Custom Annotations
Overview
While the team tries to keep data sources up-to-date, you might want to start incorporate new annotations ahead of our update cycle. Another common use case involves protected health information (PHI). Custom annotations are a mechanism that enables both use cases.
Here are some examples of how our collaborators use custom annotations:
- associating context from both a sample-level and a sample cohort level with the variant annotations
- adding content that is licensed (e.g. HGMD) to the variant annotations
At the moment, we have two different custom annotation file formats. One provides additional annotations to variants (both small variants and SVs) while the other caters to gene annotations.
In both cases, the custom annotation file format is a tab-delimited file that is separated into two parts: the header & the data.
The header is where you can customize how you want the data to appear in the JSON file and provide context about the genome assembly and how Illumina Connected Annotations should match the variants.
At Illumina, there are usually many components downstream of Illumina Connected Annotations that have to parse our annotations. If a customer provides a custom annotation, those downstream tools need to understand more about the data such as:
- data type (e.g. number, boolean, or a string)
- data category (e.g. is this an allele count, allele number, allele frequency, etc.)
- associated population (i.e. if this is an allele frequency)
For each custom annotation, Illumina Connected Annotations uses this context to create a JSON schema that can be sent to downstream tools. If a tool knows that this is an allele frequency, it can validate user input to ensure that it's in the range of [0, 1].
Variant File Format
File Format
Illumina Connected Annotations expects plain text (or gzipped text) files. Using tools like Excel can add extra characters that can break parsing. We highly recommend creating and modifying these files with plain text editor like Notepad, Notepad++ or Atom.
Basic Allele Frequency Example
Create the Custom Annotation TSV
Imagine that you want to create a basic allele frequency custom annotation for small variants. If we visualized the tab-delimited file (TSV), it would look something like this:
Col 1 | Col 2 | Col 3 | Col 4 | Col 5 |
---|---|---|---|---|
#title=MyDataSource | ||||
#assembly=GRCh38 | ||||
#matchVariantsBy=allele | ||||
#CHROM | POS | REF | ALT | allAf |
#categories | . | . | . | AlleleFrequency |
#descriptions | . | . | . | ALL |
#type | . | . | . | number |
chr16 | 23603511 | TGA | T | 0.000006579 |
chr16 | 68801894 | G | A | 0.000006569 |
chr19 | 11107436 | G | A | 0.00003291 |
Here's the full TSV file.
Let's go over the header and discuss the contents:
title
indicates the name of the JSON keyassembly
indicates that this data is only valid forGRCh38
.matchVariantsBy
indicates how annotations should be matched and reported. In this case annotations will be matched and reported by allele.categories
provides hints to downstream tools on how they might want to treat the data. In this case, we indicate that it's an allele frequency.descriptions
are used in special circumstances to provide more context. Even though column 5 is calledallAf
, it might not be clear to a downstream tool that this means a global allele frequency using all sub-populations. In this case,ALL
indicates the intended population.type
indicates to downstream tools the data type. Since allele frequencies are numbers, we'll writenumber
in this column.
Reference Base Checking
Illumina Connected Annotations validates all the reference bases in a custom annotation. If a variant or genomic region is specified that has the wrong reference base, an error will be produced.
Sorting
The variants within each chromosome must be sorted by genomic position.
Convert to Illumina Connected Annotations Format
First we need to convert the TSV file to Illumina Connected Annotations's native file format and let's put that file in a new directory called CA:
$ mkdir CA
$ dotnet bin/Release/netcoreapp2.1/SAUtils.dll customvar \
-r Data/References/Homo_sapiens.GRCh38.Nirvana.dat -i MyDataSource.tsv -o CA
---------------------------------------------------------------------------
SAUtils (c) 2020 Illumina, Inc.
Stromberg, Roy, Lajugie, Jiang, Li, and Kang 3.12.0
---------------------------------------------------------------------------
Chromosome 16 completed in 00:00:00.1
Chromosome 19 completed in 00:00:00.0
Time: 00:00:00.2
Annotate with Illumina Connected Annotations
Let's annotate the following VCF (notice that it's one of the variants that we have in our custom annotation):
##fileformat=VCFv4.1
#CHROM POS ID REF ALT QUAL FILTER INFO
16 68801894 . G A . . .
Here's the full VCF file.
