Evidence map›Paper›PMID 37904736›Full record

ArticleArXiv2023

Interpreting Sequence-Levenshtein distance for determining error type and frequency between two embedded sequences of equal length.

Robert Logan, Amy Wangsness Wehe, Dori C Woods, Jon Tilly, Konstantin Khrapko

Abstract readPreprint
In one paragraph

Article in ArXiv, 2023. 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.

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

5 authors.

Robert LoganScience and Technology Division, Biology and Bioinformatics Department, Eastern Nazarene College, Quincy, MA 02170.
Amy Wangsness WeheHealth and Natural Sciences Division, Mathematics Department, Fitchburg State University, Fitch-burg, MA 01420-2697.
Dori C WoodsCollege of Science, Department of Biology, Northeastern University, 330 Huntington Ave, Boston, MA 02115.
Jon TillyCollege of Science, Department of Biology, Northeastern University, 330 Huntington Ave, Boston, MA 02115.
Konstantin KhrapkoCollege of Science, Department of Biology, Northeastern University, 330 Huntington Ave, Boston, MA 02115.

Funding

mtDNA phylogeny of the germ line: mechanism, structure and function of the mtDNA bottleneckR01HD091439 · NICHD · NORTHEASTERN UNIVERSITY · PI KHRAPKO, KONSTANTIN, TILLY, JONATHAN LEE · 2018 to 2022
$1.6M
NICHD NIH HHS R01 HD091439
6 · The paper itself

Abstract

Levenshtein distance is a commonly used edit distance metric, typically applied in language processing, and to a lesser extent, in molecular biology analysis. Biological nucleic acid sequences are often embedded in longer sequences and are subject to insertion and deletion errors that introduce frameshift during sequencing. These frameshift errors are due to string context and should not be counted as true biological errors. Sequence-Levenshtein distance is a modification to Levenshtein distance that is permissive of frameshift error without additional penalty. However, in a biological context Levenshtein distance needs to accommodate both frameshift and weighted errors, which Sequence-Levenshtein distance cannot do. Errors are weighted when they are associated with a numerical cost that corresponds to their frequency of appearance. Here, we describe a modification that allows the use of Levenshtein distance and Sequence-Levenshtein distance to appropriately accommodate penalty-free frameshift between embedded sequences and correctly weight specific error types.

Indexed as

edit distanceLevenshtein distancematrixmolecular biologyweighted error

Identifiers

PMID37904736
PMCPMC10614987

What OpenQuestion holds

Textmetadata
LicenceCC BY-SA
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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.