| First Author | Brown NP | Year | 1995 |
| Journal | J Mol Biol | Volume | 249 |
| Issue | 2 | Pages | 342-59 |
| PubMed ID | 7783198 | Mgi Jnum | J:26123 |
| Mgi Id | MGI:73745 | Doi | 10.1006/jmbi.1995.0301 |
| Citation | Brown NP, et al. (1995) Identification and analysis of multigene families by comparison of exon fingerprints. J Mol Biol 249(2):342-59 |
| abstractText | Gene families are often recognised by sequence homology using similarity searching to find relationships, however, genomic sequence data provides gene architectural information not used by conventional search methods. In particular, intron positions and phases are expected to be relatively conserved features, because mis-splicing and reading frame shifts should be selected against. A fast search technique capable of detecting possible weak sequence homologies apparent at the intron/exon level of gene organization is presented for comparing spliceosomal genes and gene fragments. FINEX compares strings of exons delimited by intron/exon boundary positions and intron phases (exon fingerprint) using a global dynamic programming algorithm with a combined intron phase identity and exon size dissimilarity score. Exon fingerprints are typically two orders of magnitude smaller than their nucleic acid sequence counterparts giving rise to fast search times: a ranked search against a library of 6755 fingerprints for a typical three exon fingerprint completes in under 30 seconds on an ordinary workstation, while a worst case largest fingerprint of 52 exons completes in just over one minute. The short sequence length of exon fingerprints in comparisons is compensated for by the large exon alphabet compounded of intron phase types and a wide range of exon sizes, the latter contributing the most information to alignments. FINEX performs better in some searches than conventional methods, finding matches with similar exon organization, but low sequence homology. A search using a human serum albumin finds all members of the multigene family in the FINEX database at the top of the search ranking, despite very low amino acid percentage identities between family members. The method should complement conventional sequence searching and alignment techniques, offering a means of identifying otherwise hard to detect homologies where genomic data are available. |
