V2 SampleSheet Maker

Build and Deploy PyPI version

Generate an Illumina SampleSheet CSV from JSON.

Supported Sections

Sections currently supported:

  • Header
  • Reads
  • BCLConvert_Settings
  • BCLConvert_Data
  • Cloud_Settings
  • Cloud_Data
  • (Cloud_)?TSO500S_Settings
  • (Cloud_)?TS0500S_Data
  • (Cloud_)?TSO500L_Settings
  • (Cloud_)?TSO500L_Data

Installation

Installation from pypi

pip install v2-samplesheet-maker

Alternatively one may use the docker container ghcr.io/umccr/v2-samplesheet-maker:latest

Usage

Usage:
v2-samplesheet-maker <input-json> <output-csv>

Use - for input-json parameter to parse json from stdin,
Use - for output-json parameter to parse output samplesheet csv to stdout.

Examples

See examples/ for more info

Input JSON

Click to expand! 
{
  "header": {
    "file_format_version": 2,
    "run_name": "my-illumina-sequencing-run",
    "run_description": "A test run",
    "instrument_platform": "NovaSeq 6000",
    "instrument_type": "NovaSeq"
  },
  "reads": {
    "read_1_cycles": 151,
    "read_2_cycles": 151,
    "index_1_cycles": 10,
    "index_2_cycles": 10
  },
  "bclconvert_settings": {
    "adapter_behavior": "trim",
    "adapter_read_1": null,
    "adapter_read_2": null,
    "adapter_stringency": null,
    "barcode_mismatches_index_1": 1,
    "barcode_mismatches_index_2": 1,
    "minimum_trimmed_read_length": null,
    "minimum_adapter_overlap": 2,
    "mask_short_reads": null,
    "override_cycles": "Y151;Y10;Y8N2;Y151",
    "trim_umi": null,
    "create_fastq_for_index_reads": false,
    "no_lane_splitting": false,
    "fastq_compression_format": "gzip",
    "find_adapters_with_indels": null,
    "independent_index_collision_check": null
  },
  "bclconvert_data": [
    {
      "sample_id": "MyFirstSample",
      "lane": 1,
      "index": "AAAAAAAAAA",
      "index2": "CCCCCCCC",
      "sample_project": "SampleProject",
      "sample_name": null
    },
    {
      "sample_id": "MySecondSample",
      "lane": 1,
      "index": "GGGGGGGGGG",
      "index2": "TTTTTTTT",
      "sample_project": "SampleProject",
      "sample_name": null
    }
  ]
}

Output CSV

Click to expand! 
[Header]
FileFormatVersion,2
RunName,my-illumina-sequencing-run
RunDescription,A test run
InstrumentPlatform,NovaSeq 6000
InstrumentType,NovaSeq

[Reads]
Read1Cycles,151
Read2Cycles,151
Index1Cycles,10
Index2Cycles,10

[BCLConvert_Settings]
AdapterBehavior,trim
BarcodeMismatchesIndex1,1
BarcodeMismatchesIndex2,1
MinimumAdapterOverlap,2
OverrideCycles,Y151;Y10;Y8N2;Y151
CreateFastqForIndexReads,False
NoLaneSplitting,False
FastqCompressionFormat,gzip

[BCLConvert_Data]
Lane,Sample_ID,index,index2,Sample_Project
1,MyFirstSample,AAAAAAAAAA,CCCCCCCC,SampleProject
1,MySecondSample,GGGGGGGGGG,TTTTTTTT,SampleProject

Cloud URN Support

We also support URNs for BSSH auto-launch

The urn attribute can be placed either in the Cloud_Settings section under the key <app_name>_Pipeline or the settings section of the application under the key urn. In both circumstances, the urn will be placed in the cloud settings section of the samplesheet under <app_name_Pipeline>.

When using the Cloud_Settings and Cloud_Data section, one should place the library_prep_kit_name and index_adapter_kit_name for each element in bclconvert_data.

One will also need to place the following key, value pairs under the cloud_settings key:

  • generated_version: "0.0.0",
  • cloud_workflow: "ica_workflow_1"

An example can be seen below:

