Annotation Infrastructure
Annotation is an essential step in any genomic analysis. It is the process of assigning attributes or properties to a set of objects that an analyst studies. For example, one can annotate the genetic variants identified in a group of case and control individuals in a genetic study with a prediction of pathogenicity of the variants and estimates for the conservation at the loci of the variants.
GPF has a well-developed infrastructure for annotation of genetic variants. Annotation is carried out when importing studies into the system or through the use of CLI tools. This document will explain how to configure and utilize the annotation infrastructure.
Pipeline configuration
A series of annotations (performed by annotators) is called a “pipeline”. Pipelines are defined through configuration files in the YAML format, in the form of a list of annotators. Each annotator can be additionally configured depending on its type.
Annotators can be written as so:
- <annotator type>:
setting 1: value
setting 2: other value
...
Some attributes are general and some are annotator specific. General ones include: attributes and input_annotatable
Syntax shortcuts are available, such as:
- <annotator type>
or
- <annotator type>: <resource id>
These short forms however lack the ability to configure the annotator’s settings.
Preamble
Other than a list of annotators, annotation configs can also have a preamble section that holds additional information about the pipeline useful for both human readers and documentation tools.
Below is an example of a pipeline config with a preamble section.
Note that the annotators must be placed in a annotators section if a preamble is added.
preamble:
summary: phyloP hg38 annotation
description: Annotates with all available HG38 phyloP scores.
input_reference_genome: hg38/genomes/GRCh38-hg38
metadata:
author: Me
customField: "The metadata section can hold arbitrary key/value pairs - you can put anything here."
customField2: someCustomValue
customNestedDictionary:
key1: value1
annotators:
- position_score: hg38/scores/phyloP100way
- position_score: hg38/scores/phyloP30way
- position_score: hg38/scores/phyloP20way
- position_score: hg38/scores/phyloP7way
Annotators
This is a list of all available annotators and their settings in the GPF system.
Annotator attribute fields
These are the fields that can be set when configuring an annotator’s attributes.
Setting |
Description |
|---|---|
source |
String. The source from which to take the attribute. |
name |
String. How the attribute will be labeled in the output annotation. |
destination |
String. Deprecated variation of |
internal |
Boolean. Whether to discard the attribute when writing the output file. Useful for temporary attributes used to calculate other attributes. |
input_annotatable |
String. What annotatable object to use instead of the default one read from the input. Used with liftover and normalize allele annotators. |
value_transform |
String. Python function to evaluate on each value of the attribute. Examples: |
Score annotators
The attributes of these annotators generally follow the pattern below:
- source: <source score ID>
name: <destination attribute name>
The available scores are those configured in the resource used with the score annotator.
source is the ID of the score as it is configured in its resource, and name is how it will be labeled in the output annotation.
Aggregators
mean
median
max
min
mode
join (i.e., join(;))
list
dict
concatenate
Position score
Annotate with a position score resource.
- position_score:
resource_id: <position score resource ID>
attributes:
- source: <source score ID>
name: <destination attribute name>
position_aggregator: <aggregator to use for INDELs>
NP score
Annotate with a nucleotide polymorphism score resource.
- np_score:
resource_id: <NP-score resource ID>
attributes:
- source: <source score ID>
name: <destination attribute name>
position_aggregator: <aggregator to use for INDELs>
Allele score
Annotate with an allele score resource.
- allele_score:
resource_id: <allele score resource ID>
attributes:
- source: <source score ID>
name: <destination attribute name>
Effect annotator
Predicts the variant’s effect on proteins (i.e. missense, synonymous, LGD, etc.). The attributes the annotator will output are the following:
- worst_effect
The worst effect across all transcripts.
- gene_effects
Effect types for each gene.
- effect_details
Effect details for each affected transcript.
- effect_annotator:
genome: <reference genome resource ID>
gene_models: <gene models resource ID>
Liftover annotator
Lifts over a variant from one reference genome to another.
The product is an “annotatable” (an object annotators can work on) in the target reference genome.
This produced annotatable is labeled liftover_annotatable and can be passed to other annotators using the input_annotatable setting.
