Homology search to be used in emerging bioinformatics problems such as metagenomics is of increasing importance and challenge as its application area grows more broadly while the computational complexity is increasing, thus requiring massive parallel data processing. Earlier work by some of the authors have devised novel algorithms such as GHOSTX, but the master-worker parallelization to enumerate and schedule for data processing was done with a privately developed, MPI-based master-worker framework called GHOST-MP. An alternative is to utilize the now-popular big data software substrates, such as MapReduce with abundant associated software tool-chains, but it is not clear whether the massive resource required by metagenomic homology search would not overwhelm its known limitations. By converting the GHOST-MP master-worker data processing pipeline to accommodate MapReduce, and benchmarking them on a variety of high-performance MapReduce incarnations including Hadoop and Spark, we attempt to characterize the appropriateness of MapReduce as a generic framework for metagenomics that embody extremely resource consuming requirements for both compute and data.
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