Input

Click to expand! 
{
  "header": {
    "file_format_version": 2,
    "run_name": "TruSeq-PCRfree-NA12878-10B",
    "instrument_type": "NovaSeqXPlus",
    "index_orientation": "Forward"
  },
  "reads": {
    "read_1_cycles": 151,
    "read_2_cycles": 151,
    "index_1_cycles": 10,
    "index_2_cycles": 10
  },
  "bclconvert_settings": {
    "software_version": "4.1.27",
    "adapter_read_1": "AGATCGGAAGAGCACACGTCTGAACTCCAGTCA",
    "adapter_read_2": "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT",
    "override_cycles": "Y151;I8N2;N2I8;Y151",
    "fastq_compression_format": "gzip",
    "urn": "urn:ilmn:ica:pipeline:bf93b5cf-cb27-4dfa-846e-acd6eb081aca#BclConvert_v4_2_7"
  },
  "bclconvert_data": [
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "CCGCGGTT",
      "index2": "AGCGCTAG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "TTATAACC",
      "index2": "GATATCGA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "GGACTTGG",
      "index2": "CGCAGACG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "AAGTCCAA",
      "index2": "TATGAGTA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "ATCCACTG",
      "index2": "AGGTGCGT",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "GCTTGTCA",
      "index2": "GAACATAC",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "CAAGCTAG",
      "index2": "ACATAGCG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep1",
      "index": "TGGATCGA",
      "index2": "GTGCGATA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "AGTTCAGG",
      "index2": "CCAACAGA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "GACCTGAA",
      "index2": "TTGGTGAG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "TCTCTACT",
      "index2": "CGCGGTTC",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "CTCTCGTC",
      "index2": "TATAACCT",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "CCAAGTCT",
      "index2": "AAGGATGA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "TTGGACTC",
      "index2": "GGAAGCAG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "GGCTTAAG",
      "index2": "TCGTGACC",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep2",
      "index": "AATCCGGA",
      "index2": "CTACAGTT",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "TAATACAG",
      "index2": "ATATTCAC",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "CGGCGTGA",
      "index2": "GCGCCTGT",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "ATGTAAGT",
      "index2": "ACTCTATG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "GCACGGAC",
      "index2": "GTCTCGCA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "GGTACCTT",
      "index2": "AAGACGTC",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "AACGTTCC",
      "index2": "GGAGTACT",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "GCAGAATT",
      "index2": "ACCGGCCA",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    },
    {
      "lane": 1,
      "sample_id": "TSPF-NA12878-10B-Rep3",
      "index": "ATGAGGCC",
      "index2": "GTTAATTG",
      "library_prep_kit_name": "TruSeqDNAPCRFree",
      "index_adapter_kit_name": "TruSeqDnaUDIndexes96Indexes"
    }
  ],
  "cloud_settings": {
    "generated_version": "0.0.0",
    "cloud_workflow": "ica_workflow_1"
  }
}

Yields

Output

Click to expand! 
[Header]
FileFormatVersion,2
RunName,TruSeq-PCRfree-NA12878-10B
InstrumentType,NovaSeqXPlus
IndexOrientation,Forward

[Reads]
Read1Cycles,151
Read2Cycles,151
Index1Cycles,10
Index2Cycles,10

[BCLConvert_Settings]
AdapterRead1,AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
AdapterRead2,AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
OverrideCycles,Y151;I8N2;N2I8;Y151
FastqCompressionFormat,gzip
SoftwareVersion,4.1.27

[BCLConvert_Data]
Lane,Sample_ID,index,index2
1,TSPF-NA12878-10B-Rep1,CCGCGGTT,AGCGCTAG
1,TSPF-NA12878-10B-Rep1,TTATAACC,GATATCGA
1,TSPF-NA12878-10B-Rep1,GGACTTGG,CGCAGACG
1,TSPF-NA12878-10B-Rep1,AAGTCCAA,TATGAGTA
1,TSPF-NA12878-10B-Rep1,ATCCACTG,AGGTGCGT
1,TSPF-NA12878-10B-Rep1,GCTTGTCA,GAACATAC
1,TSPF-NA12878-10B-Rep1,CAAGCTAG,ACATAGCG
1,TSPF-NA12878-10B-Rep1,TGGATCGA,GTGCGATA
1,TSPF-NA12878-10B-Rep2,AGTTCAGG,CCAACAGA
1,TSPF-NA12878-10B-Rep2,GACCTGAA,TTGGTGAG
1,TSPF-NA12878-10B-Rep2,TCTCTACT,CGCGGTTC
1,TSPF-NA12878-10B-Rep2,CTCTCGTC,TATAACCT
1,TSPF-NA12878-10B-Rep2,CCAAGTCT,AAGGATGA
1,TSPF-NA12878-10B-Rep2,TTGGACTC,GGAAGCAG
1,TSPF-NA12878-10B-Rep2,GGCTTAAG,TCGTGACC
1,TSPF-NA12878-10B-Rep2,AATCCGGA,CTACAGTT
1,TSPF-NA12878-10B-Rep3,TAATACAG,ATATTCAC
1,TSPF-NA12878-10B-Rep3,CGGCGTGA,GCGCCTGT
1,TSPF-NA12878-10B-Rep3,ATGTAAGT,ACTCTATG
1,TSPF-NA12878-10B-Rep3,GCACGGAC,GTCTCGCA
1,TSPF-NA12878-10B-Rep3,GGTACCTT,AAGACGTC
1,TSPF-NA12878-10B-Rep3,AACGTTCC,GGAGTACT
1,TSPF-NA12878-10B-Rep3,GCAGAATT,ACCGGCCA
1,TSPF-NA12878-10B-Rep3,ATGAGGCC,GTTAATTG

