- Meeting report
- Open Access
© GenomeBiology.com 2000
- Published: 1 September 2000
A report from the Bioinformatics 2000 conference [http://www.cbs.dtu.dk/bioinformatics2000/], held in Elsinore, Denmark, 27-30 April, 2000.
- Support Vector Machine
- Human Genome Project
- Protein Structure Prediction
- Small Nucleolar RNAs
- snoRNA Gene
Despite the explosion in sequence information, the problem of modeling and prediction of protein folding still remains unsolved. As Jeff Augen (Life Sciences Division of IBM) pointed out, there are two problems manifested by the sequence information explosion: the first is the continuing need to solve computationally intensive biochemical problems such as tertiary protein structure prediction. IBM [http://www.ibm.com/news/1999/12/06.phtml] is attempting to build the largest and fastest computer ever (capable of more than one petaflop; that is, more than one quadrillion - 1015 - operations, per second), and have plans to use it to try and solve the computationally intensive 'protein folding problem', by modeling the folding of a small protein. They have not yet decided which particular protein they will choose for this analysis.
The second problem resulting from the sequence explosion is the need to manage, store, search, study, and compare enormous amounts of genetic and proteomic data. Clare O'Donnovan (EMBL Outstation, Cambridge, UK) described the human proteomics initiative of the Swiss Institute of Bioinformatics and the European Bioinformatics Institute. This is a major project that will provide information about the structure, function and subcellular location of every known protein encoded in the human genome. More information about this, as well as the current status of the project, can be found at the Human Proteomics initiative web page [http://www.ebi.ac.uk/swissprot/hpi/hpi.html]. O'Donnovan also talked about attempts to deal with the explosion of sequence information. At the moment, to find all the available information about a given protein, a large number of databases must be searched. There is a real need for some sort of centralized, curated (and reliable) source of information about all human proteins identified by the human genome.
On the subject of ab initio protein structure prediction, David Baker (University of Washington, USA) described the development of a fast computational method to approximate protein folding. To simulate protein structure, Baker uses 'mini-threading', in which small protein segments of known structure are joined together to build the complete protein, using an optimization scheme to obtain the best fit. This approach has proven quite successful in the recent Critical Assessment of Structure Prediction - CASP3 [http://predictioncenter.llnl.gov/casp3/] competition. Some of the best ab initio protein structure prediction methods (including Baker's) still give structures in the four to eight Angstrom resolution range; unfortunately, this level of resolution usually does not allow the assignment of functions to individual residues. It is, however, occasionally possible to use these structure predictions to assign a function, or to provide useful information for drug discovery. This is possible because the known 'local structures' from well characterized proteins can sometimes be stitched together in a different context to provide a reasonable estimate of the structure in a novel protein.
Extensive sequence information has led to the discovery of new RNA-encoding genes as well as new protein-encoding ones, and two speakers based their talks on RNA structures. Sean Eddy (Washington University, St Louis, USA) described the families of small nucleolar RNAs involved in splicing. In some cases, mRNA codes for a 'protein' which is never made, and the 'introns' in this RNA turn out to be essential RNAs. Eddy found small nucleolar RNAs, or 'snoRNAs', in such a setting. Putative snoRNA genes have now been found in Archaea, but not in bacteria, by sequence analysis alone.
The Intron Sequence Information System (ISIS) website [http://isis.bit.uq.edu.au/] described by Soeren Schandorff (University of Copenhagen, Denmark) contains information on more than 170,000 spliceosomal introns. ISIS contains phylogenetic and protein homology categories, information about individual sequences and various bioinformatics analyses of taxonomical groupings of sequences using non-redundant subsets of the data. Schandorff has found that at least 42% of all human genes are alternatively spliced, and considers this to be a conservative estimate. The ISIS web site is based at the Department of Mathematics, University of Queensland, Australia, where Schandorff 's collaborators are working.
Support vector machines (SVMs) represent a general, nonlinear machine-learning method, which is commonly used for classification but can also be used for the analysis of mRNA expression data from DNA chip microarrays. David Haussler (University of California, Santa Cruz, USA) gave a clear overview of the theory behind support vector machines (for more information see http://www.kernel-machines.org), and presented results of protein sequence alignments using a joint hidden marker model (HMM; a probabilistic method) and SVM approach (Fischer kernel), which performs better than existing approaches, such as PSI-BLAST and stand-alone HMMs.
'Life on the Edge' was the theme of Bernhard Palsson's (University of California, San Diego, USA) talk about modeling metabolic genotypes of sequenced microbes. Using a combination of all the predicted proteins in the Escherichia coli K-12 genome (isolate MG1655), and an exhaustive knowledge of the biochemistry, Palsson has modeled metabolism under various growth conditions, and could accurately predict the behavior of 73 out of 80 mutants. This model also predicts that there are relatively few critical gene products in central metabolism, and some of the predicted non-essential genes have been experimentally verified.
A comparison of the quality of material expected from the Celera Drosophila sequence to that achieved for use in assembly
Reads in pairs
Insert length distance
False positive rate on mate pairs
Map-coverage (BAC pairs)