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Millions of DNA sequences (reads) are generated by Next Generation Sequencing machines everyday. There is a need for high performance algorithms to map these sequences to the reference genome to identify single nucleotide polymorphisms or rare transcripts to fulfill the dream of personalized medicine. In this paper, we present a high-throughput parallel sequence mapping program pFANGS. pFANGS is designed to find all the matches of a query sequence in the reference genome tolerating a large number of mismatches or insertions/deletions. pFANGS partitions the computational workload and data among all the processes and employs load-balancing mechanisms to ensure better process efficiency. Our experiments show that, with 512 processors, we are able to map approximately 31 million 454/Roche queries of length 500 each to a reference human genome per hour allowing 5 mismatches or insertion/deletions at full sensitivity. We also report and compare the performance results of two alternative parallel implementations of pFANGS: a shared memory OpenMP implementation and a MPI-OpenMP hybrid implementation.