fix conditing window size (was 500kb not 500)

docs/annotations.md

@@ -15,7 +15,7 @@ Furthermore, the pipeline outputs one annotation file for VEP, CADD, DeepRiPe, D
 
 The pipeline uses left-normalized bcf files containing variant information, a reference fasta file as well as a text file that maps data blocks to chromosomes as input. It is expected that the bcf files contain the columns "CHROM" "POS" "ID" "REF" and "ALT". 
 Any other columns, including genotype information are stripped from the data before annotation tools are used on the data. The variants may be split into several vcf files for each chromosome and each "block" of data. 
-The filenames should then contain the corresponding chromosome and block number. The pattern of the file names, as well as file structure may be specified in the corresponding [config file](https://github.com/PMBio/deeprvat/blob/main/pipelines/config/deeprvat_annotation_config.yaml). The pipeline also requires input data and repositories descried in [requirements](#requirements).
+The filenames should then contain the corresponding chromosome and block number. The pattern of the file names, as well as file structure may be specified in the corresponding [config file](https://github.com/PMBio/deeprvat/blob/main/example/config/deeprvat_annotation_config.yaml). The pipeline also requires input data and repositories descried in [requirements](#requirements).
 
 (requirements)=
 ## Requirements
@@ -27,7 +27,7 @@ BCFtools as well as HTSlib should be installed on the machine,
 - [faatpipe](https://github.com/HealthML/faatpipe), and the
 - [vep-plugins repository](https://github.com/Ensembl/VEP_plugins/)
 
-should be installed for running the pipeline, together with the [plugins](https://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html) for primateAI and spliceAI. Annotation data for CADD, spliceAI and primateAI should be downloaded. The path to the data may be specified in the corresponding [config file](https://github.com/PMBio/deeprvat/blob/main/pipelines/config/deeprvat_annotation_config.yaml). 
+should be installed for running the pipeline, together with the [plugins](https://www.ensembl.org/info/docs/tools/vep/script/vep_plugins.html) for primateAI and spliceAI. Annotation data for CADD, spliceAI and primateAI should be downloaded. The path to the data may be specified in the corresponding [config file](https://github.com/PMBio/deeprvat/blob/main/example/config/deeprvat_annotation_config.yaml). 
 Download paths:
 - [CADD](https://cadd.bihealth.org/download): "All possible SNVs of GRCh38/hg38" and "gnomad.genomes.r3.0.indel.tsv.gz" incl. their Tabix Indices
 - [SpliceAI](https://basespace.illumina.com/s/otSPW8hnhaZR): "genome_scores_v1.3"/"spliceai_scores.raw.snv.hg38.vcf.gz" and "spliceai_scores.raw.indel.hg38.vcf.gz" 
@@ -37,7 +37,7 @@ Also a reference GTF file containing transcript annotations should be provided,
 
 
 ## Configure the annotation pipeline
-The snakemake annotation pipeline is configured using a yaml file with the format akin to the [example file](https://github.com/PMBio/deeprvat/blob/main/pipelines/config/deeprvat_annotation_config.yaml).
+The snakemake annotation pipeline is configured using a yaml file with the format akin to the [example file](https://github.com/PMBio/deeprvat/blob/main/example/config/deeprvat_annotation_config.yaml).
 
 The config above would use the following directory structure:
 ```shell
@@ -100,7 +100,7 @@ Data for VEP plugins and the CADD cache are stored in `annotation data`.
     mamba env create -f repo_dir/kipoi-veff2/environment.minimal.linux.yml
     mamba env create -f deeprvat/deeprvat_annotations.yml
     ```
-  If you already have some of the needed repositories on your machine you can edit the paths in the [config](https://github.com/PMBio/deeprvat/blob/main/pipelines/config/deeprvat_annotation_config.yaml).
+  If you already have some of the needed repositories on your machine you can edit the paths in the [config](https://github.com/PMBio/deeprvat/blob/main/example/config/deeprvat_annotation_config.yaml).
 
 
 - Inside the annotation directory create a directory `annotation_dir` and download/link the prescored files for CADD, SpliceAI, and PrimateAI (see [requirements](#requirements))

docs/preprocessing.md

@@ -44,7 +44,7 @@ pip install -e .
 ## Configure preprocessing
 
 The snakemake preprocessing is configured using a yaml file with the format below.
-An example file is included in this repo: [example config](https://github.com/PMBio/deeprvat/blob/main/pipelines/config/deeprvat_preprocess_config.yaml).
+An example file is included in this repo: [example config](https://github.com/PMBio/deeprvat/blob/main/example/config/deeprvat_preprocess_config.yaml).
 
 ```yaml
 # What chromosomes should be processed

docs/pretrained_models.md

@@ -7,7 +7,7 @@ For using the pretrained DeepRVAT model provided as part of the package, or a cu
 
 Configuration parameters must be specified in `deeprvat_input_pretrained_models_config.yaml`, see [example file](https://github.com/PMBio/deeprvat/blob/main/example/config/deeprvat_input_pretrained_models_config.yaml). For details on the meanings of the parameters and the format of input files, see [here](input_data).
 
-To use pretrained models, you must specify `use_pretrained_models: True` in your `deeprvat_input_pretrained_models_config.yaml` configuration file. Additionally, provide the path to pretrained models (an output of the training pipeline) in the parameter `pretrained_model_path`. Within the `pretrained_model_path` directory, there must be a `config.yaml` file in that directory with the following set of specified keys that were used for training the pretrained models; `rare_variant_annotations`, `training_data_thresholds`, and `model` . See [example file](https://github.com/PMBio/deeprvat/blob/main/pretrained_models/config.yaml).
+To use pretrained models, you must specify `use_pretrained_models: True` in your `deeprvat_input_pretrained_models_config.yaml` configuration file. Additionally, provide the path to pretrained models (an output of the training pipeline) in the parameter `pretrained_model_path`. Within the `pretrained_model_path` directory, there must be a `config.yaml` file in that directory with the following set of specified keys that were used for training the pretrained models; `rare_variant_annotations`, `training_data_thresholds`, and `model` . See [example file](https://github.com/PMBio/deeprvat/blob/main/pretrained_models/model_config.yaml).
 
 Below outlines the configuration parameters specified in `deeprvat_input_pretrained_models_config.yaml`.
 

pipelines/association_testing_control_for_common_variants.snakefile

@@ -91,7 +91,7 @@ phecode_dict = {'Apolipoprotein_A': 30630,
 
 gtf_file = 'gencode.v34lift37.annotation.gtf.gz'
 genotype_base_dir = 'genotypes/'
-padding = 500
+padding = 500000
 
 burden_phenotype = phenotypes[0]