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https://doi.org/10.17504/proto...
Article . 2016 . Peer-reviewed
License: CC BY
Data sources: Crossref
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K-mer-based host screening v1

Authors: Bonnie Hurwitz; not provided Ken Youens-Clark;

K-mer-based host screening v1

Abstract

K-mer-based approaches to determining sequence similiarity can befaster than traditional use of BLAST. This application usesJellyfishto index host FASTA files, then uses the mode value (default 2)of the number of matching k-mers (default k=20) from the input sequenceto determine whether the sequence is similar enough to the host sequence to be rejected. The output of the app is: "screened" directory containing the sequences from each file that were found to be dissimlar to the host "rejected" directory containing the sequence from each file that were too similar to the host "jf" directory containing Jellyfish indexes of each "host" file (useful for later runs with other files to skip recreating) "kmer" directory containing k-mers of query sequences and ".loc" file showing the number of ".kmer" lines associated to each sequence Code is freely available atGithub.

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid