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Reference-free phylogeny from sequencing data

Overview of attention for article published in BioData Mining, March 2023
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

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6 X users

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2 Mendeley
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Title
Reference-free phylogeny from sequencing data
Published in
BioData Mining, March 2023
DOI 10.1186/s13040-023-00329-x
Pubmed ID
Authors

Petr Ryšavý, Filip Železný

Abstract

Clustering of genetic sequences is one of the key parts of bioinformatics analyses. Resulting phylogenetic trees are beneficial for solving many research questions, including tracing the history of species, studying migration in the past, or tracing a source of a virus outbreak. At the same time, biologists provide more data in the raw form of reads or only on contig-level assembly. Therefore, tools that are able to process those data without supervision need to be developed. In this paper, we present a tool for reference-free phylogeny capable of handling data where no mature-level assembly is available. The tool allows distance calculation for raw reads, contigs, and the combination of the latter. The tool provides an estimation of the Levenshtein distance between the sequences, which in turn estimates the number of mutations between the organisms. Compared to the previous research, the novelty of the method lies in a newly proposed combination of the read and contig measures, a new method for read-contig mapping, and an efficient embedding of contigs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 March 2023.
All research outputs
#7,381,124
of 25,600,774 outputs
Outputs from BioData Mining
#145
of 324 outputs
Outputs of similar age
#130,040
of 423,226 outputs
Outputs of similar age from BioData Mining
#5
of 8 outputs
Altmetric has tracked 25,600,774 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 324 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 423,226 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.