QTG_Finder (version 1.1)
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.
|Gene1 in QTL1|
|Gene2 in QTL1|
|Gene3 in QTL1|
|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.
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'
python QTG_Finder_predict.py -h
For analyses and replications
The 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.