Evidence map›Paper›PMID 40662833›Full record

ArticleBioinformatics (Oxford, England)2025

Ultrafast and ultralarge multiple sequence alignments using TWILIGHT.

Yu-Hsiang Tseng, Sumit Walia, Yatish Turakhia

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yu-Hsiang TsengDepartment of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, United States.ORCID 0009-0005-8758-9739
Sumit WaliaDepartment of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, United States.ORCID 0000-0003-4557-2339
Yatish TurakhiaDepartment of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, United States.ORCID 0000-0001-5600-2900

Funding

AMD AI & HPC FundOffice of Advanced Molecular Detection #75D30123C17463U.S. Centers for Disease Control and Prevention
6 · The paper itself

Abstract

motivationMultiple sequence alignment (MSA) is a fundamental operation in bioinformatics, yet existing MSA tools are struggling to keep up with the speed and volume of incoming data. This is because the runtimes and memory requirements of current MSA tools become untenable when processing large numbers of long input sequences, and they also fail to fully harness the parallelism provided by modern CPUs and GPUs.

resultsWe present Tall and Wide Alignments at High Throughput (TWILIGHT), a novel MSA tool optimized for speed, accuracy, scalability, and memory constraints, with both CPU and GPU support. TWILIGHT incorporates innovative parallelization and memory-efficiency strategies that enable it to build ultralarge alignments at high speed even on memory-constrained devices. On challenging datasets, TWILIGHT outperformed all other tools in speed and accuracy. It scaled beyond the limits of existing tools and performed an alignment of 1 million RNASim sequences within 30 min while utilizing <16 GB of memory. TWILIGHT is the first tool to align over 8 million publicly available SARS-CoV-2 sequences, setting a new standard for large-scale genomic alignment and data analysis. AVAILABILITY AND IMPLEMENTATION: TWILIGHT's code is freely available under the MIT license at https://github.com/TurakhiaLab/TWILIGHT. The test datasets and experimental results, including our alignment of 8 million SARS-CoV-2 sequences, are available at https://zenodo.org/records/14722035.

Indexed as

Computational BiologySARS-CoV-2Sequence AlignmentSoftwareAlgorithmsCOVID-19

Identifiers

PMID40662833
PMCPMC12261412

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.