The explain module takes a model and exports the feature importances of said model.Įach of the importance type is described here.ĭo note that the output of the explain module will always a gzipped TSV, just like the output of predict. Is only supported for CAPICE models that have been created using CAPICE v5.0.0 or greater! load_model( '/path/to/model.ubj') Usage for the explain module Note: To load in the (universal binary) json model, use the following commands: Has successfully trained on the input data, containing additional attributes CAPICE_version, vep_features and processable_features. xgb_classifier: Universal Binary Json or JSON file of.Outputs for training a new model:Ī file will be put out containing the following element: Train is optimized for a 2 class problem. Binarized label should be either 0 or 1,ĭepending on your labels of classification. Sample weight can be 1 for all samples if no sample weight should be applied. Please note that performance is validated on natively supported features. , REF, ALT, sample_weight, binarized_label (case sensitive)), user can provide their Since the input file features are not validated apart from 6 features ( CHROM, POS Outside of Predictions, this repository also provides users the availability to create new CAPICE like models according suggested_class: Suggested output class of the variant keeping in mind the score and gene.The higher the score, the more likely that the variant is score: The predicted CAPICE score for the variant.feature_type: The type of the feature of the variant as supplied.feature: The feature of the variant as supplied.gene_name: The gene name of the variant as supplied.chr: column containing the chromosome of a variant. Output of CAPICE prediction filesĪ file will be put out containing the following columns: scripts/convert_vep_vcf_to_tsv_capice.sh -i -o CAPICEĬAPICE can be run by using the following command:Ĭapice -help to show help on the command line. Then you have to convert the VEP output to TSV using our own BCFTools script: Note: Certain arguments might not be needed if training/predicting without using all possible features offered by CAPICE. IMPORTANT: Ensure the right files are used based on GRCH37 or GRCH38!!! fork -dont_skip -allow_non_variant -use_given_ref -exclude_predicted \ dir_cache -species "homo_sapiens" -assembly \ shift_3prime 1 -allele_number -refseq -total_length -no_stats -offline -cache \ vcf -compress_output gzip -sift s -polyphen s -numbers -symbol \ Vep -input_file -format vcf -output_file \ Performance on other Python versions is not Note: performance of CAPICE has been tested on Python 3.10. Sections will guide you through the steps needed for the variant annotation and the execution of making predictions The CAPICE software is also provided in this repository for running CAPICE in your own environment. Singularity could also work, but requires manual adjusting of the conversion script.Apptainer = 1.1.* (For: BCFTools singularity image).Including additional data (GRCh38) available here:.Including additional data (GRCh37) available here:.Including VEP cache (which needs to be unarchived!):.Mentioned features are used, some items in the list below can be skipped. Depending on whether GRCh37 and/or GRCh38 is used and whether all The software can be used as web service, as pre-computed scores or by installing the software locally, all describedĬAPICE can be used as online service at Requirements Method in pathogenicity estimation for variants of different molecular consequences and allele frequency. Performs consistently across diverse independent synthetic, and real clinical data sets. Model trained using a variety of genomic annotations used by CADD score and trained on the clinical significance. CAPICE : a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variationsĬAPICE is a computational method for predicting the pathogenicity of SNVs and InDels.
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