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Predicting function: from genes to genomes and back

P Bork1, T Dandekar, Y Diaz-Lazcoz

  • 1European Molecular Biology Laboratory, Meyerhofstr. 1, Heidelberg, PF 10.2209, Germany. Bork@EMBL-Heidelberg.de

Journal of Molecular Biology
|October 29, 1998
PubMed
Summary
This summary is machine-generated.

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Completely sequenced genomes significantly enhance computational gene function prediction. Integrating genomic, proteomic, and expression data will enable predicting complex cellular processes and mapping genotype to phenotype.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Predicting gene function computationally is complex and typically gene-by-gene.
  • Genomic sequencing offers new avenues for function prediction.
  • Protein function is best understood through molecular interactions.

Purpose of the Study:

  • To review the value of whole genome sequencing in gene function prediction.
  • To explore various levels of sequence annotation and function prediction.
  • To discuss future directions in predicting higher-order cellular processes.

Main Methods:

  • Review of existing literature on genomic sequence analysis.
  • Discussion of computational approaches for function prediction.
  • Integration of genomics, proteomics, and expression data.

Related Experiment Videos

Main Results:

  • Genomic data enhances function prediction beyond individual gene analysis.
  • Predicting protein function in the context of molecular interactions is current standard.
  • Future prediction will encompass gene expression regulation, metabolic pathways, and signaling cascades.

Conclusions:

  • Whole genome sequencing is crucial for advancing function prediction.
  • Integrating multi-omics data is key for understanding complex biological systems.
  • The ultimate goal is to map genotype to phenotype accurately.