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NucDiff: in-depth characterization and annotation of differences between two sets of DNA sequences

Overview of attention for article published in BMC Bioinformatics, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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16 X users

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Title
NucDiff: in-depth characterization and annotation of differences between two sets of DNA sequences
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1748-z
Pubmed ID
Authors

Ksenia Khelik, Karin Lagesen, Geir Kjetil Sandve, Torbjørn Rognes, Alexander Johan Nederbragt

Abstract

Comparing sets of sequences is a situation frequently encountered in bioinformatics, examples being comparing an assembly to a reference genome, or two genomes to each other. The purpose of the comparison is usually to find where the two sets differ, e.g. to find where a subsequence is repeated or deleted, or where insertions have been introduced. Such comparisons can be done using whole-genome alignments. Several tools for making such alignments exist, but none of them 1) provides detailed information about the types and locations of all differences between the two sets of sequences, 2) enables visualisation of alignment results at different levels of detail, and 3) carefully takes genomic repeats into consideration. We here present NucDiff, a tool aimed at locating and categorizing differences between two sets of closely related DNA sequences. NucDiff is able to deal with very fragmented genomes, repeated sequences, and various local differences and structural rearrangements. NucDiff determines differences by a rigorous analysis of alignment results obtained by the NUCmer, delta-filter and show-snps programs in the MUMmer sequence alignment package. All differences found are categorized according to a carefully defined classification scheme covering all possible differences between two sequences. Information about the differences is made available as GFF3 files, thus enabling visualisation using genome browsers as well as usage of the results as a component in an analysis pipeline. NucDiff was tested with varying parameters for the alignment step and compared with existing alternatives, called QUAST and dnadiff. We have developed a whole genome alignment difference classification scheme together with the program NucDiff for finding such differences. The proposed classification scheme is comprehensive and can be used by other tools. NucDiff performs comparably to QUAST and dnadiff but gives much more detailed results that can easily be visualized. NucDiff is freely available on https://github.com/uio-cels/NucDiff under the MPL license.

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The data shown below were collected from the profiles of 16 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 1%
Unknown 74 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 25%
Student > Ph. D. Student 13 17%
Student > Bachelor 10 13%
Student > Master 9 12%
Other 3 4%
Other 7 9%
Unknown 14 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 39%
Biochemistry, Genetics and Molecular Biology 18 24%
Immunology and Microbiology 4 5%
Engineering 3 4%
Medicine and Dentistry 2 3%
Other 5 7%
Unknown 14 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 12 January 2021.
All research outputs
#4,192,890
of 23,929,753 outputs
Outputs from BMC Bioinformatics
#1,543
of 7,460 outputs
Outputs of similar age
#70,666
of 315,044 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 103 outputs
Altmetric has tracked 23,929,753 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,460 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 79% 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 315,044 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.