We used the MCL-clustering algorithm with inflation parameter set to 20 to capture also small clusters.
The MCL parameters were varied to create the optimum core genome, using the lowest inflation parameter possible to get the highest number of homology groups that can be identified in every isolate, maximising the core genome.
The graph partitioning procedure, using default MCL parameters (I = 2), generated 11 highly homogeneous clusters (Fig. 1C F).
To remove redundant sequences from within this data set, we applied the Markov cluster (MCL) algorithm to group together similar sequences using the default inflation parameter and a BLAST bit-score cutoff of 5 (Enright et al., 2002).
To summarize in brief, orthologous clusters of proteins were estimated from 16 Ascomycota genomes using the program MCL (Enright et al. 2002) across 13 inflation parameters (1.1, 1.2, 1.3, 1.4, 1.5, 1.7, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0).
All remaining eukaryotic homologues were clustered into protein families with MCL (v1.008 Enright et al. 2002) using the default parameters.
using the following parameters.
OrthoMCL clustering analyses were performed using the following parameters: p-value cut-off = 1 × 10−5; Percent Identity cut-off = 0; Percent match cut-off = 80; MCL Inflation = 1.5; Maximum weight = 316.
Regrouping by strand was performed by using the BLAST-MCL procedure, but allowing searches on the complementary strand and setting parameter 'I' to 1.4.
The regrouping procedures summarized in Table 2 were made as follows: Regrouping by sequence was made by using the BLAST-MCL procedure (see above) on all SCRs, but in a less stringent way, i.e. setting parameter 'I' to 1.4.
The participants completed the perceived ease of use and perceived usefulness questionnaires after using the GPS-based MCL system.
