Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images.

Yusei Kobori, Satoshi Mizuta

Abstract read

In one paragraph

Article in Genomics, proteomics & bioinformatics, 2016. 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.

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

1 citing paper in PubMed.

WalkIm: Compact image-based encoding for high-performance classification of biological sequences using simple tuning-free CNNs.PloS one · 2022
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

2 authors.

Yusei KoboriGraduate School of Science and Technology, Hirosaki University, Hirosaki, Aomori 036-8561, Japan.

Satoshi MizutaGraduate School of Science and Technology, Hirosaki University, Hirosaki, Aomori 036-8561, Japan. Electronic address: slmizu@hirosaki-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Graphical representation of DNA sequences is one of the most popular techniques for alignment-free sequence comparison. Here, we propose a new method for the feature extraction of DNA sequences represented by binary images, by estimating the similarity between DNA sequences using the frequency histograms of local bitmap patterns of images. Our method shows linear time complexity for the length of DNA sequences, which is practical even when long sequences, such as whole genome sequences, are compared. We tested five distance measures for the estimation of sequence similarities, and found that the histogram intersection and Manhattan distance are the most appropriate ones for phylogenetic analyses.

Indexed as

AlgorithmsAnimalsBase SequenceGenome, MitochondrialHumansPhylogenySequence AlignmentSequence Analysis, DNABitmap imageDistance measureGenome sequenceMitochondriaOccurrence frequency

Identifiers

PMID27132143

DOI10.1016/j.gpb.2015.09.007

PMCPMC4880953

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

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.