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>
nucleotide_aggregator: <aggregator to use for NPs>
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>
position_aggregator: <aggregator to use for INDELs>
allele_aggregator: <aggregator to use for alleles>
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.
- gene_list
List of all genes
- 3’UTR_gene_list
List of all 3’UTR genes
- 3’UTR-intron_gene_list
List of all 3’UTR-intron genes
- 5’UTR_gene_list
List of all 5’UTR genes
- 5’UTR-intron_gene_list
List of all 5’UTR-intron genes
- frame-shift_gene_list
List of all frame-shift genes
- intergenic_gene_list
List of all intergenic genes
- intron_gene_list
List of all intron genes
- missense_gene_list
List of all missense genes
- no-frame-shift_gene_list
List of all no-frame-shift genes
- no-frame-shift-newStop_gene_list
List of all no-frame-shift-newStop genes
- noEnd_gene_list
List of all noEnd genes
- noStart_gene_list
List of all noStart genes
- non-coding_gene_list
List of all non-coding genes
- non-coding-intron_gene_list
List of all non-coding-intron genes
- nonsense_gene_list
List of all nonsense genes
- splice-site_gene_list
List of all splice-site genes
- synonymous_gene_list
List of all synonymous genes
- CDS_gene_list
List of all CDS genes
- CNV+_gene_list
List of all CNV+ genes
- CNV-_gene_list
List of all CNV- genes
- coding_gene_list
List of all coding genes
- noncoding_gene_list
List of all noncoding genes
- CNV_gene_list
List of all CNV genes
- LGDs_gene_list
List of all LGD genes
- LGD_gene_list (deprecated)
List of all LGD genes
- nonsynonymous_gene_list
List of all nonsynonymous genes
- UTRs_gene_list
List of all UTRs genes
- effect_annotator:
genome: <reference genome resource ID>
gene_models: <gene models resource ID>
Specifying the genome parameter in the configuration is optional - the effect annotator will attempt to get a genome in the following order:
genomeparameter in annotator configurationreference_genomelabel in providedgene_modelsresource’s configurationinput_reference_genomefield from annotation pipeline config preamble sectionGenomic context
Simple effect annotator
Classify an event according to the scheme described in https://pmc.ncbi.nlm.nih.gov/articles/PMC8410909/figure/Fig2/. The attributes the annotator will output are the following:
- effect
The worst effect across all transcripts.
- genes
The affected genes.
- gene_list
List of all genes.
- simple_effect_annotator:
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
Specifying the source_genome and target_genome parameters in the configuration is optional - if none are provided, the annotator will attempt to collect them from the provided chain resource’s configuration. Specifically, the chain resource can have source_genome and target_genome labels in its configuration’s meta section.
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
attributes:
- source: normalized_allele
name: hg38_normalized_annotatable
internal: true
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 gene sets in a gene set collection.
- gene_set_annotator:
resource_id: <gene set collection resource ID>
input_gene_list: <gene list to use to match variants>
attributes:
- <gene set name>
- in_sets
The gene set annotator can output an attribute for every single gene set in a collection along with a special attribute in_sets, which is a list of the names of every gene set that the variant belongs to from the collection.
VEP annotators
There are two annotators which depend on and use the Ensembl Variant Effect Predictor (VEP).
Setting up
Using the Ensembl VEP annotators requires the gpf_vep_annotator conda package to be installed.
mamba install \
-c conda-forge \
-c bioconda \
-c iossifovlab \
-c defaults \
gpf_vep_annotator
The VEP annotators can be run only in batch mode.
VEP Full Annotator
Using the full VEP annotator requires a VEP cache to be accessible in the local file system.
Downloading the VEP cache is done with the vep_install tool provided by the ensembl-vep conda package, which comes with gpf_vep_annotator.
Download the cache for hg38.
