We propose the hiring of seven faculty in bioinformatics. As detailed in section below, these would likely be hired in several different departments across several different colleges. The following specific subareas of bioinformatics are listed in general (but not precise) order of hiring priority, and in order of their relationship to the general field of genomics. Also, the categories are not precise and there can be great overlap in the areas. Note that the numbers do not add to seven, allowing the flexibility to change priorities in response to hiring opportunities, to reflect the views of early and major hires, and to changes in the field.
1. Two FTE in DNA and protein sequence analysis, particularly multiple sequence comparison and clustering. Other emphases include gene finding, motif detection and characterization, and development of biologically effective and computationally efficient models and methods for sequence comparison. These areas are at the heart of present-day sequence-oriented bioinformatics, as discussed earlier.
2. One or two FTE in interspecies genomic comparisons. For example, the genome center at LLNL is working on man/mouse genome comparisons. These comparisons involve more than gene comparisons, for example genome organization and synteny.
3. One or two FTE in protein structure modeling, and the inference of structural features from sequence. This also includes methods such as homology modeling and threading.
4. One FTE in the design, use and integration of databases for biological data. With the number of databases growing exponentially, it is essential that they be made to work together. Moreover, as the complexity of the biological data increases (for example as we move into systems biology) the complexity of the databases will increase as well.
5. One or two FTE in visualization, modeling, simulation and analysis of complex biological systems, for example biochemical pathways, brain structures. Complex databases are being constructed for these systems. What is needed are algorithms and insight into how to exploit these databases to both help the researcher handle the dataload, and to find interesting features in the data.
6. One FTE in computational problems in genomic laboratory protocols, for example the design and use of array technology, pooling methods in DNA sequencing, or sequencing by hybridization. There are many algorithmic, mathematical and statistical problems here.
7. One FTE position in computational or statistical issues in phylogenetics.
8. One FTE position molecular quantitative genetics, for example, QTL mapping.