Since Illumina Connected Annotations can handle multiple directories with external annotations, all we need to do is specify our new CA directory in addition to the normal Illumina Connected Annotations command-line.
$ dotnet Annotator.dll -c Data/Cache/GRCh38/Both \
-r Data/References/Homo_sapiens.GRCh38.Nirvana.dat \
--sd Data/SupplementaryAnnotation/GRCh38 --sd CA -i TestCA.vcf -o TestCA
---------------------------------------------------------------------------
IlluminaConnectedAnnotations (c) 2020 Illumina, Inc.
Stromberg, Roy, Lajugie, Jiang, Li, and Kang 3.12.0
---------------------------------------------------------------------------
Initialization Time Positions/s
---------------------------------------------------------------------------
Cache 00:00:01.8
SA Position Scan 00:00:00.0 19
Reference Preload Annotation Variants/s
---------------------------------------------------------------------------
chr16 00:00:00.2 00:00:01.3 1
Summary Time Percent
---------------------------------------------------------------------------
Initialization 00:00:01.9 25.5 %
Preload 00:00:00.2 3.3 %
Annotation 00:00:01.3 18.2 %
Time: 00:00:06.3
Investigate the Results
We would expect the following data to show up in our JSON output file:
"variants": [
{
"vid": "16-68801894-G-A",
"chromosome": "16",
"begin": 68801894,
"end": 68801894,
"refAllele": "G",
"altAllele": "A",
"variantType": "SNV",
"hgvsg": "NC_000016.10:g.68801894G>A",
"phylopScore": 1,
"MyDataSource": {
"refAllele": "G",
"altAllele": "A",
"allAf": 7e-06
},
"clinvar": [
Here's the full JSON file.
Illumina Connected Annotations preserves up to 6 decimal places for allele frequency data.
Categories & Descriptions Example
Create the Custom Annotation TSV
Building on the previous example, we can add other types of annotations like predictions and general notes.
Col 1 | Col 2 | Col 3 | Col 4 | Col 5 | Col 6 | Col 7 |
---|---|---|---|---|---|---|
#title=MyDataSource | ||||||
#assembly=GRCh38 | ||||||
#matchVariantsBy=allele | ||||||
#CHROM | POS | REF | ALT | allAf | pathogenicity | notes |
#categories | . | . | . | AlleleFrequency | Prediction | . |
#descriptions | . | . | . | ALL | . | . |
#type | . | . | . | number | string | string |
chr16 | 23603511 | TGA | T | 0.000006579 | P | . |
chr16 | 68801894 | G | A | 0.000006569 | LP | Seen in case 123 |
chr19 | 11107436 | G | A | 0.00003291 | . | . |
Here's the full TSV file.
Placeholders
You can use a period to denote an empty value (much in the same way as periods are used in VCF files to signify missing values). While Illumina Connected Annotations also accepts empty columns in the TSV file, we use them in these examples to promote readability.
Let's go over what's new in this example:
- Column 6 adds a field called
pathogenicity
which uses thePrediction
category. When using this category, Illumina Connected Annotations will validate to make sure that the field contains either the abbreviations (B, LB, VUS, LP, and P) or the long-form equivalents (e.g. benign or pathogenic). - Column 7 adds a field called
notes
and it doesn't have a category or description. We're just going to use it to add some internal notes.
Annotate with Illumina Connected Annotations
Let's use a new VCF file. It includes all the same positions as our custom annotation file, but only the middle variant also matches the alternate allele (allele-specific match):
##fileformat=VCFv4.1
#CHROM POS ID REF ALT QUAL FILTER INFO
16 23603511 . TG T . . .
16 68801894 . G A . . .
19 11107436 . G C . . .
Here's the full VCF file.
Investigate the Results
Because we specified #matchVariantsBy=allele
in our custom annotation file, only the middle variant will get an annotation:
"variants": [
{
"vid": "16-68801894-G-A",
"chromosome": "16",
"begin": 68801894,
"end": 68801894,
"refAllele": "G",
"altAllele": "A",
"variantType": "SNV",
"hgvsg": "NC_000016.10:g.68801894G>A",
"phylopScore": 1,
"MyDataSource": {
"refAllele": "G",
"altAllele": "A",
"allAf": 7e-06,
"pathogenicity": "LP",
"notes": "Seen in case 123"
},
"clinvar": [
Here's the full JSON file.
