| Center for | KVL |
| Bioinformatics |
Use opgang 6, 6th. floor in højhuset, Thorvaldsensvej 40.
Anyone can participate, registration is not necessary.
Erik Sonnhammer
Stockholm Bioinformatics Center AlbaNova University Center,
Stockholm University
http://sonnhammer.sbc.su.se/
Abstract
FunCoup is a method aimed to discover novel functional links
between proteins - information that can be used for focussing work on
individual proteins of interest or creating sophisticated gene
networks. The method uses Bayesian networks optimized with
multivariate techniques to integrate genomics and proteomics
information of various types and from different sources. The crucial
novelty is involving data on orthologous genes in well-studied model
organisms. Orthologs are provided by InParanoid, which has the
advantage of including inparalogs. FunCoup, as InParanoid, is mainly
focused on eukaryotic genomes. Currently, FunCoup uses a range of data
sources - from relatively loose associations like coexpression to physically
interacting proteins. Querying of particular proteins for functional
coupling is available at
http://www.sbc.su.se/~andale/.
Background
Sonnhammer has been active in several bioinformatics research areas during
the last 15 years. He pioneered several protein domain databases and
developed general-purpose bioinformatics methods/tools for sequence and
genome analysis that are currently used around the world. These include the
Pfam and ProDom databases of protein domain families, the dot-matrix program
Dotter, the homology analysis workbench Blixem, and the hidden Markov
model-based transmembrane topology prediction programs TMHMM and Phobius
(which also predicts signal peptides).
Recently, Sonnhammer has developed several automated methods to find groups of true orthologs while avoiding inclusion of ``ancient paralogs'' that often contaminate ortholog clusters in other databases. The algorithms are known as Inparanoid, based on fast pairwise alignments, and Orthostrapper, which uses more rigorous tree-based methods. These algorithms were applied to complete genome datasets to generate the Inparanoid database, and to the Pfam database to generate the HOPS database. Furthermore, a disease-oriented database of orthologs to known disease genes was set up, called OrthoDisease
He has also developed new computational methods for predicting effective antisense oligos and siRNA that presently are the most accurate in the field. During the last years, Sonnhammer began to focus on exploiting functional genomics and associated data to predict gene function, and was the first to prove significant genomic clustering of functionally related genes in eukaryotes.
Henrik Nielsen
Center for Biological Sequence Analysis,
The Technical University of Denmark.
http://cbs.dtu.dk/staff/show-staff.php?id=535.
Abstract
A knowledge of the positions of introns in eukaryotic genes is
important for understanding the evolution of introns. Despite this,
there has been relatively little focus on the distribution of
intron positions in genes.
In proteins with signal peptides, there is an
over-abundance of phase 1 introns around the region of the signal
peptide cleavage site. This has been described before. But in
proteins without signal peptides, a novel phenomenon is observed:
There is a sharp peak of phase 0 intron positions immediately
following the start codon, ie between codons 1 and 2. This effect
is seen in a wide range of eukaryotes: Vertebrates, arthropods, fungi,
and flowering plants. Proteins carrying this start codon intron are
found to comprise a special class of relatively short, lysine-rich
and conserved proteins with an overrepresentation of ribosomal
proteins. In addition, there is a peak of phase 0 introns at
position 5 in Drosophila genes with signal peptides,
predominantly representing cuticle proteins.
There is an overabundance of phase 0
introns immediately after the start codon in eukaryotic genes, which
has been described before only for human ribosomal proteins. We give
a detailed description of these start codon introns and the proteins
that contain them.
Background
Dr. Henrik Nielsen is has a long standing record in post modifications of
proteins and have been involved in developing several methods, including
SignalP, ChloroP and TargetP. He is most known for his work on SignalP, a
computational method to predict signal peptides and their clevage sites in
protein sequences from Gram-positive bacteria, Gram-negative bacteria, and
eukaryotes. His work is heavily cited and the SignalP paper itself hold more
than 3300 citations.
Max F. Rothschild
Max F. Rothschild is C.F. Curtiss Distinguished Professor in Agriculture
and Director, Center for Integrated Animal Genomics at
Iowa State University.
http://www.ans.iastate.edu/faculty/index.php?id=mfrothsc
Background
Max F. Rothschild, is a Distinguished professor in the College of
Agriculture at Iowa State University. He has been a faculty member at ISU
for 26 years and is now director of the Center for Integrated Animal
Genomics which is working to facilitate genomic and bioinformatic research
and activities on campus. His research interests include pig genetics and
genomics and comparative genomics using the pig and dog. Rothschild has
served as USDA Pig Genome Coordinator and in that regard has helped to
develop bioinformatic tools to help pig gene discovery.