Evidence map›Paper›PMID 38352323›Full record

ArticlebioRxiv : the preprint server for biology2024

nail: software for high-speed, high-sensitivity protein sequence annotation.

Jack W Roddy, David H Rich, Travis J Wheeler

Open access · greenAbstract 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.

0numbers the graph read from it
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

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Jack W RoddyR. Ken Coit College of Pharmacy, University of Arizona, Tucson, Arizona, USA.ORCID 0000-0001-6055-7706
David H RichDepartment of Computer Science, University of Montana, Missoula, Montana, USA.
Travis J WheelerR. Ken Coit College of Pharmacy, University of Arizona, Tucson, Arizona, USA.ORCID 0000-0003-2004-1785
University of Arizona · USUniversity of Montana · US

Funding

Surveillance genome sequencing to detect SARS-CoV-2 virus variants in MontanaP20GM103546 · NIGMS · UNIVERSITY OF MONTANA · PI BOWLER, BRUCE E · 2012 to 2021
$19.2M
Maintenance and Development of RepeatMasker and RepeatModelerR01HG002939 · NHGRI · INSTITUTE FOR SYSTEMS BIOLOGY · PI Robert MacDonald Hubley, Arian Fredericus Anthonius Smit · 2003 to 2026
$9.1M
Dfam: sustainable growth, curation support, and improved quality for mobile element annotationU24HG010136 · NHGRI · INSTITUTE FOR SYSTEMS BIOLOGY · PI Robert MacDonald Hubley, Arian Fredericus Anthonius Smit · 2018 to 2026
$5.2M
Machine learning approaches for improved accuracy and speed in sequence annotation: supplement for software enhancementR01GM132600 · NIGMS · UNIVERSITY OF MONTANA · PI WHEELER, TRAVIS JOHN · 2019 to 2022
$1.4M
NHGRI NIH HHS R01 HG002939NHGRI NIH HHS U24 HG010136NIGMS NIH HHS P20 GM103546NIGMS NIH HHS R01 GM132600
6 · The paper itself

Abstract

" Fast is fine, but accuracy is final. " -- Wyatt Earp. Background: The extreme diversity of newly sequenced organisms and considerable scale of modern sequence databases lead to a tension between competing needs for sensitivity and speed in sequence annotation, with multiple tools displacing the venerable BLAST software suite on one axis or another. Alignment based on profile hidden Markov models (pHMMs) has demonstrated state of art sensitivity, while recent algorithmic advances have resulted in hyper-fast annotation tools with sensitivity close to that of BLAST. Results: Here, we introduce a new tool that bridges the gap between advances in these two directions, reaching speeds comparable to fast annotation methods such as MMseqs2 while retaining most of the sensitivity offered by pHMMs. The tool, called nail, implements a heuristic approximation of the pHMM Forward/Backward (FB) algorithm by identifying a sparse subset of the cells in the FB dynamic programming matrix that contains most of the probability mass. The method produces an accurate approximation of pHMM scores and E-values with high speed and small memory requirements. On a protein benchmark, nail recovers the majority of recall difference between MMseqs2 and HMMER, with run time ~26x faster than HMMER3 (only ~2.4x slower than MMseqs2's sensitive variant). nail is released under the open BSD-3-clause license and is available for download at https://github.com/TravisWheelerLab/nail.

Identifiers

PMID38352323
PMCPMC10862755
OpenAlexW4391517935

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.