Using Positional Matches
What would happen if we changed to #matchVariantsBy=position
? Two things will happen. First, our positional variants will now match:
"variants": [
{
"vid": "16-23603511-TG-T",
"chromosome": "16",
"begin": 23603512,
"end": 23603512,
"refAllele": "G",
"altAllele": "-",
"variantType": "deletion",
"hgvsg": "NC_000016.10:g.23603512delG",
"MyDataSource": [
{
"refAllele": "GA",
"altAllele": "-",
"allAf": 7e-06,
"pathogenicity": "P"
}
],
"clinvar": [
In addition, you will now see an extra flag for our allele-specific variant:
"variants": [
{
"vid": "16-68801894-G-A",
"chromosome": "16",
"begin": 68801894,
"end": 68801894,
"refAllele": "G",
"altAllele": "A",
"variantType": "SNV",
"hgvsg": "NC_000016.10:g.68801894G>A",
"phylopScore": 1,
"MyDataSource": [
{
"refAllele": "G",
"altAllele": "A",
"allAf": 7e-06,
"pathogenicity": "LP",
"notes": "Seen in case 123",
"isAlleleSpecific": true
}
],
"clinvar": [
Genomic Region Example
Create the Custom Annotation TSV
In the previous example, we added a note for the middle variant, but sometimes it's handy to annotate a genomic region. Consider the following example:
Col 1 | Col 2 | Col 3 | Col 4 | Col 5 |
---|---|---|---|---|
#title=MyDataSource | ||||
#assembly=GRCh38 | ||||
#matchVariantsBy=allele | ||||
#CHROM | POS | REF | END | notes |
#categories | . | . | . | . |
#descriptions | . | . | . | . |
#type | . | . | . | string |
chr16 | 20000000 | T | 70000000 | Lots of false positives in this region |
Here's the full TSV file.
Let's go over what's new in this example:
- Column 5 now has a field called
notes
. In essence, it looks exactly like column 7 from our previous example. - The main difference is that now one of our custom annotation entries is actually a genomic region. Any variant that overlaps with that region will get a custom annotation.
In the previous example we learned about positional matching vs allele-specific matching. For genomic regions, #matchVariantsBy=allele
and #matchVariantsBy=position
produce
the same result.
Annotate with Illumina Connected Annotations
Let's use the same VCF file as our previous example.
Investigate the Results
{
"chromosome": "16",
"position": 23603511,
"refAllele": "TG",
"altAlleles": [
"T"
],
"cytogeneticBand": "16p12.2",
"MyDataSource": [
{
"start": 20000000,
"end": 70000000,
"notes": "Lots of false positives in this region",
"reciprocalOverlap": 0,
"annotationOverlap": 0
}
],
"variants": [
Here's the full JSON file.
Reciprocal & Annotation Overlap
For all intervals, Illumina Connected Annotations internally calculates two overlaps: a variant overlap and an annotation overlap. Variant overlap is the percentage of the variant's length that is overlapped. Annotation overlap is the percentage of the annotation's length that is overlap.
Reciprocal overlap is the minimum of those two overlaps. Given that the annotation is 50 Mbp and the deletion is one 1 bp, both overlaps will be pretty close to 0.
We will also see this annotation for the other variant on chr16:
{
"chromosome": "16",
"position": 68801894,
"refAllele": "G",
"altAlleles": [
"A"
],
"cytogeneticBand": "16q22.1",
"MyDataSource": [
{
"start": 20000000,
"end": 70000000,
"notes": "Lots of false positives in this region",
"reciprocalOverlap": 0,
"annotationOverlap": 0
}
],
"variants": [
Genomic Regions for Structural Variants Example
Create the Custom Annotation TSV
Often we use genomic regions to represent other known CNVs and SVs in the genome. In this use case, we usually don't want to match these regions to other small variants. To force Illumina Connected Annotations to match regions only to other SVs, use the #matchVariantsBy=sv
option in the header. Here is an example:
Col 1 | Col 2 | Col 3 | Col 4 | Col 5 |
---|---|---|---|---|
#title=MyDataSource | ||||
#assembly=GRCh38 | ||||
#matchVariantsBy=sv | ||||
#CHROM | POS | REF | END | notes |
#categories | . | . | . | . |
#descriptions | . | . | . | . |
#type | . | . | . | string |
chr16 | 20000000 | T | 70000000 | Lots of false positives in this region |
Here's the full TSV file.
