Matching Reads to Many Genomes with the <i>r</i>-Index

Mun, Taher; Kuhnle, Alan; Boucher, Christina; Gagie, Travis; Langmead, Ben; Manzini, Giovanni

doi:10.1089/cmb.2019.0316

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…While this comparison between SPUMONI and minimap2 is close, we expect that as we are able to index and align to more human references simultaneously – for example, as more assemblies from the Human Pangenome Reference Consortium ( Human Pangenome Reference, 2021 ) and similar projects emerge — SPUMONI is well positioned for sublinear index growth and a greater throughput advantage. For instance, the r -index underlying SPUMONI was previously shown to be able to index up to 10 human genomes with sublinear growth in the index size ( Mun et al., 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…Importantly, the space required by an r -index is proportional to the number of runs in the Burrows-Wheeler transform (BWT) of the reference genomes (defined as r ) rather than the total length of the reference genomes. When the collection is highly repetitive, r grows sublinearly and far more slowly than the total length ( Mun et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, in a typical metagenomics experiment, the exact strain or substrain ll OPEN ACCESS of a microorganism is unknown before sequencing, and therefore, for optimal targeted sequencing, all strains and substrains need to be incorporated into the reference for identification. SPUMONI takes advantage of the overall repetitiveness of these references by building an r-index (Mun et al, 2020) and using the MONI algorithm to calculate matching statistics (MSs) (Rossi et al, 2021). The r-index enables efficient indexing of repetitive collections of reference genomes -for example, all of the strains of a bacterial species or several human genome assemblies -while still supporting efficient queries.…”

Section: Introductionmentioning

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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pan-genomic matching statistics for targeted nanopore sequencing

et al. 2021

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“…For example, in a typical metagenomics experiment, the exact strain or sub-strain of a microorganism is unknown prior to sequencing and therefore, for optimal targeted sequencing, all strains and substrains need to be incorporated into the reference for identification. SPUMONI takes advantage of the overall repetitiveness of these references by building an r -index [5], and using the MONI algorithm to calculate matching statistics (MSs) [6].…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, the space required by an r -index is proportional to the number of runs in the Burrows-Wheeler Transform of the reference genomes (defined as r ) rather than the total length of the reference genomes. When the collection is highly repetitive, r grows sublinearly, and far more slowly than the total length [5].…”

Section: Introductionmentioning

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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Computational graph pangenomics: a tutorial on data structures and their applications

et al. 2022

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Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations—thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or a graph pangenome, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of haplotypes and the variability of genotypes in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.

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Matching Reads to Many Genomes with the r-Index

Cited by 8 publications

References 15 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

Computational graph pangenomics: a tutorial on data structures and their applications

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