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331a473f1a
11.0
12.0
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bedlam
bedlam-name-fixes
dive
erik
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python-renames
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user/anddt/misc/discount
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user/anddt/network/strongswan
user/anddt/network/umurmur
user/anddt/network/yt-dlp
user/anddt/network/znc
user/anddt/office/gnumeric
user/anddt/office/sigil
user/anddt/system/FontAwesome
user/anddt/system/conky
user/anddt/system/fakeroot
user/anddt/system/hplip-plugin
user/anddt/system/postgresql
user/anddt/system/testdisk
user/anddt/system/worker
user/anddt/system/xen
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user/chris.willing/updates
user/dslackw/updates
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user/ponce/updates
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willysr
wxWidgets-wip
GPG-KEY
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Heinz Wiesinger
63daf9f79a
All: Support $PRINT_PACKAGE_NAME env var
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| README | 9c795f8652 academic/WEVOTE: Added (WEighted VOting Taxonomic idEntification). | hace 5 años |
| References | 9c795f8652 academic/WEVOTE: Added (WEighted VOting Taxonomic idEntification). | hace 5 años |
| WEVOTE.SlackBuild | 63daf9f79a All: Support $PRINT_PACKAGE_NAME env var | hace 3 años |
| WEVOTE.info | 9c795f8652 academic/WEVOTE: Added (WEighted VOting Taxonomic idEntification). | hace 5 años |
| slack-desc | 9c795f8652 academic/WEVOTE: Added (WEighted VOting Taxonomic idEntification). | hace 5 años |
README
WEVOTE (WEighted VOting Taxonomic idEntification)
WEVOTE is a method that classifies metagenome shotgun sequencing DNA
reads based on an ensemble of existing methods using k-mer based,
marker-based, and naive-similarity based approaches. The performance
evaluation based on fourteen simulated microbiome datasets
consistently demonstrates that WEVOTE achieves a high level of
sensitivity and precision compared to the individual methods across
different taxonomic levels. The major advantage of the WEVOTE pipeline
is that the user can make the choice of which tools to use in order to
explore the trade-off between sensitivity, precision, time and memory.
The WEVOTE architecture is flexible so that additional taxonomic tools
can be easily added, or the current tools can be replaced by improved
ones. Moreover, the score assigned to the taxon for each read
indicates the confidence level of the assignment. This information is
especially useful for the assessment of false positive annotations at
a particular taxonomic level. The classification score given by WEVOTE
can be used for any downstream analysis that requires the high
confidence of the annotated sequences.
Publication:
Ahmed A. Metwally, Yang Dai, Patricia W. Finn, and David L. Perkins.
WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial
Sequences.
PloS ONE, 2016.
WEVOTE is a method that classifies metagenome shotgun sequencing DNA
reads based on an ensemble of existing methods using k-mer based,
marker-based, and naive-similarity based approaches. The performance
evaluation based on fourteen simulated microbiome datasets
consistently demonstrates that WEVOTE achieves a high level of
sensitivity and precision compared to the individual methods across
different taxonomic levels. The major advantage of the WEVOTE pipeline
is that the user can make the choice of which tools to use in order to
explore the trade-off between sensitivity, precision, time and memory.
The WEVOTE architecture is flexible so that additional taxonomic tools
can be easily added, or the current tools can be replaced by improved
ones. Moreover, the score assigned to the taxon for each read
indicates the confidence level of the assignment. This information is
especially useful for the assessment of false positive annotations at
a particular taxonomic level. The classification score given by WEVOTE
can be used for any downstream analysis that requires the high
confidence of the annotated sequences.
Publication:
Ahmed A. Metwally, Yang Dai, Patricia W. Finn, and David L. Perkins.
WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial
Sequences.
PloS ONE, 2016.