Let's go over what's new in this example:
- The main difference is the header field
#matchVariantsBy=sv
which indicates that only structural variants that overlap these genomic regions will receive annotations.
Annotate with Illumina Connected Annotations
Let's use a new VCF file. It contains the first variant from the previous file and a structural variant deletion- both of which overlap the given genomic region.
##fileformat=VCFv4.1
#CHROM POS ID REF ALT QUAL FILTER INFO
16 23603511 . TG T . . .
16 68801894 . G <DEL> . . END=73683789;SVTYPE=DEL
Here's the full VCF file.
Investigate the Results
Note that this time, MyDataSource
only showed up for the <DEL>
and not the deletion 16-23603511-TG-T
.
{
"chromosome": "16",
"position": 23603511,
"refAllele": "TG",
"altAlleles": [
"T"
],
"cytogeneticBand": "16p12.2",
"variants": [
...
...
{
"chromosome": "16",
"position": 68801894,
"svEnd": 73683789,
"refAllele": "G",
"altAlleles": [
"<DEL>"
],
"cytogeneticBand": "16q22.1-q22.3",
"MyDataSource": [
{
"start": 20000000,
"end": 70000000,
"notes": "Lots of false positives in this region",
"reciprocalOverlap": 0.02396,
"annotationOverlap": 0.02396
}
],
"variants": [
Mixing Small Variants and Genomic Regions
Create the Custom Annotation TSV
Previously we looked at examples that either had small variants or genomic regions. Let's create a file that contains both:
Col 1 | Col 2 | Col 3 | Col 4 | Col 5 | Col 6 |
---|---|---|---|---|---|
#title=MyDataSource | |||||
#assembly=GRCh38 | |||||
#matchVariantsBy=allele | |||||
#CHROM | POS | REF | ALT | END | notes |
#categories | . | . | . | . | . |
#descriptions | . | . | . | . | . |
#type | . | . | . | . | string |
chr16 | 23603511 | TGA | T | . | . |
chr16 | 68801894 | G | A | . | . |
chr19 | 11107436 | G | A | . | . |
chr21 | 10510818 | C | . | 10699435 | Interval #1 |
chr21 | 10510818 | C | <DEL> | 10699435 | Interval #2 |
chr22 | 12370388 | T | T[chr22:12370729[ | . | Known false-positive |
Here's the full TSV file.
Let's go over what's new in this example:
- Column 4 now has the
REF
field. Exception for the case listed below, this is only used by small variants or translocation breakends. - Column 5 now has the
END
field. This is only used by genomic regions. - There are two custom annotations on chr21 and the start and end coordinates look the same, so what's different? Interval #2 has a symbolic allele in the ALT column. When this is used in custom annotation, the start position is treated as the padding base (using VCF conventions). When Illumina Connected Annotations matches a variant to interval #2, it will ignore the padding base and consider the start position to be at position 10510819.
Annotate with Illumina Connected Annotations
Let's use a new VCF file to study how matching works for intervals #1 and #2:
##fileformat=VCFv4.1
#CHROM POS ID REF ALT QUAL FILTER INFO
21 10510818 . C <DUP> . . END=10699435;SVTYPE=DUP
22 12370388 . T T[chr22:12370729[ . . SVTYPE=BND
Here's the full VCF file.
The first variant is similar to the custom annotation labelled "interval #2". Position 10510818 is the padding base, so it effectively starts at position 10510819.
Investigate the Results
"positions": [
{
"chromosome": "21",
"position": 10510818,
"svEnd": 10699435,
"refAllele": "C",
"altAlleles": [
"<DUP>"
],
"cytogeneticBand": "21p11.2",
"MyDataSource": [
{
"start": 10510818,
"end": 10699435,
"notes": "Interval #1",
"reciprocalOverlap": 0.99999,
"annotationOverlap": 0.99999
},
{
"start": 10510819,
"end": 10699435,
"notes": "Interval #2",
"reciprocalOverlap": 1,
"annotationOverlap": 1
}
],
Here's the full JSON file.
As expected, the variant and interval #2 have matching endpoints, therefore there is 100% overlap. Interval #1 technically starts 1 bp earlier, so its overlap 99.9%.