- liftover_annotator:
chain: liftover/hg38ToHg19
source_genome: hg38/genomes/GRCh38-hg38
target_genome: hg19/genomes/GATK_ResourceBundle_5777_b37_phiX174
attributes:
- source: liftover_annotatable
name: hg19_annotatable
internal: true
Normalize allele annotator
Normalize an allele using the algorithm defined here: https://genome.sph.umich.edu/wiki/Variant_Normalization
Similar to the liftover annotator, produces an “annotatable” object called normalized_allele.
- normalize_allele_annotator:
genome: hg38/genomes/GRCh38-hg38
Gene score annotator
Annotate a variant with a gene score.
- gene_score_annotator:
resource_id: <gene score resource ID>
input_gene_list: <gene list to use to match variants (see below)>
attributes:
- source: <source score ID>
name: <destination attribute name>
gene_aggregator: <aggregator type>
Note
Input gene list is a list of genes that must be present in the annotation context.
Gene lists are produced by the effect annotator, therefore gene score annotation is dependent on an effect annotator being present earlier in the pipeline.
Effect annotators currently provide two gene lists - gene_list and LGD_gene_list.
Gene set annotator
Used to annotate whether a variant belongs to a certain gene set.
- gene_set_annotator:
resource_id: <gene set collection resource ID>
gene_set_id: <gene set id>
input_gene_list: <gene list to use to match variants>
Command line tools
Three annotation tools are provided with GPF - annotate_columns, annotate_vcf and annotate_schema2_parquet.
Each of these handles a different format, but apart from some differences in the arguments that can be passed, they work in a similar way.
Across all three, input data and a pipeline configuration file are the mandatory arguments that must be provided.
Additionally, a number of optional arguments exist, which can be used to modify much of the behaviour of these tools.
All three tools are capable of parallelizing their workload, and will attempt to do so by default.
Also provided is the annotate_doc tool, which produces a human-readable HTML document from a given pipeline configuration.
The purpose of this HTML document is to display the pipeline in an easy-to-read layout.
Notes on usage
When parallelizing is used, a directory for storing task flags and task logs will be created in your working directory. If you wish to re-run the annotation, it is necessary to remove this directory as the flags inside it will prevent the tasks from running.
The option to reannotate data is provided. This is useful when you wish to modify only specific columns of an already annotated piece of data - for example to update a score column whose score resource has received a new version. To carry this out in the
annotate_columnsandannotate_vcftools, you will have to use to provide the old annotation pipeline through the--reannotateflag. Forannotate_schema2_parquet, this is done automatically, as the annotation pipeline is stored in its metadata.
annotate_columns
This tool is used to annotate DSV (delimiter-separated values) formats - CSV, TSV, etc. It expects a header to be provided, from which it will attempt to identify relevant columns - chromosome, position, reference, alternative, etc. If the file has been indexed using Tabix, the tool will parallelize its workload by splitting it into tasks by region size.
Example usage of annotate_columns
$ annotate_columns.py input.tsv annotation.yaml
Options for annotate_columns
$ annotate_columns --help
usage: annotate_columns [-h] [-r REGION_SIZE] [-w WORK_DIR] [-o OUTPUT]
[-in_sep INPUT_SEPARATOR] [-out_sep OUTPUT_SEPARATOR]
[--reannotate REANNOTATE] [--batch-mode] [-ar]
[-g GRR_FILENAME] [--grr-directory GRR_DIRECTORY]
[-R REFERENCE_GENOME_RESOURCE_ID]
[-G GENE_MODELS_RESOURCE_ID]
[--col_vcf_like COL_VCF_LIKE]
[--col_variant COL_VARIANT]
[--col_cnv_type COL_CNV_TYPE]
[--col_pos_beg COL_POS_BEG] [--col_ref COL_REF]
[--col_alt COL_ALT] [--col_pos COL_POS]
[--col_location COL_LOCATION] [--col_chrom COL_CHROM]
[--col_pos_end COL_POS_END] [-j JOBS]
[-N DASK_CLUSTER_NAME] [-c DASK_CLUSTER_CONFIG_FILE]
[--tasks-log-dir LOG_DIR] [-t TASK_IDS [TASK_IDS ...]]
