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- W4298439973 abstract "In order to avoid the reference bias introduced by mapping reads to a reference genome, bioinformaticians are investigating reference-free methods for analyzing sequenced genomes. With large projects sequencing thousands of individuals, this raises the need for tools capable of handling terabases of sequence data. A key method is the Burrows-Wheeler transform (BWT), which is widely used for compressing and indexing reads. We propose a practical algorithm for building the BWT of a large read collection by merging the BWTs of subcollections. With our 2.4 Tbp datasets, the algorithm can merge 600 Gbp/day on a single system, using 30 gigabytes of memory overhead on top of the run-length encoded BWTs." @default.
- W4298439973 created "2022-10-02" @default.
- W4298439973 creator A5066730758 @default.
- W4298439973 date "2015-11-03" @default.
- W4298439973 modified "2023-10-08" @default.
- W4298439973 title "Burrows-Wheeler transform for terabases" @default.
- W4298439973 doi "https://doi.org/10.48550/arxiv.1511.00898" @default.
- W4298439973 hasPublicationYear "2015" @default.
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