Further down the JSON file, we find the annotated translocation breakend:
"variants": [
{
"vid": "22-12370388-T-T[chr22:12370729[",
"chromosome": "22",
"begin": 12370388,
"end": 12370388,
"isStructuralVariant": true,
"refAllele": "T",
"altAllele": "T[chr22:12370729[",
"variantType": "translocation_breakend",
"MyDataSource": {
"refAllele": "T",
"altAllele": "T[chr22:12370729[",
"notes": "Known false-positive"
}
}
Gene File Format
Basic Gene Example
Create the Custom Annotation TSV
Previously we looked at examples that either had small variants or genomic regions, however, sometimes we would like to add custom gene annotations. The gene custom annotation file format looks slightly different:
Col 1 | Col 2 | Col 3 | Col 4 |
---|---|---|---|
#title=MyDataSource | |||
#geneSymbol | geneId | phenotype | notes |
#categories | . | . | . |
#descriptions | . | . | . |
#type | . | string | string |
TP53 | 7157 | Colorectal cancer, hereditary nonpolyposis, type 5 | . |
KRAS | ENSG00000133703 | Mismatch repair cancer syndrome | Seen in cohort 123 |
Here's the full TSV file.
Let's go over what's in this example:
- Column 2 has the
geneId
field. This can be either an Entrez Gene ID or an Ensembl ID.
Gene Symbols
Gene symbols are always in flux and are being updated on a daily basis at the NCBI and at HGNC. Due to this, Illumina Connected Annotations uses the geneId
to match genes rather than the gene symbol. However, to
make the custom annotation files easier to read, we've included the geneSymbol
column as well.
Unknown Gene IDs
When Illumina Connected Annotations parses the gene custom annotation file, it will note any gene IDs that are currently not recognized in Illumina Connected Annotations. In such a case, Illumina Connected Annotations will display an error showing all the unrecognized gene IDs.
Annotate with Illumina Connected Annotations
Let's use a VCF file that contain variants in TP53 and KRAS:
##fileformat=VCFv4.1
#CHROM POS ID REF ALT QUAL FILTER INFO
12 25227255 . A T . . .
17 7675074 . C A . . .
Here's the full VCF file.
Investigate the Results
"genes": [
{
"name": "KRAS",
"clingenGeneValidity": [
{
"diseaseId": "MONDO_0009026",
"disease": "Costello syndrome",
"classification": "disputed",
"classificationDate": "2018-07-24"
}
],
"clingenDosageSensitivityMap": {
"haploinsufficiency": "no evidence to suggest that dosage sensitivity is associated with clinical phenotype",
"triplosensitivity": "no evidence to suggest that dosage sensitivity is associated with clinical phenotype"
},
"gnomAD": {
"pLi": 0.000788,
"pRec": 0.789,
"pNull": 0.21,
"synZ": 0.336,
"misZ": 2.32,
"loeuf": 1.24
},
"MyDataSource": {
"phenotype": "Mismatch repair cancer syndrome",
"notes": "Seen in cohort 123"
}
},
This is the abbreviated output for KRAS. Here's the full JSON file if you want to see the complete KRAS entry.
Customizing the Header
Title
For the title, you can provide any string that hasn't already been used. The title should be unique.
caution
Make sure that the title does not conflict with other keys in the JSON file.
For small variants, you can't provide a title that conflicts with other keys in the variant object. Some examples of this would be
vid
, chromosome
, transcripts
, etc.. The title should also not conflict with other data source keys like clinvar
or gnomad
.
For structural variants, you can't provide a title that conflicts with other keys in the position object. Some examples of this would be
chromosome
, svLength
, cytogeneticBand
, etc. The title should also not conflict with other data source keys like clingen
or dgv
.
caution
Care should be taken not to annotate using multiple custom annotations that all use the same title.
Genome Assemblies
The following genome assemblies can be specified:
- GRCh37
- GRCh38
Matching Criteria
The matching criteria instructs how Illumina Connected Annotations should match a VCF variant to the custom annotation.
The following matching criteria can be specified:
allele
- use this when you only want allele-specific matches. This is commonly the case when using allele frequency data sources likegnomAD
position
- use this when you want positional matches. This is commonly used with disease phenotype data sources likeClinVar
sv
- use this when you want to match to all other overlapping SVs. This use case arose when we were adding custom annotations for baseline copy number intervals along the genome.
Categories
Categories are not used by Illumina Connected Annotations, but are often used by downstream tools. Categories provide hints for how those tools should filter or display the annotation data.
