Motivation: Life science researchers often require an exhaustive list of protein coding genes similar to a given query gene. To find such genes, homology search tools, such as BLAST or PatternHunter, return a set of high-scoring pairs (HSPs). These HSPs then need to be correlated with existing sequence annotations, or assembled manually into putative gene structures. This process is error-prone and labor-intensive, especially in genomes without reliable gene annotation. Results: We have developed a homology search solution that automates this process, and instead of HSPs returns complete gene structures. We achieve better sensitivity and specificity by adapting a hidden Markov model for gene finding to reflect features of the query gene. Compared to traditional homology search, our novel approach identifies splice sites much more reliably and can even locate exons that were lost in the query gene. On a testing set of 400 mouse query genes, we report 79% exon sensitivity and 80% exon specificity in the human genome based on orthologous genes annotated in NCBI HomoloGene. In the same set, we also found 50 (12%) gene structures with better protein alignment scores than the ones identified in HomoloGene.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics