001.

Highly conserved non-coding DNA

• Irina Abnizova, Klaudia Walter, Rene te Boekhorst, Greg Elgar and Walter R. Gilks. Statistical information characterization of conserved non-coding elements in vertebrates. Journal of Bioinformatics and Computational Biology , 2007, 5(2B), 533-547.
• Tanya Vavouri, Klaudia Walter, Walter R. Gilks, Ben Lehner and Greg Elgar. Parallel evolution of conserved noncoding elements that target a common set of developmental regulatory genes from worms to humans. Genome Biology, 2007, 8(2), R15.
• Tanya Vavouri, Gayle K. McEwen, Adam Woolfe, Walter R. Gilks and Greg Elgar. Defining a genomic radius for long-range enhancer action: duplicated conserved non-coding elements hold the key. Trends in Genetics, 2006, 22, 5-10.
• Klaudia Walter, Irina Abnizova, Greg Elgar and Walter R. Gilks. Striking nucleotide frequency pattern at the borders of highly conserved vertebrate non-coding sequences.. Trends in Genetics, 2005, 21, 436-440.

Regulatory DNA

• Irina Abnizova and Walter R. Gilks. Studying statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the eukaryotic genomes. Briefings in Bioinformatics, 2006, 7, 48-54.
• Irina Abnizova, Alistair G. Rust, Mark Robinson, Rene te Boekhorst and Walter R. Gilks. Transcription binding site prediction using Markov models. Journal of Bioinformatics and Computational Biology, 2006, 4, 425-441.
• Irina Abnizova, Rene te Boekhorst, Klaudia Walter and Walter R. Gilks. Some statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the Drosophila genome: the fluffy-tail test. BMC Bioinformatics , 2005, 6, 109.

Database annotation errors

• Walter R. Gilks, Benjamin Audit, Daniela de Angelis, Sophia Tsoka, Christos A. Ouzounis. Percolation of annotation errors through hierarchically structured protein sequence databases. Mathematical Biosciences , 2005, 193, 223-234.
• Emmanuel D. Levy, Christos A. Ouzounis, Walter R. Gilks and Benjamin Audit. Probabilistic annotation of protein sequences based on functional classifications. BMC Bioinformatics , 2005, 6, 302.

Microarrays

• Walter R. Gilks, Brian D. M. Tom and Alvis Brazma. Fusing microarray experiments with multivariate regression. Bioinformatics , 2005, 21, ii137 - ii143.
• Brian D. M. Tom, Walter R. Gilks, Elizabeth T. Brooke-Powell and James W. Ajioka. Quality determination and the repair of poor quality spots in array experiments. BMC Bioinformatics , 2005, 6, 234.

• 2006-07:
  MATH2770: Medical Statistics
  
• 2007-08:
  MATH5840: Hidden Markov Models for Biological Sequence Analysis
  
• 2008-09:
  MATH5840: Hidden Markov Models for Biological Sequence Analysis
  
• 2009-10:
  MATH5840: Hidden Markov Models for Biological Sequence Analysis
  
• 2010-11:
  MATH2770: Medical Statistics
  
• 2011-12:
  MATH3841: Introduction to Hidden Markov Models
  MATH5841: Hidden Markov Models and their Application in Bioinformatics