[--keep-going] [--force] [-d TASK_STATUS_DIR]
[--verbose]
[input] [pipeline] [{run,list,status}]
Annotate columns
positional arguments:
input the input column file (default: -)
pipeline The pipeline definition file. By default, or if the
value is gpf_instance, the annotation pipeline from
the configured gpf instance will be used. (default:
context)
options:
-h, --help show this help message and exit
-r REGION_SIZE, --region-size REGION_SIZE
region size to parallelize by (default: 300000000)
-w WORK_DIR, --work-dir WORK_DIR
Directory to store intermediate output files in
(default: annotate_columns_output)
-o OUTPUT, --output OUTPUT
Filename of the output result (default: None)
-in_sep INPUT_SEPARATOR, --input-separator INPUT_SEPARATOR
The column separator in the input (default: )
-out_sep OUTPUT_SEPARATOR, --output-separator OUTPUT_SEPARATOR
The column separator in the output (default: )
--reannotate REANNOTATE
Old pipeline config to reannotate over (default: None)
--batch-mode
-ar, --allow-repeated-attributes
Rename repeated attributes instead of raising an
error. (default: False)
-g GRR_FILENAME, --grr-filename GRR_FILENAME, --grr GRR_FILENAME
The GRR configuration file. If the argument is absent,
the a GRR repository from the current genomic context
(i.e. gpf_instance) will be used or, if that fails,
the default GRR configuration will be used. (default:
None)
--grr-directory GRR_DIRECTORY
Local GRR directory to use as repository. (default:
None)
-R REFERENCE_GENOME_RESOURCE_ID, --reference-genome-resource-id REFERENCE_GENOME_RESOURCE_ID, --ref REFERENCE_GENOME_RESOURCE_ID
The resource id for the reference genome. If the
argument is absent the reference genome from the
current genomic context will be used. (default: None)
-G GENE_MODELS_RESOURCE_ID, --gene-models-resource-id GENE_MODELS_RESOURCE_ID, --genes GENE_MODELS_RESOURCE_ID
The resource is of the gene models resource. If the
argument is absent the gene models from the current
genomic context will be used. (default: None)
--col_vcf_like COL_VCF_LIKE
The column name that stores vcf_like (default:
vcf_like)
--col_variant COL_VARIANT
The column name that stores variant (default: variant)
--col_cnv_type COL_CNV_TYPE
The column name that stores cnv_type (default:
cnv_type)
--col_pos_beg COL_POS_BEG
The column name that stores pos_beg (default: pos_beg)
--col_ref COL_REF The column name that stores ref (default: ref)
--col_alt COL_ALT The column name that stores alt (default: alt)
--col_pos COL_POS The column name that stores pos (default: pos)
--col_location COL_LOCATION
The column name that stores location (default:
location)
--col_chrom COL_CHROM
The column name that stores chrom (default: chrom)
--col_pos_end COL_POS_END
The column name that stores pos_end (default: pos_end)
--verbose, -v, -V
Task Graph Executor:
-j JOBS, --jobs JOBS Number of jobs to run in parallel. Defaults to the
number of processors on the machine (default: None)
-N DASK_CLUSTER_NAME, --dask-cluster-name DASK_CLUSTER_NAME, --dcn DASK_CLUSTER_NAME
The named of the named dask cluster (default: None)
-c DASK_CLUSTER_CONFIG_FILE, --dccf DASK_CLUSTER_CONFIG_FILE, --dask-cluster-config-file DASK_CLUSTER_CONFIG_FILE
dask cluster config file (default: None)
--tasks-log-dir LOG_DIR
Path to directory where to store tasks' logs (default:
./.task-log)
Execution Mode:
{run,list,status} Command to execute on the import configuration. run -
runs the import process list - lists the tasks to be
executed but doesn't run them status - synonym for
list (default: run)
-t TASK_IDS [TASK_IDS ...], --task-ids TASK_IDS [TASK_IDS ...]
--keep-going Whether or not to keep executing in case of an error
(default: False)
--force, -f Ignore precomputed state and always rerun all tasks.
(default: False)
-d TASK_STATUS_DIR, --task-status-dir TASK_STATUS_DIR, --tsd TASK_STATUS_DIR
Directory to store the task progress. (default: .)
annotate_vcf
This tool is used to annotate files in the VCF format. If the file has been indexed using Tabix, the tool will parallelize its workload by splitting it into tasks by region size.
