Assessment and refinement of eukaryotic gene structure prediction with gene-structure-aware multiple protein sequence alignment
1 Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan
2 Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
3 Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN 38163, USA
BMC Bioinformatics 2014, 15:189 doi:10.1186/1471-2105-15-189Published: 14 June 2014
Accurate computational identification of eukaryotic gene organization is a long-standing problem. Despite the fundamental importance of precise annotation of genes encoded in newly sequenced genomes, the accuracy of predicted gene structures has not been critically evaluated, mostly due to the scarcity of proper assessment methods.
We present a gene-structure-aware multiple sequence alignment method for gene prediction using amino acid sequences translated from homologous genes from many genomes. The approach provides rich information concerning the reliability of each predicted gene structure. We have also devised an iterative method that attempts to improve the structures of suspiciously predicted genes based on a spliced alignment algorithm using consensus sequences or reliable homologs as templates. Application of our methods to cytochrome P450 and ribosomal proteins from 47 plant genomes indicated that 50 ~ 60 % of the annotated gene structures are likely to contain some defects. Whereas more than half of the defect-containing genes may be intrinsically broken, i.e. they are pseudogenes or gene fragments, located in unfinished sequencing areas, or corresponding to non-productive isoforms, the defects found in a majority of the remaining gene candidates can be remedied by our iterative refinement method.
Refinement of eukaryotic gene structures mediated by gene-structure-aware multiple protein sequence alignment is a useful strategy to dramatically improve the overall prediction quality of a set of homologous genes. Our method will be applicable to various families of protein-coding genes if their domain structures are evolutionarily stable. It is also feasible to apply our method to gene families from all kingdoms of life, not just plants.