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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

For prediction

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.

  1. 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
  1. 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.

  2. 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 -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.