Example usage of annotate_vcf
$ annotate_vcf.py input.vcf.gz annotation.yaml
Options for annotate_vcf
$ annotate_vcf --help
usage: annotate_vcf [-h] [-r REGION_SIZE] [-w WORK_DIR] [-o OUTPUT]
[--reannotate REANNOTATE] [-ar] [-g GRR_FILENAME]
[--grr-directory GRR_DIRECTORY]
[-R REFERENCE_GENOME_RESOURCE_ID]
[-G GENE_MODELS_RESOURCE_ID] [-j JOBS]
[-N DASK_CLUSTER_NAME] [-c DASK_CLUSTER_CONFIG_FILE]
[--tasks-log-dir LOG_DIR] [-t TASK_IDS [TASK_IDS ...]]
[--keep-going] [--force] [-d TASK_STATUS_DIR] [--verbose]
[input] [pipeline] [{run,list,status}]
Annotate VCF
positional arguments:
input the input vcf file (default: -)
pipeline The pipeline definition file. By default, or if the
value is gpf_instance, the annotation pipeline from
the configured gpf instance will be used. (default:
context)
options:
-h, --help show this help message and exit
-r REGION_SIZE, --region-size REGION_SIZE
region size to parallelize by (default: 300000000)
-w WORK_DIR, --work-dir WORK_DIR
Directory to store intermediate output files (default:
annotate_vcf_output)
-o OUTPUT, --output OUTPUT
Filename of the output VCF result (default: None)
--reannotate REANNOTATE
Old pipeline config to reannotate over (default: None)
-ar, --allow-repeated-attributes
Rename repeated attributes instead of raising an
error. (default: False)
-g GRR_FILENAME, --grr-filename GRR_FILENAME, --grr GRR_FILENAME
The GRR configuration file. If the argument is absent,
the a GRR repository from the current genomic context
(i.e. gpf_instance) will be used or, if that fails,
the default GRR configuration will be used. (default:
None)
--grr-directory GRR_DIRECTORY
Local GRR directory to use as repository. (default:
None)
-R REFERENCE_GENOME_RESOURCE_ID, --reference-genome-resource-id REFERENCE_GENOME_RESOURCE_ID, --ref REFERENCE_GENOME_RESOURCE_ID
The resource id for the reference genome. If the
argument is absent the reference genome from the
current genomic context will be used. (default: None)
-G GENE_MODELS_RESOURCE_ID, --gene-models-resource-id GENE_MODELS_RESOURCE_ID, --genes GENE_MODELS_RESOURCE_ID
The resource is of the gene models resource. If the
argument is absent the gene models from the current
genomic context will be used. (default: None)
--verbose, -v, -V
Task Graph Executor:
-j JOBS, --jobs JOBS Number of jobs to run in parallel. Defaults to the
number of processors on the machine (default: None)
-N DASK_CLUSTER_NAME, --dask-cluster-name DASK_CLUSTER_NAME, --dcn DASK_CLUSTER_NAME
The named of the named dask cluster (default: None)
-c DASK_CLUSTER_CONFIG_FILE, --dccf DASK_CLUSTER_CONFIG_FILE, --dask-cluster-config-file DASK_CLUSTER_CONFIG_FILE
dask cluster config file (default: None)
--tasks-log-dir LOG_DIR
Path to directory where to store tasks' logs (default:
./.task-log)
Execution Mode:
{run,list,status} Command to execute on the import configuration. run -
runs the import process list - lists the tasks to be
executed but doesn't run them status - synonym for
list (default: run)
-t TASK_IDS [TASK_IDS ...], --task-ids TASK_IDS [TASK_IDS ...]
--keep-going Whether or not to keep executing in case of an error
(default: False)
--force, -f Ignore precomputed state and always rerun all tasks.
(default: False)
-d TASK_STATUS_DIR, --task-status-dir TASK_STATUS_DIR, --tsd TASK_STATUS_DIR
Directory to store the task progress. (default: .)
annotate_schema2_parquet
This tool is used to annotate Parquet datasets partitioned according to GPF’s schema2 format. It expects a directory of the dataset as input.
The tool will always parallelize the annotation, unless explicitly disabled using the -j 1 argument.
Unlike the other tools, reannotation will be carried out automatically if a previous annotation is detected in the dataset.
