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For training the i-vector extractor (i.e., the total variability matrix T), we have used all the multi-condition training data generated using the scripts provided by the REVERB challenge 2014 organizer.
For training the DNN models, we use the multi-condition training data generated from the clean training data (WSJCAM0 corpus) using the scripts provided by the organizer [32, 35].
All results can be reproduced using the scripts provided in Text S1.
For each node, edge and resulting overall graph the available metrics were calculated using the scripts provided within the Brain Connectivity Toolbox [49].
Sequences were clustered into gene groups with ≥80% identity using CD-hit [ 44] and the headers formatted for use with SRST2 using the scripts provided (cdhit_to_csv.py, csv_to_gene_db.py).
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Furthermore, to evaluate the detection performance differences between methods statistically, we conducted the bootstrap analysis using the script provided in a report of an earlier study (Green and Brenner, 2002).
To construct BLOP20, we used the scripts provided by (Lemaitre et al., 2011).
However, for this project, custom calibration curves were constructed using the MATLAB scripts provided in the original publication [ 11].
Counts of unitags that mapped to the same gene were summed and unitags that aligned with more than one gene were removed from the data set using the Perl scripts provided in SI File S2.
Bremer support was calculating using the script KWBremer.run (provided by Kipling Will, personal communication), a modified version of Bremer.run available on the TNT scripts page.
The IMM models and BLAST database required by PhymmBL were constructed using the "customGenomicData.pl" script provided in the PhymmBL package using the taxonomic information obtained from the Galaxy genome server.
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