Practical Random Access to SLP-Compressed Texts

Gagie, Travis; Tomohiro, I; Manzini, Giovanni; Navarro, Gonzalo; Sakamoto, Hiroshi; Benkner, Louisa Seelbach; Takabatake, Yoshimasa

doi:10.1007/978-3-030-59212-7_16

Cited by 14 publications

(13 citation statements)

References 27 publications

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“…A MONI index consists of four main components, the run-length encoded BWT, suffix-array samples taken at run boundaries, the thresholds, and a grammar ( Gagie et al. 2019 , 2020b ) that provides random access to T . These data structures allow computation of matching statistics in

time and take

space where g is the size of a given straight-line program for T .…”

Section: Methodsmentioning

confidence: 99%

“…Rossi et al (Rossi et al, 2021) with MONI showed how to efficiently compute the thresholds for highly repetitive texts, and implemented the matching statistics algorithm. A MONI index consists of four main components, the run-length encoded BWT, suffix-array samples taken at run boundaries, the thresholds, and a grammar (Gagie et al 2019(Gagie et al , 2020b) that provides random access to T. These data structures allow computation of matching statistics in OðmlognÞ time and take Oðr + gÞ space where g is the size of a given straight-line program for T.…”

Section: Limitations Of Studymentioning

confidence: 99%

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Pan-genomic matching statistics for targeted nanopore sequencing

et al. 2021

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Summary Nanopore sequencing is an increasingly powerful tool for genomics. Recently, computational advances have allowed nanopores to sequence in a targeted fashion; as the sequencer emits data, software can analyze the data in real time and signal the sequencer to eject “nontarget” DNA molecules. We present a novel method called SPUMONI, which enables rapid and accurate targeted sequencing using efficient pan-genome indexes. SPUMONI uses a compressed index to rapidly generate exact or approximate matching statistics in a streaming fashion. When used to target a specific strain in a mock community, SPUMONI has similar accuracy as minimap2 when both are run against an index containing many strains per species. However SPUMONI is 12 times faster than minimap2. SPUMONI's index and peak memory footprint are also 16 to 4 times smaller than those of minimap2, respectively. This could enable accurate targeted sequencing even when the targeted strains have not necessarily been sequenced or assembled previously.

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time and take

space where g is the size of a given straight-line program for T .…”

Section: Methodsmentioning

confidence: 99%

Section: Limitations Of Studymentioning

confidence: 99%

Pan-genomic matching statistics for targeted nanopore sequencing

et al. 2021

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“…Rossi et al [6] with MONI showed how to efficiently compute the thresholds for highly repetitive texts, and implemented the matching statistics algorithm. A MONI index consists of four main components, the run-length encoded BWT, suffix-array samples taken at run boundaries, the thresholds, and a grammar [16, 17] that provides random access to T . These data structures allow computation of matching statistics in O ( m log n ) time, and take O ( r + g ) space where g is the size of a given straight-line program (SLP) for T .…”

Section: Methodsmentioning

confidence: 99%

Pan-genomic Matching Statistics for Targeted Nanopore Sequencing

Ahmed

Rossi

Kovaka

et al. 2021

Preprint

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Nanopore sequencing is an increasingly powerful tool for genomics. Recently, computational advances have allowed nanopores to sequence in a targeted fashion; as the sequencer emits data, software can analyze the data in real time and signal the sequencer to eject “non-target” DNA molecules. We present a novel method called SPUMONI, which enables rapid and accurate targeted sequencing with the help of efficient pangenome indexes. SPUMONI uses a compressed index to rapidly generate exact or approximate matching statistics (half-maximal exact matches) in a streaming fashion. When used to target a specific strain in a mock community, SPUMONI has similar accuracy as minimap2 when both are run against an index containing many strains per species. However SPUMONI is 12 times faster than minimap2. SPUMONI’s index and peak memory footprint are also 15 to 4 times smaller than minimap2, respectively. These improvements become even more pronounced with even larger reference databases; SPUMONI’s index size scales sublinearly with the number of reference genomes included. This could enable accurate targeted sequencing even in the case where the targeted strains have not necessarily been sequenced or assembled previously. SPUMONI is open source software available from https://github.com/oma219/spumoni.

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“…We used SACA-K (Nong, 2013) to lexicographically sort the parses, and gSACA-K (Louza et al, 2017) to compute the SA and LCP array of the dictionary. We provide random access to the reference using the practical random access to SLPs of Gagie et al (2020b) built on the grammar obtained using BigRePair . We used the ksw2 (Suzuki and Kasahara, 2018;Li, 2018) library available at https://github.com/lh3/ksw2 to compute the local alignment.…”

Section: Methodsmentioning

confidence: 99%

MONI: A Pangenomics Index for Finding MEMs

Rossi

Oliva

Langmead

et al. 2021

Preprint

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Recently, Gagie et al. proposed a version of the FM-index, called the r-index, that can store thousands of human genomes on a commodity computer. Then Kuhnle et al. showed how to build the r-index efficiently via a technique called prefix-free parsing (PFP) and demonstrated its effectiveness for exact pattern matching. Exact pattern matching can be leveraged to support approximate pattern matching but the r-index itself cannot support efficiently popular and important queries such as finding maximal exact matches (MEMs). To address this shortcoming, Bannai et al. introduced the concept of thresholds, and showed that storing them together with the r-index enables efficient MEM finding --- but they did not say how to find those thresholds. We present a novel algorithm that applies PFP to build the r-index and find the thresholds simultaneously and in linear time and space with respect to the size of the prefix-free parse. Our implementation called MONI can rapidly find MEMs between reads and large sequence collections of highly repetitive sequences. Compared to other read aligners -- PuffAligner, Bowtie2, BWA-MEM, and CHIC -- MONI used 2--11 times less memory and was 2--32 times faster for index construction. Moreover, MONI was less than one thousandth the size of competing indexes for large collections of human chromosomes. Thus, MONI represents a major advance in our ability to perform MEM finding against very large collections of related references. Availability: MONI is publicly available at https://github.com/maxrossi91/moni.

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Practical Random Access to SLP-Compressed Texts

Cited by 14 publications

References 27 publications

Pan-genomic matching statistics for targeted nanopore sequencing

Pan-genomic matching statistics for targeted nanopore sequencing

Pan-genomic Matching Statistics for Targeted Nanopore Sequencing

MONI: A Pangenomics Index for Finding MEMs

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