Example usage of annotate_schema2_parquet
$ annotate_schema2_parquet.py input_parquet_dir annotation.yaml
Options for annotate_schema2_parquet
$ annotate_schema2_parquet --help
usage: annotate_schema2_parquet [-h] [-r REGION_SIZE] [-w WORK_DIR]
[-o OUTPUT] [-i FULL_REANNOTATION] [-ar]
[-g GRR_FILENAME]
[--grr-directory GRR_DIRECTORY]
[-R REFERENCE_GENOME_RESOURCE_ID]
[-G GENE_MODELS_RESOURCE_ID] [-j JOBS]
[-N DASK_CLUSTER_NAME]
[-c DASK_CLUSTER_CONFIG_FILE]
[--tasks-log-dir LOG_DIR]
[-t TASK_IDS [TASK_IDS ...]] [--keep-going]
[--force] [-d TASK_STATUS_DIR] [--verbose]
[input] [pipeline] [{run,list,status}]
Annotate Schema2 Parquet
positional arguments:
input the directory containing Parquet files (default: -)
pipeline The pipeline definition file. By default, or if the
value is gpf_instance, the annotation pipeline from
the configured gpf instance will be used. (default:
context)
options:
-h, --help show this help message and exit
-r REGION_SIZE, --region-size REGION_SIZE
region size to parallelize by (default: 300000000)
-w WORK_DIR, --work-dir WORK_DIR
Directory to store intermediate output files in
(default: annotate_schema2_output)
-o OUTPUT, --output OUTPUT
Path of the directory to hold the output (default:
None)
-i FULL_REANNOTATION, --full-reannotation FULL_REANNOTATION
Ignore any previous annotation and run a full
reannotation. (default: None)
-ar, --allow-repeated-attributes
Rename repeated attributes instead of raising an
error. (default: False)
-g GRR_FILENAME, --grr-filename GRR_FILENAME, --grr GRR_FILENAME
The GRR configuration file. If the argument is absent,
the a GRR repository from the current genomic context
(i.e. gpf_instance) will be used or, if that fails,
the default GRR configuration will be used. (default:
None)
--grr-directory GRR_DIRECTORY
Local GRR directory to use as repository. (default:
None)
-R REFERENCE_GENOME_RESOURCE_ID, --reference-genome-resource-id REFERENCE_GENOME_RESOURCE_ID, --ref REFERENCE_GENOME_RESOURCE_ID
The resource id for the reference genome. If the
argument is absent the reference genome from the
current genomic context will be used. (default: None)
-G GENE_MODELS_RESOURCE_ID, --gene-models-resource-id GENE_MODELS_RESOURCE_ID, --genes GENE_MODELS_RESOURCE_ID
The resource is of the gene models resource. If the
argument is absent the gene models from the current
genomic context will be used. (default: None)
--verbose, -v, -V
Task Graph Executor:
-j JOBS, --jobs JOBS Number of jobs to run in parallel. Defaults to the
number of processors on the machine (default: None)
-N DASK_CLUSTER_NAME, --dask-cluster-name DASK_CLUSTER_NAME, --dcn DASK_CLUSTER_NAME
The named of the named dask cluster (default: None)
-c DASK_CLUSTER_CONFIG_FILE, --dccf DASK_CLUSTER_CONFIG_FILE, --dask-cluster-config-file DASK_CLUSTER_CONFIG_FILE
dask cluster config file (default: None)
--tasks-log-dir LOG_DIR
Path to directory where to store tasks' logs (default:
./.task-log)
Execution Mode:
{run,list,status} Command to execute on the import configuration. run -
runs the import process list - lists the tasks to be
executed but doesn't run them status - synonym for
list (default: run)
-t TASK_IDS [TASK_IDS ...], --task-ids TASK_IDS [TASK_IDS ...]
--keep-going Whether or not to keep executing in case of an error
(default: False)
--force, -f Ignore precomputed state and always rerun all tasks.
(default: False)
-d TASK_STATUS_DIR, --task-status-dir TASK_STATUS_DIR, --tsd TASK_STATUS_DIR
Directory to store the task progress. (default: .)
annotate_doc
Produce an HTML file describing the provided pipeline.
