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A major challenge in next-generation genome sequencing (NGS) is to assemblemassive overlapping short reads that are randomly sampled from DNA fragments.To complete assembling, one needs to finish a fundamental task in many leadingassembly algorithms: counting the number of occurrences of k-mers (length-ksubstrings in sequences). The counting results are critical for many componentsin assembly (e.g. variants detection and read error correction). For largegenomes, the k-mer counting task can easily consume a huge amount of memory,making it impossible for large-scale parallel assembly on commodity servers.  In this paper, we develop MSPKmerCounter, a disk-based approach, toefficiently perform k-mer counting for large genomes using a small amount ofmemory. Our approach is based on a novel technique called Minimum SubstringPartitioning (MSP). MSP breaks short reads into multiple disjoint partitionssuch that each partition can be loaded into memory and processed individually.By leveraging the overlaps among the k-mers derived from the same short read,MSP can achieve astonishing compression ratio so that the I/O cost can besignificantly reduced. For the task of k-mer counting, MSPKmerCounter offers avery fast and memory-efficient solution. Experiment results on large real-lifeshort reads data sets demonstrate that MSPKmerCounter can achieve betteroverall performance than state-of-the-art k-mer counting approaches.  MSPKmerCounter is available at http://www.cs.ucsb.edu/~yangli/MSPKmerCounter

MSPKmerCounter: A Fast and Memory Efficient Approach for K-mer Counting