With the increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. Although algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are memory-intensive, limiting their applications to small data sets with few libraries. The strategy used in the current project minimizes memory consumption while simultaneously obtaining comparable or improved accuracy over existing algorithms. It provides support for incremental updates of assemblies when new libraries become available. 6 figures, 4 tables, and 31 references (publisher abstract modified)
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