Evidence map›Paper›PMID 37961660›Full record

ArticlebioRxiv : the preprint server for biology2024

Movi: a fast and cache-efficient full-text pangenome index.

Mohsen Zakeri, Nathaniel K Brown, Omar Y Ahmed, Travis Gagie, Ben Langmead

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Mohsen ZakeriDepartment of Computer Science, Johns Hopkins University.ORCID 0000-0002-9856-719X
Nathaniel K BrownDepartment of Computer Science, Johns Hopkins University.ORCID 0000-0002-6201-2301
Omar Y AhmedDepartment of Computer Science, Johns Hopkins University.ORCID 0000-0002-9933-8508
Travis GagieFaculty of Computer Science, Dalhousie University.ORCID 0000-0003-3689-327X
Ben LangmeadDepartment of Computer Science, Johns Hopkins University.ORCID 0000-0003-2437-1976

Funding

Personal and panel references for improved alignmentR01HG011392 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI LANGMEAD, BENJAMIN THOMAS · 2020 to 2025
$4.2M
Efficient and scalable pangenomes with the move structureR21HG013433 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI LANGMEAD, BENJAMIN THOMAS · 2024 to 2025
$399k
NHGRI NIH HHS R01 HG011392NHGRI NIH HHS R21 HG013433
6 · The paper itself

Abstract

Efficient pangenome indexes are promising tools for many applications, including rapid classification of nanopore sequencing reads. Recently, a compressed-index data structure called the "move structure" was proposed as an alternative to other BWT-based indexes like the FM index and r-index. The move structure uniquely achieves both O(r) space and O(1)-time queries, where r is the number of runs in the pangenome BWT. We implemented Movi, an efficient tool for building and querying move-structure pangenome indexes. While the size of the Movi's index is larger than the r-index, it scales at a smaller rate for pangenome references, as its size is exactly proportional to r, the number of runs in the BWT of the reference. Movi can compute sophisticated matching queries needed for classification - such as pseudo-matching lengths and backward search - at least ten times faster than the fastest available methods, and in some cases more than 30-fold faster. Movi achieves this speed by leveraging the move structure's strong locality of reference, incurring close to the minimum possible number of cache misses for queries against large pangenomes. We achieve still further speed improvements by using memory prefetching to attain a degree of latency hiding that would be difficult with other index structures like the r-index. Movi's fast constant-time query loop makes it well suited to real-time applications like adaptive sampling for nanopore sequencing, where decisions must be made in a small and predictable time interval.

Identifiers

PMID37961660
PMCPMC10635132

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.