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Bioinformaticists Will Be Busy Bees

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At a bioinformatics conference last fall, EBI’s Ewan Birney, MIT’s Chris Burge, and GlaxoSmithKline’s Jim Fickett gave an impromptu roundup of the future challenges of the field. Burge polished them up for GT:

Top 10 Future Challenges

 

1 Precise, predictive model of transcription initiation and termination: ability to predict where and when transcription will occur in a genome

2 Precise, predictive model of RNA splicing/alternative splicing: ability to predict the splicing pattern of any primary transcript in any tissue

3 Precise, quantitative models of signal transduction pathways: ability to predict cellular responses to external stimuli

4 Determining effective protein:DNA, protein:RNA and protein:protein recognition codes

5 Accurate ab initio protein structure prediction

6 Rational design of small molecule inhibitors of proteins

7 Mechanistic understanding of protein evolution: understanding exactly how new protein functions evolve

8 Mechanistic understanding of speciation: molecular details of how speciation occurs

9 Continued development of effective gene ontologies — systematic ways to describe the functions of any gene or protein

10 Education: development of appropriate bioinformatics curricula for secondary, undergraduate, and graduate education

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