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# classconcordance #
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Description
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python script to compute Classification Concordance based of the independent taxonomic classification of read 1 and read 2. Useful to infer taxonomic classification accuracy at all major taxonomic ranks on samples where the truth is unknown.

Dependencies
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python 2.7+ (but not python 3.x)
numpy 1.9.2+

Inputs
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Read-pair classifications: Tab-delimited file with 3 fields: <Fragment ID> <Read1 taxID> <Read2 taxID>
Taxonomy table: Available at ftp://ftp.nygenome.org/taxmaps/Indexes/ for the indexes under the same directory or custom-generated using the taxMaps-taxtbl script available at https://github.com/nygenome/taxmaps

Usage
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cat <read-pair classification> | ./classconcordance -t <taxonomy table>

Output
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Tab-delimited file with 2 fields: <Taxonomic Rank> <Classification Concordance>

Notes
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Unclassified reads must have "0" as taxID.

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# precision_sensitivity_f1.pl #
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Description
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perl script to compute sensitivity, precision and F-score of taxonomic classification at all major taxonomic ranks for samples where the truth is known for every read/read-pair.

Dependencies
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Perl 5.16.3+

Inputs
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Read classifications and known truth: Tab-delimited file with 3 fields: <Read ID> <Classification taxID> <Truth taxID>
Taxonomy table: Available at ftp://ftp.nygenome.org/taxmaps/Indexes/ for the indexes under the same directory or custom-generated using the taxMaps-taxtbl script available at https://github.com/nygenome/taxmaps

Usage
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perl precision_sensitivity_f1.pl -i <read classification + truth> -tt <taxonomy table>

Output
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Tab-delimited file with 7 fields: <Rank> <Correct> <Incorrect> <Unclassified> <Recall> <Precision> <F-score>

Notes
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Unclassified reads must have "0" as taxID.