[Cloud_Settings]
GeneratedVersion,0.0.0
Cloud_Workflow,ica_workflow_1
BCLConvert_Pipeline,urn:ilmn:ica:pipeline:bf93b5cf-cb27-4dfa-846e-acd6eb081aca#BclConvert_v4_2_7

[Cloud_Data]
Sample_ID,LibraryName,LibraryPrepKitName,IndexAdapterKitName
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_AAGTCCAA_TATGAGTA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_ATCCACTG_AGGTGCGT,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_CAAGCTAG_ACATAGCG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_CCGCGGTT_AGCGCTAG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_GCTTGTCA_GAACATAC,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_GGACTTGG_CGCAGACG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_TGGATCGA_GTGCGATA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep1,TSPF-NA12878-10B-Rep1_TTATAACC_GATATCGA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_AATCCGGA_CTACAGTT,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_AGTTCAGG_CCAACAGA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_CCAAGTCT_AAGGATGA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_CTCTCGTC_TATAACCT,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_GACCTGAA_TTGGTGAG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_GGCTTAAG_TCGTGACC,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_TCTCTACT_CGCGGTTC,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep2,TSPF-NA12878-10B-Rep2_TTGGACTC_GGAAGCAG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_AACGTTCC_GGAGTACT,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_ATGAGGCC_GTTAATTG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_ATGTAAGT_ACTCTATG,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_CGGCGTGA_GCGCCTGT,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_GCACGGAC_GTCTCGCA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_GCAGAATT_ACCGGCCA,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_GGTACCTT_AAGACGTC,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes
TSPF-NA12878-10B-Rep3,TSPF-NA12878-10B-Rep3_TAATACAG_ATATTCAC,TruSeqDNAPCRFree,TruSeqDnaUDIndexes96Indexes

Testing your outputs

The v2-samplesheet-maker will validate that your parameters are the correct type, the correct name AND within the appropriate range. It will NOT however determine if your settings are appropriate or compatible with a BCLConvert run, i.e it will NOT ensure that your indexes have an appropriate hamming distance.

If you wish to validate your output samplesheet with BCLConvert, you can use the following script -

scripts/build-samplesheet-and-validate-with-bcl-convert.sh <input-json> [docker-image]. This will convert the input json to csv and then run the docker container bclconvert against your samplesheet.

You will need both jq and docker installed for this to work.

Additional Usage

Parsing a SampleSheet CSV to JSON

We also have created a parser between the csv and json, with the following usage syntax

v2-samplesheet-to-json SampleSheet.csv -

Input SampleSheet CSV

Click to expand! 
[Header]
FileFormatVersion,2
RunName,my-illumina-sequencing-run
RunDescription,A test run
InstrumentPlatform,NovaSeq 6000
InstrumentType,NovaSeq

[Reads]
Read1Cycles,151
Read2Cycles,151
Index1Cycles,10
Index2Cycles,10

[BCLConvert_Settings]
AdapterBehavior,trim
BarcodeMismatchesIndex1,1
BarcodeMismatchesIndex2,1
MinimumAdapterOverlap,2
OverrideCycles,Y151;Y10;Y8N2;Y151
CreateFastqForIndexReads,False
NoLaneSplitting,False
FastqCompressionFormat,gzip

[BCLConvert_Data]
Lane,Sample_ID,index,index2,Sample_Project
1,MyFirstSample,AAAAAAAAAA,CCCCCCCC,SampleProject
1,MySecondSample,GGGGGGGGGG,TTTTTTTT,SampleProject

Gives us

Click to expand! 
{
  "header": {
    "file_format_version": 2,
    "run_name": "my-illumina-sequencing-run",
    "run_description": "A test run",
    "instrument_platform": "NovaSeq 6000",
    "instrument_type": "NovaSeq"
  },
  "reads": {
    "read_1_cycles": 151,
    "read_2_cycles": 151,
    "index_1_cycles": 10,
    "index_2_cycles": 10
  },
  "bclconvert_settings": {
    "adapter_behavior": "trim",
    "barcode_mismatches_index_1": 1,
    "barcode_mismatches_index_2": 1,
    "minimum_adapter_overlap": 2,
    "override_cycles": "Y151;Y10;Y8N2;Y151",
    "create_fastq_for_index_reads": false,
    "no_lane_splitting": false,
    "fastq_compression_format": "gzip"
  },
  "bclconvert_data": [
    {
      "lane": 1,
      "sample_id": "MyFirstSample",
      "index": "AAAAAAAAAA",
      "index2": "CCCCCCCC",
      "sample_project": "SampleProject"
    },
    {
      "lane": 1,
      "sample_id": "MySecondSample",
      "index": "GGGGGGGGGG",
      "index2": "TTTTTTTT",
      "sample_project": "SampleProject"
    }
  ]
}

Manipulating a samplesheet by parsing through JSON

For this component, you will need some basic knowledge of the jq command line tool.

