ayout 1 - EMBL Grenoble

EMBL-EBI
Evolutionary tools for sequence analysis
Previous and current research
Research in the Goldman group concentrates on methods of data analysis that use evolutionary
information in sequence data and phylogenies to infer the history of living organisms, to describe
and understand processes of evolution, and to use this information to make predictions about the
function of genomic sequence. One focus of the group is on comparative genomics and the bulk
analysis of biological sequence data. Collaborations with major sequencing consortia remain fruitful,
providing new data, challenges and a proving ground for new methods of sequence analysis.
Intra-group collaborations between members developing theory and methods and those involved
in the comparative analysis of genomic data remain a stimulating source of inspiration in all of our
research areas.
The group has traditionally been strong in examining the theoretical foundations of phylogenetic
reconstruction and analysis. In 2008 we have had a productive year, developing methods to infer
and visualise evolutionary trees, and methods to use trees to improve sequence alignment. Our research
aims to increase our understanding of the process of evolution and to provide new tools for
biologists to elucidate the changing function of biological sequences.
Future projects and goals
The study of genome evolution continues to inspire us with novel problems in phylogenetic
methodology. The complex nature of the non-independence of sequence data due to their evolutionary
relatedness continues to generate statistically challenging problems, and the group is confident
that we will continue to contribute to this theoretical field of research. We remain dedicated
Nick Goldman
PhD 1992, University of
Cambridge.
Postdoctoral work at National
Institute for Medical
Research, London, and
University of Cambridge.
Wellcome Trust Senior Fellow
1995-2006.
Group leader at EMBL-EBI
since 2002. Training
Coordinator since 200;
Research and Training
Coordinator since 2007.
to retaining our interest in the practical applications of these methods in order to promote best practice in computational evolutionary and
genomic biology, to keep in touch with the evolving needs of laboratory scientists and to continue to benefit from a supply of motivational
biological questions where computational methods can help.
In 2009, next-generation sequencing technologies will produce almost as much sequence data as was produced in total prior to that date. For
groups like ours that work on methods for sequence analysis, it is necessary to remember that the questions biologists want to ask of these
data will change as more diverse experiments become practical. We have started to address this through our work on understanding the actual
generation of data from next-generation sequencing platforms, and we will continue to work to allow the greatest benefit from modern
molecular biology.
Screenshot of the PRANKSTER
software for visualising multiple
sequence alignments. The
evolutionary tree relating the
sequences, without which the
evolutionary meaning of the
alignment cannot be fully
interpreted, is visible to the left,
and various interesting insertion
and deletion patterns are
indicated (bottom).
Selected references
Jordan, G.E. & Piel, W.H. (2008). PhyloWidget: Web-based
visualizations for the tree of life. Bioinformatics, 2, 161-162
Löytynoja, A. & Goldman, N. (2008). Phylogeny-aware gap
placement prevents errors in sequence alignment and evolutionary
analysis. Science, 320, 1632-1635
Taylor, M. S. et al. (2008). Rapidly evolving human promoter regions.
Nature Genetics, 0, 1262-1263
Tress, M.L. et al. (2008). Determination and validation of principal
gene products. Bioinformatics, 2, 11-17
Washietl, S., Machné, R. & Goldman, N. (2008). Evolutionary
footprints of nucleosome positions in yeast. Trends in Genetics, 2,
583-587
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