Running TRANSIT¶
GUI Mode¶
To run TRANSIT in GUI mode (should be the same on Linux, Windows and MacOS), from the command line run:
python PATH/src/transit.py
where PATH is the path to the TRANSIT installation directory. You might be able to double-click on icon for transit.py, if your OS associates .py files with python and automatically runs them. Note, because TRANSIT has a graphical user interface, if you are trying to run TRANSIT across a network, for example, running on a unix server but displaying on a desktop machine, you will probably need to use ‘ssh -Y’ and a local X11 client (like Xming or Cygwin/X on PCs).
Command line Mode¶
TRANSIT can also be run from the command line, without the GUI interface. This is convenient if you want to run many analyses in batch, as you can write a script that automatically runs that automatically runs TRANSIT from the command line. TRANSIT expects the user to specify which analysis method they wish to run. The user can choose from “gumbel”, “hmm”, or “resampling”. By choosing a method, and adding the “-h” flag, you will get a list of all the necessary parameters and optional flags for the chosen method:
python PATH/src/transit.py gumbel -h
Gumbel¶
To run the Gumbel analysis from the command line, type “python PATH/src/transit.py gumbel” followed by the following arguments:
| Argument | Type | Description | Default | Example |
|---|---|---|---|---|
| annotation | Required | Path to annotation file in .prot_table format | genomes/H37Rv. prot_table | |
| control_files | Required | Comma-separate d list of paths to the *.wig replicate datasets | data/glycerol_reads_rep1.w ig,data/glycer ol_reads_rep 2.wig | |
| output_file | Required | Name of the output file with the results. | results/gumbel _glycerol.dat | |
| -s SAMPLES | Optional | Number of samples to take. | 10000 | -s 20000 |
| -m MINREAD | Optional | Smallest read-count considered to be an insertion. | 1 | -m 2 |
| -b BURNIN | Optional | Burn in period, Skips this number of samples before getting estimates. See documentation. | 500 | -b 100 |
| -t TRIM | Optional | Number of samples to trim. See documentation. | 1 | -t 2 |
| -r REP | Optional | How to handle replicates read-counts: ‘Sum’ or ‘Mean’. | Sum | -r Mean |
| -iN IGNOREN | Optional | Ignore TAs occuring at X% of the N terminus. | 5 | -iN 0 |
| -iC IGNOREC | Optional | Ignore TAs occuring at X% of the C terminus. | 5 | -iC 10 |
python PATH/src/transit.py gumbel genomes/H37Rv.prot_table data/glycerol_reads_rep1.wig,data/glycerol_reads_rep2.wig test_console_gumbel.dat -s 20000 -b 1000
Tn5 Gaps¶
To run the Tn5 Gaps analysis from the command line, type “python PATH/src/transit.py tn5gaps” followed by the following arguments:
Argument Type Description Default Example annotation Required Path to annotation file in .prot_table format genomes/Salmonella- Ty2.prot_table control_files Required Comma-separated list of paths to the *.wig replicate datasets data/salmonella_2122_rep1.wig,data/salmonella_2122_rep2.wig output_file Required Name of the output file with the results. results/test_console_tn5gaps.dat -m MINREAD Optional Smallest read- count considered to be an insertion. 1 -m 2 -r REP Optional How to handle replicates read-counts: ‘Sum’ or ‘Mean’. Sum -r Sum
Example Tn5 Gaps command:
python PATH/src/transit.py tn5gaps genomes/Salmonella-Ty2.prot_table data/salmonella_2122_rep1.wig,data/salmonella_2122_rep2.wig results/test_console_tn5gaps.dat -m 2 -r Sum
Example HMM command:
python PATH/src/transit.py hmm genomes/H37Rv.prot_table data/glycerol_reads_rep1.wig,data/glycerol_reads_rep2.wig test_console_hmm.dat -r Sum
Resampling¶
To run the Resampling analysis from the command line, type “python PATH/src/transit.py resampling” followed by the following arguments:
| Argument | Type | Description | Default | Example |
|---|---|---|---|---|
| annotation | Required | Path to annotation file in .prot_table format | genomes/H37Rv. prot_table | |
| control_files | Required | Comma-separate d list of paths to the *.wig replicate datasets for the control condition | data/glycerol_reads_rep1.w ig,data/glycer ol_reads_rep 2.wig | |
| exp_files | Required | Comma-separate d list of paths to the *.wig replicate datasets for the experimental condition | data/cholester ol_reads_rep 1.wig,data/cho lesterol_read s_rep2.wig | |
| output_file | Required | Name of the output file with the results. | results/gumbel _glycerol.dat | |
| -s SAMPLES | Optional | Number of permutations performed. | 10000 | -s 5000 |
| -H | Optional | Creates histograms of the permutations for all genes. | Not set | -H |
| -a | Optional | Performs adaptive appoximation to resampling. | Not set | -a |
| -N | Optional | Select which normalizing procedure to use. Can choose between ‘TTR’, ‘nzmean’, ‘totreads’, ‘zinfnb’, ‘betageom’, and ‘nonorm’. See the parameters section for the Re-sampling method for a description of these normalization options. | nzmean | -N nzmean |
| -iN IGNOREN | Optional | Ignore TAs occuring at X% of the N terminus. | 5 | -iN 0 |
| -iC IGNOREC | Optional | Ignore TAs occuring at X% of the C terminus. | 5 | -iC 10 |
Example Resampling command:
python PATH/src/transit.py resampling genomes/H37Rv.prot_table data/glycerol_reads_rep1.wig,data/glycerol_reads_rep2.wig data/cholesterol_reads_rep1.wig,data/cholesterol_reads_rep2.wig,data/cholesterol_reads_rep3.wig test_console_resampling.dat -H -s 10000 -N nzmean