The scoring sequences on profile Hidden Markov Models with delete states elimination by GPUs

Abstract

A profile Hidden Markov Model (HMM) is well suited for representing profiles of multiple sequences alignments, and it has been becoming the main method of multiple sequences alignments in bioinformatics. The scoring of sequences on profile HMMs is compute-intensive, especially when there are many Markov models and many states in each model. A parallel algorithm for Graphic Processing Unit (GPU)s is presented to score multiple sequences quickly on profile HMMs, and it featured with delete states elimination to reduce the compute-load greatly using a commodity graphics processing unit. The access to the parameters of profile HMMs is accelerated by allocating space in proper memory hierarchy. The algorithm was tested on a NVIDIA 9800 GTX+ graphic processing unit, experimental results showed the parallel algorithm can score multiple sequences on profile HMMs 8∼50 times faster than the serial algorithm does on Pentium E5200 CPU.