vep_install -a cf -s homo_sapiens -y GRCh38 -c /output/path/to/cache --convert
The annotator configuration looks like this:
- vep_full_annotator:
cache_dir: <VEP cache directory>
Full Annotator Output Attributes
The full VEP annotator can output the following attributes:
- Location
Location of variant in standard coordinate format (chr:start or chr:start-end)
- Allele
The variant allele used to calculate the consequence
- Gene
Stable ID of affected gene
- Feature
Stable ID of feature
- Feature_type
Type of feature - Transcript, RegulatoryFeature or MotifFeature
- Consequence
Consequence type
- cDNA_position
Relative position of base pair in cDNA sequence
- CDS_position
Relative position of base pair in coding sequence
- Protein_position
Relative position of amino acid in protein
- Amino_acids
Reference and variant amino acids
- Codons
Reference and variant codon sequence
- Existing_variation
Identifier(s) of co-located known variants
- IMPACT
Subjective impact classification of consequence type
- DISTANCE
Shortest distance from variant to transcript
- STRAND
Strand of the feature (1/-1)
- FLAGS
Transcript quality flags
- VARIANT_CLASS
SO variant class
- SYMBOL
Gene symbol (e.g. HGNC)
- SYMBOL_SOURCE
Source of gene symbol
- HGNC_ID
Stable identifer of HGNC gene symbol
- BIOTYPE
Biotype of transcript or regulatory feature
- CANONICAL
Indicates if transcript is canonical for this gene
- MANE_SELECT
MANE Select (Matched Annotation from NCBI and EMBL-EBI) Transcript
- MANE_PLUS_CLINICAL
MANE Plus Clinical (Matched Annotation from NCBI and EMBL-EBI) Transcript
- TSL
Transcript support level
- APPRIS
Annotates alternatively spliced transcripts as primary or alternate based on a range of computational methods
- CCDS
Indicates if transcript is a CCDS transcript
- ENSP
Protein identifer
- SWISSPROT
UniProtKB/Swiss-Prot accession
- TREMBL
UniProtKB/TrEMBL accession
- UNIPARC
UniParc accession
- UNIPROT_ISOFORM
Direct mappings to UniProtKB isoforms
- GENE_PHENO
Indicates if gene is associated with a phenotype, disease or trait
- SIFT
SIFT prediction and/or score
- PolyPhen
PolyPhen prediction and/or score
- EXON
Exon number(s) / total
- INTRON
Intron number(s) / total
- DOMAINS
The source and identifer of any overlapping protein domains
- miRNA
SO terms of overlapped miRNA secondary structure feature(s)
- HGVSc
HGVS coding sequence name
- HGVSp
HGVS protein sequence name
- HGVS_OFFSET
Indicates by how many bases the HGVS notations for this variant have been shifted
- AF
Frequency of existing variant in 1000 Genomes combined population
- AFR_AF
Frequency of existing variant in 1000 Genomes combined African population
- AMR_AF
Frequency of existing variant in 1000 Genomes combined American population
- EAS_AF
Frequency of existing variant in 1000 Genomes combined East Asian population
- EUR_AF
Frequency of existing variant in 1000 Genomes combined European population
- SAS_AF
Frequency of existing variant in 1000 Genomes combined South Asian population
- gnomADe_AF
Frequency of existing variant in gnomAD exomes combined population
- gnomADe_AFR_AF
Frequency of existing variant in gnomAD exomes African/American population
- gnomADe_AMR_AF
Frequency of existing variant in gnomAD exomes American population
- gnomADe_ASJ_AF
Frequency of existing variant in gnomAD exomes Ashkenazi Jewish population
- gnomADe_EAS_AF
Frequency of existing variant in gnomAD exomes East Asian population
- gnomADe_FIN_AF
Frequency of existing variant in gnomAD exomes Finnish population
- gnomADe_NFE_AF
Frequency of existing variant in gnomAD exomes Non-Finnish European population
- gnomADe_OTH_AF
Frequency of existing variant in gnomAD exomes other combined populations
- gnomADe_SAS_AF
Frequency of existing variant in gnomAD exomes South Asian population
- gnomADg_AF
Frequency of existing variant in gnomAD genomes combined population
- gnomADg_AFR_AF
Frequency of existing variant in gnomAD genomes African/American population
- gnomADg_AMI_AF
Frequency of existing variant in gnomAD genomes Amish population
- gnomADg_AMR_AF
Frequency of existing variant in gnomAD genomes American population
- gnomADg_ASJ_AF
Frequency of existing variant in gnomAD genomes Ashkenazi Jewish population
- gnomADg_EAS_AF
Frequency of existing variant in gnomAD genomes East Asian population
- gnomADg_FIN_AF
Frequency of existing variant in gnomAD genomes Finnish population
- gnomADg_MID_AF
Frequency of existing variant in gnomAD genomes Mid-eastern population
- gnomADg_NFE_AF
Frequency of existing variant in gnomAD genomes Non-Finnish European population
- gnomADg_OTH_AF
Frequency of existing variant in gnomAD genomes other combined populations
- gnomADg_SAS_AF
Frequency of existing variant in gnomAD genomes South Asian population
- MAX_AF
Maximum observed allele frequency in 1000 Genomes, ESP and ExAC/gnomAD
- MAX_AF_POPS
Populations in which maximum allele frequency was observed
- CLIN_SIG
ClinVar clinical significance of the dbSNP variant
- SOMATIC
Somatic status of existing variant
- PHENO
Indicates if existing variant(s) is associated with a phenotype, disease or trait; multiple values correspond to multiple variants
- PUBMED
Pubmed ID(s) of publications that cite existing variant
- MOTIF_NAME
The stable identifier of a transcription factor binding profile (TFBP) aligned at this position
- MOTIF_POS
The relative position of the variation in the aligned TFBP
- HIGH_INF_POS
A flag indicating if the variant falls in a high information position of the TFBP
- MOTIF_SCORE_CHANGE
The difference in motif score of the reference and variant sequences for the TFBP
- TRANSCRIPTION_FACTORS
List of transcription factors which bind to the transcription factor binding profile
- worst_consequence
Worst consequence reported by VEP
- highest_impact
Highest impact reported by VEP
- gene_consequence
List of gene consequence pairs reported by VEP
By default, only the following are produced: SYMBOL, Feature, Feature_type, Consequence, worst_consequence, highest_impact, gene_consequence.
