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Data was then subjected to a second interrogation using a reverse database to further eliminate non-mycobacterial specific spectra.
False discovery rates were also estimated using a reverse database as decoy.
To ensure specificity, we apply a standard decoy database strategy (Bradshaw et al., 2006) using a reverse database.
Using a reverse database it is possible to calculate a false discovery rate (FDR) from matches to the forwards and decoy (reverse) database above a given APS protein threshold.
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Using a reverse decoy database approach, the dataset was then filtered to a 1% false discovery rate (FDR); approximately 2000 proteins were identified with high confidence.
PSMs were filtered using Percolator (implemented in Proteome Discoverer) to control false discovery rates (FDR) to <1% as determined using a reverse-sequence decoy database.
Total RNA was extracted using Trizol reagent and reverse transcribed using a Reverse Transcription System (Promega).
After the peptide mixture is separated using a reverse-phase liquid chromatography electrospray ionization mass spectrometer, the peptides are identified by sequence database analysis [ 16].
The pipeline uses a concatenated reverse database [36] to estimate the FDR and incorporates filtering steps that target pseudogenes, repeat elements and sequence conservation across genomes to find additional support for the assignments made by the database search algorithm.
A reverse database strategy [33] was employed by concatenating reversed protein sequences for each database entry in SEQUEST.
The combined database was concatenated with a reverse database composed of all protein sequences in reversed order.
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