Fast and sensitive sequence database search is fundamental to modern molecular biology. This proposal describes a research plan to improve the accuracy of annotation of protein-coding content in sequenced genomes and metagenomic datasets. The research builds on established sequence database search software that employs probabilistic models to increase sensitivity through greater statistical power and ability to better model family complexity. The probabilistic models are called profile hidden Markov models (profile HMMs), and the software is HMMER. The taxonomic breadth of sequenced datasets requires methods with the power to detect remote sequence similarity; raw data and sequencing errors demand models that recognize frameshifts and splice sites; and the massive scale of datasets demands that implementations be fast. My group will develop novel models for frameshifts and splice site detection in profile HMM homology search. Direct modeling of these features within search software effectively uses homology to guide ORF/gene prediction, which in turn leads to better homology detection. Through a combination of new algorithms and application of existing approaches, these models will be fast enough to use for large-scale annotation, such as in the EMBL European Bioinformatics Metagenomics Portal.
NIH ExPorter
NIH ExPorter