| Summary: | Abstract Background Homology search is still a significant step in functional analysis for genomic data. Profile Hidden Markov Model-based homology search has been widely used in protein domain analysis in many different species. In particular, with the fast accumulation of transcriptomic data of non-model species and metagenomic data, profile homology search is widely adopted in integrated pipelines for functional analysis. While the state-of-the-art tool HMMER has achieved high sensitivity and accuracy in domain annotation, the sensitivity of HMMER on short reads declines rapidly. The low sensitivity on short read homology search can lead to inaccurate domain composition and abundance computation. Our experimental results showed that half of the reads were missed by HMMER for a RNA-Seq dataset. Thus, there is a need for better methods to improve the homology search performance for short reads. Results We introduce a profile homology search tool named Short-Pair that is designed for short paired-end reads. By using an approximate Bayesian approach employing distribution of fragment lengths and alignment scores, Short-Pair can retrieve the missing end and determine true domains. In particular, Short-Pair increases the accuracy in aligning short reads that are part of remote homologs. We applied Short-Pair to a RNA-Seq dataset and a metagenomic dataset and quantified its sensitivity and accuracy on homology search. The experimental results show that Short-Pair can achieve better overall performance than the state-of-the-art methodology of profile homology search. Conclusions Short-Pair is best used for next-generation sequencing (NGS) data that lack reference genomes. It provides a complementary paired-end read homology search tool to HMMER. The source code is freely available at https://sourceforge.net/projects/short-pair/ .
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