High-throughput RNA sequencing (RNA-seq) is primarily used in measuring gene expression, quantifying transcript abundance, and building reference transcriptomes. Without bias from a reference sequence, de novo RNA-seq assembly is particularly useful for building new reference transcriptomes, detecting fusion genes, and discovering novel spliced transcripts. This is a challenging problem, and to address it at least eight approaches, including Trans-ABySS and Trinity, were developed within the past decade. For instance, using Trinity and 12 CPUs, it takes approximately one and a half day to assemble a human RNA-seq sample of over 100 million read pairs and requires up to 80 GB of memory. While the high memory usage typical of de novo RNA-seq assemblers may be alleviated by distributed computing, access to a high-performance computing environment is a requirement that may be limiting for smaller labs. In my thesis, I present a novel de novo RNA-seq assembler, “RNA-Bloom,” which utilizes compact data structures based on Bloom filters for the storage of k-mer counts and the de Bruijn graph in memory. Compared to Trans-ABySS and Trinity, RNA-Bloom can assemble a human transcriptome with comparable accuracy using nearly half as much memory and half the wall-clock time with 12 threads.Science, Faculty ofGraduat
