Summary: When choosing a read mapper, one faces the trade off between speed and the ability to map reads in highly polymorphic regions. Here, we report NextGenMap, a fast and accurate read mapper, which reduces this dilemma. NextGenMap aligns reads reliably to a reference genome even when the sequence difference between target and reference genome is large, i.e. highly polymorphic genome. At the same time, NextGenMap outperforms current mapping methods with respect to runtime and to the number of correctly mapped reads.NextGenMap efficiently uses the available hardware by exploiting multi-core CPUs as well as graphic cards (GPUs), if available. In addition,NextGenMap handles automatically any read data independent of read length and sequencing technology.
Summary: We propose SW#, a new CUDA graphical processor unit-enabled and memory-efficient implementation of dynamic programming algorithm, for local alignment. It can be used as either a stand-alone application or a library. Although there are other graphical processor unit implementations of the Smith–Waterman algorithm, SW# is the only one publicly available that can produce sequence alignments on genome-wide scale. For long sequences, it is at least a few hundred times faster than a CPU version of the same algorithm
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