Indexing Compressed Text
By Meng He. 2003
538 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/5d82c96850793fd661870645c5e0d6111db99ff0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Wan-Ping Lee
- Jian Peng
- Erik P. Garrison
- Gabor T. Marth
- Michael P. Stromberg
- Alistair N. Ward
- Chip Stewart
- Bonnie Berger
Citations in the corpus
- Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data (2013, methodology): “SGA (Simpson and Durbin, 2012) uses a memory-efficient Burrows Wheeler transform (Burrows and Wheeler, 1994) and an FM-index (Ferragina and Manzini, 2005) to represent the k-mer spectrum.”
- Computational solutions for omics data (2013, methodology): “The FM-index contains the information from all suffixes of the original sequence and allows fast subsequence mapping and counting in O ( m ) time 36 .”
- Succinct indexable dictionaries with applications to encoding k-ary trees, prefix sums and multisets (2007, background): “As noted in Ferragina and Manzini [2005], they are important components of the “second generation” of compressed text indices (see also Navarro and Mäkinen [2007]). For example, they are essential for wavelet trees [Grossi et al. 2003]. They are also used in many other succinct data structures, incl”
- MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping (2013, background): “In general, BWT-based aligners are sensitive but include a slow query operation (each FM-index query is slower than a hash query [26,27]).”