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https://github.com/tneely/gta-hunter
Classification of GTA Genes using Machine Learning
https://github.com/tneely/gta-hunter
Last synced: 29 days ago
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Classification of GTA Genes using Machine Learning
- Host: GitHub
- URL: https://github.com/tneely/gta-hunter
- Owner: tneely
- Created: 2017-09-19T14:41:26.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-09-19T14:42:07.000Z (over 7 years ago)
- Last Synced: 2023-10-20T23:30:34.469Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 2.04 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
#GTA_Hunter
It is a bioinformatics tool that classifies homologs from GTA structural gene as true GTAs or viruses. The tool is written in `Python v3.5.1` and requires following Python packages.
- NumPy v1.11
- CVXOPT v1.1.8
**GTA_Hunter.py is the main file, and is used through command line using the following commands:**
```
usage: GTA_Hunter.py [-h] -g GTA -v VIRUS [-q QUERIES] [-k [KMER]]
[-p [PSEAAC]] [-y] [-m] [-w WEIGHT WEIGHT]
[-t CLUSTER_TYPE] [-d DIST] [-c C] [-x [XVAL]]
[-e KERNEL KERNEL] [-s]
Gene Classification Using SVM.
optional arguments:
-h, --help show this help message and exit
-g GTA, --GTA GTA The .faa or .fna training file for GTA genes.
-v VIRUS, --virus VIRUS
The .faa or .fna training file for viral genes.
-q QUERIES, --queries QUERIES
The .faa or .fna query file to be classified.
-k [KMER], --kmer [KMER]
The kmer size needed for feature generation
(default=4).
-p [PSEAAC], --pseaac [PSEAAC]
Expand feature set to include pseudo amino acid
composition. Specify lamba (default=3). Weight = 0.05.
-y, --physico Expand feature set to include physicochemical
composition.
-m, --min Print bare minimum results.
-w WEIGHT WEIGHT, --weight WEIGHT WEIGHT
Allows for weighting of training set. Will need to
specify the two pairwise distance files needed for
weighting (GTA first, then virus).
-t CLUSTER_TYPE, --cluster_type CLUSTER_TYPE
Specify 'farthest' or 'nearest' neighbors clustering
(default='farthest').
-d DIST, --dist DIST Specify the cutoff distance for clustering in the
weighting scheme (default=0.01).
-c C, --soft_margin C
The soft margin for the SVM (default=1.0).
-x [XVAL], --xval [XVAL]
Performs cross validation of training set. Specify
folds over 10 repetitions (default=5).
-e KERNEL KERNEL, --kernel KERNEL KERNEL
Specify kernel to be used and sigma if applicable
(i.e. gaussian) (default='linear', 0).
-s, --svs Show support vectors.
```
**Weight.py may also be used separately for cluster analysis:**
```
usage: Weight.py [-h] -p PROFILE_PATH -w PAIRWISE_PATH [-t CLUSTER_TYPE]
[-d CUTOFF] [-v] [-i] [-q] [-r R] [-c]
optional arguments:
-h, --help show this help message and exit
-p PROFILE_PATH, --profile_path PROFILE_PATH
The .faa or .fna file used in calculating pairwise
distances
-w PAIRWISE_PATH, --pairwise_path PAIRWISE_PATH
The .dist file of pairwise distances created by RAxML
-t CLUSTER_TYPE, --cluter_type CLUSTER_TYPE
Specify 'farthest' or 'nearest' neighbors clustering.
-d CUTOFF, --cutoff_distance CUTOFF
Specify the cutoff distance for clustering.
-v, --visualize Shows visualization of the clustered profiles.
-i, --histogram Shows histogram of pairwise distances.
-q, --quartiles Shows quartiles of pairwise distances.
-r R, --threshold R Show number of pairwise distances that fall below the
given threshold.
-c, --count Shows how many profiles clustered into how many
clusters.
```