VEP Effect Annotator
The VEP effect annotator uses genome and gene model resources to produce its output with VEP. It needs to have a genomic resource repository with the genome and gene models prepared.
- external_vep_gtf_annotator:
genome: hg38/genomes/GRCh38-hg38
gene_models: hg38/gene_models/MANE/1.3
Effect Annotator Output Attributes
The VEP effect annotator can output the following attributes:
- Location
Location of variant in standard coordinate format (chr:start or chr:start-end)
- Allele
The variant allele used to calculate the consequence
- Gene
Stable ID of affected gene
- Feature
Stable ID of feature
- Feature_type
Type of feature - Transcript, RegulatoryFeature or MotifFeature
- Consequence
Consequence type
- cDNA_position
Relative position of base pair in cDNA sequence
- CDS_position
Relative position of base pair in coding sequence
- Protein_position
Relative position of amino acid in protein
- Amino_acids
Reference and variant amino acids
- Codons
Reference and variant codon sequence
- Existing_variation
Identifier(s) of co-located known variants
- IMPACT
Subjective impact classification of consequence type
- DISTANCE
Shortest distance from variant to transcript
- STRAND
Strand of the feature (1/-1)
- FLAGS
Transcript quality flags
- SYMBOL
Gene symbol (e.g. HGNC)
- SYMBOL_SOURCE
Source of gene symbol
- HGNC_ID
Stable identifer of HGNC gene symbol
- SOURCE
Source of transcript
- worst_consequence
Worst consequence reported by VEP
- highest_impact
Highest impact reported by VEP
- gene_consequence
List of gene consequence pairs reported by VEP
- <gene model filename>
Value from provided gene models
By default, only the following are produced: SYMBOL, Feature, Feature_type, Consequence, worst_consequence, highest_impact, gene_consequence and the value from the provided gene models.
Running the VEP annotation
With a prepared variants file and annotation.yaml file, the pipeline can be ran using annotate_columns with the –batch-mode flag.
Example annotate_columns run:
annotate_columns ./variants.tsv.gz ./annotation.yaml -w work -o ./out.tsv -v -j 4 --batch-mode --col-chrom CHROM --col-pos POS --col-ref REF -r 10000 --col-alt ALT --allow-repeated-attributes
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.The option to allow repeated attributes is provided with the
--allow-repeated-attributes(short form-ar) flag. With this flag, the annotation pipeline will append the annotator ID (typicallyA<index of annotator in the pipeline config>), e.g.A0,A1) to the attribute’s name. For example, a repeating attribute calledmy_scorewill appear in the output asmy_score_A0,my_score_A1, and so on.
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-pos-end COL_POS_END] [--col-ref COL_REF]
[--col-cnv-type COL_CNV_TYPE]
[--col-variant COL_VARIANT]
[--col-location COL_LOCATION]
[--col-pos-beg COL_POS_BEG] [--col-alt COL_ALT]
[--col-pos COL_POS] [--col-vcf-like COL_VCF_LIKE]
[--col-chrom COL_CHROM] [-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-pos-end COL_POS_END
The column name that stores pos_end (default: pos_end)
--col-ref COL_REF The column name that stores ref (default: ref)
--col-cnv-type COL_CNV_TYPE
The column name that stores cnv_type (default:
cnv_type)
--col-variant COL_VARIANT
The column name that stores variant (default: variant)
--col-location COL_LOCATION
The column name that stores location (default:
location)
--col-pos-beg COL_POS_BEG
The column name that stores pos_beg (default: pos_beg)
--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-vcf-like COL_VCF_LIKE
The column name that stores vcf_like (default:
vcf_like)
--col-chrom COL_CHROM
The column name that stores chrom (default: chrom)
--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] [-s REGION_SIZE]
[-w WORK_DIR] [-i] [-o OUTPUT | -e | -m] [-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, --region REGION
annotate only a specific region (default: None)
-s 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)
-i, --full-reannotation
Ignore any previous annotation and run a full
reannotation. (default: False)
-o OUTPUT, --output OUTPUT
Path of the directory to hold the output (default:
None)
-e, --in-place Produce output directly in given input dir. (default:
False)
-m, --meta Print the input Parquet's meta properties. (default:
False)
-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
How to annotate with gene set annotators
- effect_annotator:
gene_models: hg38/gene_models/refSeq_v20200330
genome: hg38/genomes/GRCh38-hg38
- gene_set_annotator:
resource_id: gene_properties/gene_sets/autism
gene_set_id: autism candidates from Iossifov PNAS 2015
input_gene_list: gene_list
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