Example usage of annotate_doc
$ annotate_doc.py annotation.yaml
Options for annotate_doc
$ annotate_doc --help
usage: annotate_doc [-h] [-o OUTPUT] [-ar] [-g GRR_FILENAME]
[--grr-directory GRR_DIRECTORY]
[-R REFERENCE_GENOME_RESOURCE_ID]
[-G GENE_MODELS_RESOURCE_ID] [--verbose]
[pipeline]
Annotate columns
positional arguments:
pipeline The pipeline definition file. By default, or if the
value is gpf_instance, the annotation pipeline from
the configured gpf instance will be used. (default:
context)
options:
-h, --help show this help message and exit
-o OUTPUT, --output OUTPUT
Filename of the output VCF result (default: None)
-ar, --allow-repeated-attributes
Rename repeated attributes instead of raising an
error. (default: False)
-g GRR_FILENAME, --grr-filename GRR_FILENAME, --grr GRR_FILENAME
The GRR configuration file. If the argument is absent,
the a GRR repository from the current genomic context
(i.e. gpf_instance) will be used or, if that fails,
the default GRR configuration will be used. (default:
None)
--grr-directory GRR_DIRECTORY
Local GRR directory to use as repository. (default:
None)
-R REFERENCE_GENOME_RESOURCE_ID, --reference-genome-resource-id REFERENCE_GENOME_RESOURCE_ID, --ref REFERENCE_GENOME_RESOURCE_ID
The resource id for the reference genome. If the
argument is absent the reference genome from the
current genomic context will be used. (default: None)
-G GENE_MODELS_RESOURCE_ID, --gene-models-resource-id GENE_MODELS_RESOURCE_ID, --genes GENE_MODELS_RESOURCE_ID
The resource is of the gene models resource. If the
argument is absent the gene models from the current
genomic context will be used. (default: None)
--verbose, -v, -V
Example annotations
How to annotate variants with ClinVar
For this example, we’ll assume that you have a GRR repository with the ClinVar score resource.
We’ll use a small list of de Novo variants saved as denovo-variants.tsv:
CHROM POS REF ALT
chr14 21403214 T C
chr14 21431459 G C
chr14 21391016 A AT
chr14 21403019 G A
chr14 21402010 G A
chr14 21393484 TCTTC T
Note
The example variants can be downloaded from here:
denovo-variants.tsv.
Let us create an annotation configuration stored as clinvar-annotation.yaml:
- allele_score: hg38/scores/clinvar_20221105
Run the annotate_columns tool:
annotate_columns --grr ./grr_definition.yaml \
--col_pos POS --col_chrom CHROM --col_ref REF --col_alt ALT \
denovo-variants.tsv clinvar_annotation.yaml
How to annotate with gene score annotators
For this example we will reuse the denovo_variants.tsv from the previous example.
Create an annotation.yaml config with the following contents:
- effect_annotator: gene_models: hg38/gene_models/refSeq_v20200330 genome: hg38/genomes/GRCh38-hg38 - gene_score_annotator: resource_id: gene_properties/gene_scores/SFARI_gene_score input_gene_list: gene_list attributes: - source: "SFARI gene score" name: SFARI_gene_score
As mentioned before, when setting up gene score annotators, we need to have a gene list in the annotation context. The gene list is provided by the effect annotator preceding the gene score annotator.
Next, run the annotate_columns tool:
annotate_columns --grr ./grr_definition.yaml \
--col_pos POS --col_chrom CHROM --col_ref REF --col_alt ALT \
denovo-variants.tsv annotation.yaml
Gene score annotator with changed aggregator
- effect_annotator:
gene_models: hg38/gene_models/refSeq_v20200330
genome: hg38/genomes/GRCh38-hg38
- gene_score_annotator:
resource_id: gene_properties/gene_scores/pLI
input_gene_list: gene_list
attributes:
- source: pLI
gene_aggregator: max
Simple position score annotation
- position_score: hg38/scores/phyloP7way
Effect annotator with default resources
- position_score: hg38/scores/phyloP7way
- effect_annotator
This effect annotator will use the reference genome and gene models from the active GPF instance or from CLI parameters such as:
--ref hg38/genomes/GRCh38-hg38 --genes hg38/gene_models/refSeq_v20200330
Liftover annotation
- position_score: hg38/scores/phyloP100way
- liftover_annotator:
chain: liftover/hg38ToHg19
source_genome: hg38/genomes/GRCh38-hg38
target_genome: hg19/genomes/GATK_ResourceBundle_5777_b37_phiX174
attributes:
- source: liftover_annotatable
name: hg19_annotatable
internal: true
- position_score:
resource_id: hg19/scores/FitCons-i6-merged
input_annotatable: hg19_annotatable