QTG-Finder is a novel machine-learning pipeline to prioritize causal genes for QTLs identified by linkage mapping. We trained QTG-Finder models for Arabidopsis and rice based on the known causal genes from each species, respectively. By utilizing additional information like poly-morphisms, function annotation, co-function network, and paralog copy number, the models can rank QTL genes to prioritize causal genes.
Authors: Fan Lin, March 2018 Jue Fan, March 2018
The source code and input files can be found in the 'prediction' folder. Running the 'QTG_Finder_predict.py' will require a QTL gene list provided by the user.
- Users can prepare the QTL gene list as a single column table (.csv). See "SSQ_batch_QTL_genes.csv” for a example.
| // |
| QTL1 name |
| Gene1 in QTL1 |
| Gene2 in QTL1 |
| Gene3 in QTL1 |
| … |
| // |
| QTL2 name |
| Gene1 in QTL2 |
| Gene2 in QTL2 |
| Gene3 in QTL2 |
| … |
-
Make sure you have unzipped pre-calculated models "AT_model.dat" or "OS_model.dat" to your working directory. "AT_model.dat" is the Arabidopsis model. "OS_model.dat”is the rice model.
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Usage ="QTG_Finder_predict.py -gl QTL_gene_list -sp species_abbreviation"
QTL_gene_list: this is the list of QTL genes to be ranked. See 'SSQ_batch_QTL_genes.csv' for a example
species_abbreviation: "AT" for Arabidopsis; "OS" for rice
As a example,
python QTG_Finder_predict.py -gl SSQ_batch_QTL_genes.csv -sp 'AT'For help,
python QTG_Finder_predict.py -hThe source code and input files for cross-validation, feature importance analysis, literature validation and category analysis can be found in the ‘tests' folder. The usage of each scripts (.py) is described in the beginnings of the file.