In this example we are given a standard samplesheet but want to edit the adapters for one of the samples.

Whilst we could do this with grep, sed or awk, it's a little hacky, instead we can

  1. Parse the samplesheet to json to stdout
  2. Use jq to update the samplesheet
  3. Write out the updated samplesheet to csv
# Initialise new index variable
NEW_I7_INDEX="TTTTTTTTTT"
SAMPLE_ID="MySecondSample"

# Convert the SampleSheet to JSON
# Update the index for the sample "MySecondSample" in the bclconvert_data section
# Write out the new samplesheet to csv
v2-samplesheet-to-json SampleSheet.csv - | \
jq --raw-output \
  --arg sample_id "${SAMPLE_ID}" \
  --arg replacement_index "${NEW_I7_INDEX}" \
  '
    .bclconvert_data=( 
      .bclconvert_data |
      map(
        if .sample_id == $sample_id then
          .index = $replacement_index
        else
          .
        end
      )
    )
  ' | \
v2-samplesheet-maker - SampleSheet.updated_index.csv

Gives us the following samplesheet as an output

[Header]
FileFormatVersion,2
RunName,my-illumina-sequencing-run
RunDescription,A test run
InstrumentPlatform,NovaSeq 6000
InstrumentType,NovaSeq

[Reads]
Read1Cycles,151
Read2Cycles,151
Index1Cycles,10
Index2Cycles,10

[BCLConvert_Settings]
AdapterBehavior,trim
BarcodeMismatchesIndex1,1
BarcodeMismatchesIndex2,1
MinimumAdapterOverlap,2
OverrideCycles,Y151;Y10;Y8N2;Y151
CreateFastqForIndexReads,False
NoLaneSplitting,False
FastqCompressionFormat,gzip

[BCLConvert_Data]
Lane,Sample_ID,index,index2,Sample_Project
1,MyFirstSample,AAAAAAAAAA,CCCCCCCC,SampleProject
1,MySecondSample,TTTTTTTTTT,TTTTTTTT,SampleProject

If we run a diff on our original and updated samplesheet, we can see that the index for "MySecondSample" has been updated.

diff SampleSheet.csv SampleSheet.updated_index.csv

Yields

27c27
< 1,MySecondSample,GGGGGGGGGG,TTTTTTTT,SampleProject
---
> 1,MySecondSample,TTTTTTTTTT,TTTTTTTT,SampleProject

Contributing

Is there a missing section you'd like to see?

You can generate your own sections by

  1. Forking this repository.
  2. Adding the section pydantic model to the section models module
  3. Extending the SampleSheel pydantic model in the samplesheet models module
  4. Generating a class for your section in the section class module
  5. Extending the SampleSheet class to write out your section in the samplesheet class module
  6. Add a test for your section in the test_sections module
  7. Generating a PR back to this repository! ( Please squash your commits :) )

Release Cycles

We try and update this repository for every new Dragen Release (which coincides with a BCLConvert release) and tag accordingly.

Project links

Data verified by PyPI on Nov 10, 2024
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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  • Released:
Latest release

1 maintainer

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Avatar for alexiswl from gravatar.com alexiswl

Credits

Author: Alexis Lucattini

GitHub Statistics

Data verified by PyPI on Nov 10, 2024
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.10

Provides Extra

build deploy test toml

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Metadata

Project links

Data verified by PyPI on Nov 10, 2024
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for alexiswl from gravatar.com alexiswl

Credits

Author: Alexis Lucattini

GitHub Statistics

Data verified by PyPI on Nov 10, 2024
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.10

Provides Extra

build deploy test toml

Classifiers

Release files for v2-samplesheet-maker 4.2.4.post20241110133537

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Table of built distributions (wheels) for v2-samplesheet-maker 4.2.4.post20241110133537
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Total release size: 81.1 kB

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Size 35.0 kB
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Download URL v2_samplesheet_maker-4.2.4.post20241110133537-py3-none-any.whl
Size 46.1 kB
Tags Python 3
SHA-256 checksum
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