When a category is specified, Illumina Connected Annotations will provide additional validation for those fields. The following table describes each category:
Category | Description | Validation |
---|---|---|
AlleleCount | allele counts for a specific population | See the supported populations below |
AlleleNumber | allele numbers for a specific population | See the supported populations below |
AlleleFrequency | allele frequencies for a specific population | See the supported populations below |
Prediction | ACMG-style pathogenicity classifications | • benign (B)• likely benign (LB)• VUS • likely pathogenic (LP)• pathogenic (P) |
Filter | free text that signals downstream tools to add the column to the filter | Max 20 characters |
Description | free-text description | Max 100 characters |
Identifier | any ID | Max 50 characters |
HomozygousCount | count of homozygous individuals for a specific population | See the supported populations below |
Score | any score value | Any double-precision floating point number |
Descriptions
Descriptions are used to add more context to the categories. For now, descriptions are mainly used to associate allele counts, numbers, and frequencies with their respective populations.
Populations
The following populations were specified in the HapMap project, 1000 Genomes Project, ExAC, and gnomAD.
Population Code | Super-population Code | Description |
---|---|---|
ACB | AFR | African Caribbeans in Barbados |
AFR | AFR | African |
ALL | ALL | All populations |
AMR | AMR | Ad Mixed American |
ASJ | Ashkenazi Jewish | |
ASW | AFR | Americans of African Ancestry in SW USA |
BEB | SAS | Bengali from Bangladesh |
CDX | EAS | Chinese Dai in Xishuangbanna, China |
CEU | EUR | Utah Residents (CEPH) with Northern and Western European Ancestry |
CHB | EAS | Han Chinese in Beijing, China |
CHS | EAS | Southern Han Chinese |
CLM | AMR | Colombians from Medellin, Colombia |
EAS | EAS | East Asian |
ESN | AFR | Esan in Nigeria |
EUR | EUR | European |
FIN | EUR | Finnish in Finland |
GBR | EUR | British in England and Scotland |
GIH | SAS | Gujarati Indian from Houston, Texas |
GWD | AFR | Gambian in Western Divisions in the Gambia |
IBS | EUR | Iberian population in Spain |
ITU | SAS | Indian Telugu from the UK |
JPT | EAS | Japanese in Tokyo, Japan |
KHV | EAS | Kinh in Ho Chi Minh City, Vietnam |
LWK | AFR | Luhya in Webuye, Kenya |
MAG | AFR | Mandinka in the Gambia |
MKK | AFR | Maasai in Kinyawa, Kenya |
MSL | AFR | Mende in Sierra Leone |
MXL | AMR | Mexican Ancestry from Los Angeles, USA |
NFE | EUR | European (Non-Finnish) |
OTH | OTH | Other |
PEL | AMR | Peruvians from Lima, Peru |
PJL | SAS | Punjabi from Lahore, Pakistan |
PUR | AMR | Puerto Ricans from Puerto Rico |
SAS | SAS | South Asian |
STU | SAS | Sri Lankan Tamil from the UK |
TSI | EUR | Toscani in Italia |
YRI | AFR | Yoruba in Ibadan, Nigeria |
Data Types
Each custom annotation can be one of the following data types:
bool
- true or falsenumber
- any integer or floating-point numberstring
- text
tip
For boolean variables, only keys with a true
value will be output to the JSON object.
Using SAUtils
Illumina Connected Annotations includes a tool called SAUtils
that converts various data sources into Illumina Connected Annotations's native binary format. The sub-commands customvar
and customgene
are used to specify a variant file or a gene file respectively.
Convert Variant File
dotnet bin/Release/netcoreapp2.1/SAUtils.dll customvar \
-r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
-i MyDataSource.tsv \
-o SupplementaryAnnotation
- the
-r
argument specifies the compressed reference path - the
-i
argument specifies the input TSV path - the
-o
argument specifies the output directory
Convert Gene File
dotnet bin/Release/netcoreapp2.1/SAUtils.dll customgene \
-r Data/References/Homo_sapiens.GRCh37.Nirvana.dat \
-c Data/Cache \
-i MyDataSource.tsv \
-o SupplementaryAnnotation
- the
-c
argument specifies the Illumina Connected Annotations cache path - the
-i
argument specifies the input TSV path - the
-o
argument specifies the output directory